WO2025233473A1

WO2025233473A1 - Primary charged particle beam aberration for multi-beam scanning charged particle microscope based on measurements on secondary charged particle beams

Info

Publication number: WO2025233473A1
Application number: PCT/EP2025/062655
Authority: WO
Inventors: Thomas Dieterle; Benedikt TRATZMILLER; Ingo Mueller; Felix MENKE; Thomas Schmid; Dirk Zeidler; Bjoern MIKSCH; Sandra Vogel; Stefan Schubert; Sepideh Samadzadegan
Original assignee: Carl Zeiss Multisem GmbH
Current assignee: Carl Zeiss Multisem GmbH
Priority date: 2024-05-10
Filing date: 2025-05-08
Publication date: 2025-11-13
Anticipated expiration: 2026-11-10
Also published as: TW202548823A

Abstract

Techniques are disclosed to determine at least one aberration of the multiple primary charged particle beams (3.1, 3.2, 3.3) of a multi-beam scanning charged particle microscope, e.g., a deflection or a spot distortion of the multiple primary charged particle beams. The at least one aberration is determined based on an estimation of a charge buildup (1050) of the sample (7), i.e., a local surface charge of a sample surface. A prediction of the charge buildup may be determined for a future point in time.

Description

D E S C R I P T I O N

PRIMARY CHARGED PARTICLE BEAM ABERRATION FOR MULTI-BEAM SCANNING CHARGED PARTICLE MICROSCOPE BASED ON MEASUREMENTS ON SECONDARY CHARGED PARTICLE BEAMS

TECHNICAL FIELD

Various examples of the disclosure generally pertain to techniques of mitigating effects of a charge buildup on a sample illuminated by charged particle beams in a multi-beam scanning charged particle microscope. Various examples of the disclosure specifically pertain to determining an estimate of an aberration of multiple primary charged particle beams based on the charge buildup and then compensating for such aberration.

BACKGROUND

In scanning electron microscopes (SEMs) or other types scanning charged-particle microscopes (e.g., scanning helium ion microscopes), a charge buildup of the sample can lead, amongst other effects, to a so-called “drift” - a gradual shift of the field of view (FOV) relative to the imaging object/sample. The electrostatic extraction field above the sample surface is locally distorted.

In general, a charge buildup occurs primarily when the primary electron beam, after passing through the beam generation apparatus (the “illumination side” of the SEM, as opposed to the “detection side” of the SEM) interacts with an insulating or poorly conducting region of the sample. As the primary electrons impinge on the sample, secondary electrons are emitted by the sample, leading to a net accumulation of, e.g., positive charge in the irradiated area. If the sample is non-conductive or has a low conductivity, this charge does not dissipate or dissipates slowly. The resulting charge buildup or accumulation distorts the electric field in that region. This degrades image quality by introducing artifacts such as brightness variations and image distortion. Prior art mitigation techniques include coating the sample with a conductive layer or using a low vacuum in the SEM chamber. These prior art techniques face certain restrictions. For instance, coating the sample with a conductive layer affects the sample integrity, i.e., corresponds to invasive imaging. This is not always an option, e.g., for in line testing of semiconductor structures. Using a low vacuum environment reduces the image quality.

Recently, multi-beam SEMs (MSEMs) have been employed to provide large-scale composite images of samples. Here, multiple images are acquired contemporaneously for multiple FOVs - each defined by a pair of primary and secondary electron beams that are each arranged in a pattern -, to thereby define a composite FOV. The respective images thereby obtained are stitched to form a composite image.

It has been observed that the effect of a charge buildup leads to even significant distortions in the composite image. For instance, different FOVs may be differently affected by the charge buildup, thereby leading to an inter-FOV shift. The composite image thus shows artifacts.

SUMMARY

Accordingly, a need exists for reducing the impact of charge buildup of a sample imaged using a multi-beam scanning charged-particle microscope such as a MSEM.

This need is met by the features of the independent claims. The features of the dependent claims defined embodiments.

Hereinafter, techniques of determining an estimate of a charge buildup of a sample that is illuminated by multiple charged particle beams of a multi-beam scanning charged-particle microscope such as an MSEM.

According to the disclosure, one or more measurements at the detector side of the multi-beam scanning charged particle microscope may be executed and based on such detector-side measurements, the estimate of the charge buildup is inferred I determined. For instance, by considering a variation of one or more characteristics of spots associated with secondary charged particle beams across an image of the respective pattern of the secondary charged particle beams, it would be possible to determine a spatially-dependent estimate of the charge buildup, i.e. , it is possible to estimate the charge buildup for multiple positions on the sample. In other scenarios, it may suffice to determine a “global” estimate of the charge buildup, e.g., estimate an average of the charge buildup across the sample.

According to examples, once the estimate of the charge buildup is determined, the sample may be discharged. Such discharging is also referred to as charge compensation. For instance, for a positive charge buildup, a mirror-mode discharging can be executed; the time duration of such mirror-mode discharging can be estimated to optimize throughput. Depending on the amount of surface charge, i.e., the amount of charge buildup, the illumination time with primary electrons with "0 eV" landing energy (so-called mirror mode) may vary. This discharging time is preferably set as short as possible, to avoid a significant decrease of the sample throughput. Such mirror-mode illumination time can be determined based on the amount of charge buildup.

Alternatively or additionally to such discharging, it would be possible to determine an aberration of the primary charged particle beams based on the estimate of the charge buildup. The charge buildup creates an electric field; the charged particles, e.g., electrons, are diverted by this electric field, leading to an aberration of the primary charged particle beams.

This can be different types of aberrations. For instance, an aberration can be beamspecific. This means that a given one of the multiple primary charged particle beams exhibits a particular beam-specific aberration; another one of the multiple primary charged particle beams exhibits another beam-specific aberration. Another type of aberration is an aberration associated with a pattern formed by the multiple primary charged particle beams. A size/magnification of the pattern can be changed. The pattern can be rotated. The pattern can be skewed.

Next, a few examples of beam-specific aberrations are provided. For instance, a deflection of each of the multiple primary charged particle beams can be estimated. A deflection of the multiple primary charged particle beams corresponds to one or more of the multiple primary charged particle beams being diverted from their target spot position in a sample plane. Another example of a beam-specific aberration is a spot distortion of the spot shape of a given one of the multiple primary charged particle beams. The spot shape of each of the multiple primary charged particle beams may be affected. For instance, the ideal spot shape may be circular. However, the spot shape may be distorted, e.g., to an ellipse.

Yet another example of a beam-specific aberration is defocus. A focal position defines the position along each of the multiple primary charged particle beams at which the spot size (i.e. , cross section through the beam perpendicular to the propagation direction of the charged particles) takes a minimum value. Sometimes, this focal position can be shifted against a nominal position, e.g., shifted against the sample plane, leading to a defocus.

As will be appreciated above, in some scenarios, an estimate of a beam-specific aberration may be determined for each of the multiple primary charged particle beams. This is based on the finding that the strength of the beam-specific aberration - e.g., defocus or deflection - may vary depending on the position of each of the primary charged particle beams within the respective pattern of primary charged particle beams or, in other words, in a spatial relation to the charge buildup on the sample.

Based on the knowledge of the at least one aberration of the multiple primary charged particle beams, it may then be possible to take counter-measures. For instance, a physical compensation of the at least one aberration may be implemented. This may include applying a magnetic and/or electric field to the pattern of the primary charged particle beams; thereby, a common baseline of the at least one aberration of the primary charged particle beams may be compensated. The electric or magnetic field may also exhibit a gradient across the pattern of the primary charged particle beams. Thereby, e.g., a stronger deflection or defocus at one side of the pattern may be compensated. Sometimes, multi-beam deflection units capable of deflecting individual ones of the multiple primary charged particle beams are available, e.g., in a multi-aperture plate next to a source of the charged particles at the illumination side of the multi-beam scanning charged particle microscope; then, a per-beam compensation of any beam-specific aberration estimated for each of the multiple primary charged particle beams may be executed by controlling the multibeam deflection unit. In some scenarios, alternatively or additionally to such physical compensation, the compensation may be applied retrospectively, i.e. , after completing image acquisition for images of the sample. Here, a transformation can be applied to the images of the sample. This transformation can be determined based on the at least one aberration estimated for the multiple primary charged particle beams.

A method of operating a multi-beam scanning charged particle microscope is disclosed. The method may include loading a sample into an imaging position of the multi-beam scanning charged particle microscope. The method also includes illuminating the sample with multiple primary charged particle beams, e.g., when the sample is in the imaging position. The method may also include measuring at least one characteristic of secondary charged particle beams. The secondary charged particle beams are associated with the multiple primary charged particle beams. The method also includes determining an estimate of a charge buildup of the sample based on the at least one characteristic of the secondary charged particle beams. The method further includes determining an estimate of at least one aberration of the multiple primary charged particle beams. The at least one aberration results from the charge buildup of the sample. The method further includes performing a compensation of the at least one aberration based on the estimate of the at least one aberration of the multiple primary charged particle beams.

A program code for execution by at least one processor includes control instructions for operating a multi-beam scanning charged particle microscope. When the program code is loaded and executed by at least one processor, the at least one processor performs a method. The method may include loading a sample into an imaging position of the multi-beam scanning charged particle microscope. The method also includes illuminating the sample with multiple primary charged particle beams, e.g., when the sample is in the imaging position. The method may also include measuring at least one characteristic of secondary charged particle beams. The secondary charged particle beams are associated with the multiple primary charged particle beams. The method also includes determining an estimate of a charge buildup of the sample based on the at least one characteristic of the secondary charged particle beams. The method further includes determining an estimate of at least one aberration of the multiple primary charged particle beams. The at least one aberration results from the charge buildup of the sample. The method further includes performing a compensation of the at least one aberration based on the estimate of the at least one aberration of the multiple primary charged particle beams.

A circuitry for controlling operation of a multi-beam scanning charge particle microscope is configured to control the multi-beam scanning charged particle microscope to load a sample into an imaging position of the multi-beam scanning charged particle microscope and the control of the multi-beam scanning charged particle microscope to illuminate the sample when the sample is in the imaging position. Said illuminating is with multiple primary charged particle beams. The circuitry is further configured to measure at least one characteristic of secondary charged particle beams associated with the multiple primary charged particle beams and to determine an estimate of a charge buildup of the sample based on the at least one characteristic of the secondary charged particle beams. The circuitry is further configured to determine an estimate of at least one aberration of the multiple primary charged particle beams resulting from the charge buildup of the sample and to perform a compensation of the at least one aberration based on the estimate of the at least one aberration.

It is to be understood that the features mentioned above and those yet to be explained below may be used not only in the respective combinations indicated, but also in other combinations or in isolation without departing from the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an MSEM according to various examples.

FIG. 2 schematically illustrates a multi-aperture plate of the MSEM, the multi-aperture plate defining a pattern of primary electron beams according to various examples.

FIG. 3 schematically illustrates focal positions of the primary electron beams on a sample surface according to various examples.

FIG. 4 schematically illustrates a pattern of secondary electron beams in an imaging plane according to various examples.

FIG. 5 schematically illustrates a deflection of primary and secondary electron beams due to a charge buildup of a sample according to various examples. FIG. 6 schematically illustrates a distorted image depicting a field of view defined by scanning a given primary electron beam across the sample.

FIG. 7 schematically illustrates an undistorted image depicting the field of view that is also depicted in FIG. 6.

FIG. 8 schematically illustrates a detector system of the MSEM according to various examples.

FIG. 9 schematically illustrates a pattern of light detection fibers according to various examples.

FIG. 10A is a flowchart of a method according to various examples.

FIG. 10B and FIG. 10C schematically illustrate time-dependencies of multiple characteristics of the secondary electron beams according to various examples.

FIG. 10D and FIG. 10E illustrates aspects with respect to predicting a characteristic of the secondary electron beams according to various examples.

FIG. 11 schematically illustrates a model for determining an estimate of a deflection of a primary electron beam in the presence of a charge buildup on the sample.

FIG. 12 illustrates calculated estimates of the deflection of the primary electron beam as obtained from the model of FIG. 11.

DETAILED DESCRIPTION

Some examples of the present disclosure generally provide for a plurality of circuits or other electrical devices. All references to the circuits and other electrical devices and the functionality provided by each are not intended to be limited to encompassing only what is illustrated and described herein. While particular labels may be assigned to the various circuits or other electrical devices disclosed, such labels are not intended to limit the scope of operation for the circuits and the other electrical devices. Such circuits and other electrical devices may be combined with each other and/or separated in any manner based on the particular type of electrical implementation that is desired. It is recognized that any circuit or other electrical device disclosed herein may include any number of microcontrollers, a graphics processor unit (GPU), integrated circuits, memory devices (e.g., FLASH, random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or other suitable variants thereof), and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electrical devices may be configured to execute a program code that is embodied in a non-transitory computer readable medium programmed to perform any number of the functions as disclosed.

In the following, embodiments of the invention will be described in detail with reference to the accompanying drawings. It is to be understood that the following description of embodiments is not to be taken in a limiting sense. The scope of the invention is not intended to be limited by the embodiments described hereinafter or by the drawings, which are taken to be illustrative only.

The drawings are to be regarded as being schematic representations and elements illustrated in the drawings are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose become apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software, or a combination thereof.

Various techniques disclosed herein are related to imaging a sample using a multibeam charged particle scanning microscope. Hereinafter, techniques of operating a multi-beam scanning charged particle microscope will be disclosed. Specifically, techniques will be explained for MSEMs, but other types of multi-beam scanning charged particle microscope may be employed as well.

The MSEM jointly scans multiple primary electron beams across a sample. The associated secondary electron beams are detected. Multiple images are formed, one for each pair of primary-secondary electron beam pairs. Each image has an associated FOV. A composite image is then determined based on a stitching operating. The composite FOV corresponds to the aggregation of the FOVs. The composite FOV of the composite image captures the entire illuminated area of the sample. Illumination of the sample using the electron beams may cause a charge buildup.

According to various examples, the charge buildup of the sample is estimated. For instance, the charge buildup in an illuminated area on the sample across which the multiple primary charged particle beams are scanned may be estimated. Based on a respective estimate of the charge buildup of the sample, it is then possible to determine an estimate of at least one aberration of the multiple primary charged particle beams that results from the charge buildup of the sample. In other words: the effect of the charge buildup of the sample onto the primary charged particle beams is taken into account - not only the aberration of the secondary charged particle beams.

It would be possible to determine an estimate of at least one beam-specific aberration of each of the multiple primary charged particle beam; alternatively or additionally, it would be possible to determine an estimate of a pattern-specific aberration associated with a pattern formed by the multiple primary charged particle beams, e.g., in a reference plane such as a sample plane.

In other words, at least one aberration may be estimated for each of the multiple primary charged particle beams. For instance, those charged particle beams located closer to an edge of the pattern of the multiple primary charged particle beams may be located closer to an edge of the illuminated area that is exhibiting the charge buildup (the charge buildup may be estimated to zero outside of the illuminated area). Thus, due to edge effects they may be distorted or deflected differently if compared to a primary charged particle beam located closer to or at a center of that pattern.

Beyond such edge effects, there may be a further reason for different values of a given aberration observed for different ones of the primary charged particle beams: In some scenarios, the estimate of the charge buildup may be dependent on a spatial position on the sample I across the pattern of the primary and secondary electron beams. Different areas of the sample may show a different severity of the charge buildup. Reasons can lie in the structure of the sample. For instance, semiconductor samples may use local metallization and local isolation, leading to different strengths of the charge buildup. Based on the estimate of the at least one aberration, it is then possible to perform a compensation of the at least one aberration. As a general rule, such compensation of the at least one aberration can be a physical compensation, e.g., during the imaging of the sample. One or more beam control elements of the multi-beam charged particle microscope can be controlled to deflect the multiple primary charged particle beams, thereby counteracting the at least one aberration of the multiple primary charged particle beams.

Alternatively or additionally to such a physical compensation, also a digital compensation can be employed. Performing the compensation may include digitally postprocessing an image of the sample that is acquired using the multi-beam scanning charged particle microscope. Based on the estimate of the at least one aberration of the multiple primary charged particle beams, the dislocation of each pixel or set of pixels can be estimated in the acquired images. Then, this dislocation of each pixel or set of pixels can be corrected. The images can be undistorted.

As a general rule, the level of detail with which the estimate of the charge buildup is determined may vary according to the various examples of the disclosure. For instance, in some examples, the estimate of the charge buildup may be determined without any spatial dependency as well as without any time dependency. I.e. , it may be assumed that the charge buildup remains constant within a certain area on the sample, e.g., within the illuminated area onto which the multiple primary charged particle beams impinge on the sample. On the other hand, it would be possible in other scenarios to determine the estimate of the charge buildup as a function of the position on the sample and/or as a function of time. For instance, to determine the estimate of the charge buildup as a function of the position, e.g., spatially dependent within the illumination area within which the multiple charged particle beams are scanned across the sample, it would be possible to take into account individual properties of each of multiple secondary charged particle beams. For instance, a spot size or spot offset or spot shape or spot brightness of individual beam spots associated with individual secondary charged particle beams may be considered to determine the estimate of a local charge buildup. For obtaining a time dependency, it would be possible to consider a time evolution of one or more characteristics of the secondary charged particle beams. For instance, the magnification of a pattern of the multiple secondary charged particle beams may change as a function of time, e.g., until reaching a steady state. This time dependency may be indicative of a timedependency of the charge buildup that gradually increases until leveling off.

The level of detail with which the estimate of the at least one aberration is determined may correspond to the level of detail with which the estimate of the charge buildup is determined. In other words, if the charge buildup is estimated with time resolution, then, similarly, the at least one aberration can be estimated with time resolution.

FIG. 1 is a schematic illustration of an MSEM 1. Further information relating to such MSEMs and components used therein, such as, for instance, particle sources, multiaperture plate and lenses, can be obtained from the international patent applications WO 2005/024881 , WO 2007/028595, WO 2007/028596, WO 2011/124352 and WO 2007/060017 and the German patent applications having the publication numbers DE 10 2013 016 113 A1 and DE 10 2013 014 976 A1 , the disclosure of which in the full scope thereof is incorporated by reference in the present application.

The MSEM 1 uses a plurality of charged particle electron beams (also referred to as beamlet or simply beam) for imaging a sample 7. The MSEM 1 generates a plurality of J primary beams 3.1 , 3.2, 3.3 which strike the sample 7 to generate interaction products, e.g., secondary electrons, which emanate from the sample 7, form secondary beams 9.1 , 9.2, 9.3, and are subsequently detected.

Each one of the primary and secondary beams 3.1 , 3.2, 3.3, 9.1 , 9.2, 9.3 is formed and guided by a respective imaging subsystem of the MSEM 1. Each imaging subsystem is associated with a respective FOV. Images acquired by a respective imaging subsystem depict the respective FOV. The multiple FOVs are arranged in a spatial pattern to thereby define a composite FOV.

The primary beams 3.1 , 3.2, 3.3 are formed by electrons which are incident on a surface of the sample 7 at a plurality of locations and generate a plurality of primary electron beam focus spots 5,1 5.2, 5.3 that are spatially separated from one another.

The sample 7 to be examined can be of any desired type, e.g., a semiconductor wafer or a semiconductor mask, and can comprise an arrangement of miniaturized elements.

The surface of the sample 7 is arranged in a sample plane 101 of an objective lens system 102 of a first particle optical unit 100 (also referred to as illumination system). A diameter of the minimal beam spots or focus spots 5,1 5.2, 5.3 shaped in the sample plane 101 can be small. Exemplary values of this diameter are below four nanometers, for example three nm or less. The focusing of the primary beams 3.1 , 3.2, 3.3 for shaping the focus spots 5,1 5.2, 5.3 is carried out by the objective lens system 102. In this case, the objective lens system 102 can comprise a magnetic immersion lens. Further examples of focusing means are described in the German patent DE 102020125534 B3, the entire content of which is herewith incorporated in the disclosure.

The number J of primary beams 3.1 , 3.2 and 3.3 (i.e., the number of FOVs) may be five, 25, 90 to 100, or more (for sake of simplicity, only three primary beams 3.1 , 3.2 and 3.3 with corresponding focus points 5.1 , 5.2 and 5.3 are shown in FIG 1 ).

In practice, the number of beams J, and hence the number of FOVs, can be chosen to be significantly greater, such as, for example, J = 10 x 10, J = 20 x 30 or J = 100 x 100. Exemplary values of the pitch between the incidence locations and FOVs are 1 micrometer, 10 micrometers, or more, for example 40 micrometers.

The number of primary and secondary beams J defines the number of FOVs. Each imaging subsystem has a respective FOV. The respective FOV is defined by scanning the respective pair of primary and secondary beams (e.g., beams 3.1 and 9.1 ) over the sample 7 in the respective FOV.

The primary beams 3.1 , 3.2, 3.3 striking the sample 7 generate interaction products, e.g., secondary electrons, back-scattered electrons, which emanate from the surface of the sample 7, or primary particles that have experienced a reversal of movement for other reasons. A charge buildup may occur, depending on the charge dissipation properties of the sample 7. The interaction products emanating from the surface of the sample 7 are shaped by the objective lens system 102 to form the secondary beams 9.1 , 9.2, 9.3. Secondary electrons included in the secondary beams 9.1 , 9.2, 9.3 are used for imaging.

The MSEM 1 , at a detection side, provides a detection beam path for guiding the plurality of secondary beams 9.1 , 9.2, 9.3 to a secondary electron imaging system 200. The secondary electron imaging system 200 includes several electron-optical lenses 205.1 to 205.5 for directing the secondary beams 9.1 , 9.2, 9.3 towards a spatially resolving detector system 600. The imaging with the secondary electron imaging system 200 is strongly magnifying such that both the pattern of the primary beams on the wafer surface and the size and shape of focal points of the primary beams are imaged in much magnified fashion. By way of example, a scale factor I magnification is between 100x and 300x such that one nm on the wafer surface is imaged enlarged to between 100 nm and 300 nm. In an example, an image field of a multi-beam device with for example 100 pm diameter is enlarged to approximately 30 mm.

The primary beams 3.1 , 3.2, 3.3 are generated, at the illumination side, in a beam generation apparatus 300 comprising at least one particle source 301 (e.g., an electron source), at least one collimation lens 303, a multi-aperture arrangement 305 and a first field lens 331 and a second field lens 333. The particle source 301 generates at least one diverging particle beam 309, which is at least substantially collimated by the at least one collimation lens 303, and which illuminates the multiaperture arrangement 305. The multi-aperture arrangement 305 includes a multiaperture plate (MAP) 304 (also referred to as filter plate or multi-hole aperture plate), which has a plurality of J openings formed therein in a first raster arrangement. Particles of the illuminating particle beam 309 pass through the J apertures or openings of the MAP 304 and form the plurality J of primary beams 3.1 , 3.2, 3.3. Particles of the illuminating particle beam 309 which strike the first aperture plate 304 are absorbed by the latter and do not contribute to the formation of the primary beams 3.1 , 3.2, 3.3. A multi-aperture arrangement 305 sometimes has at least a further MAP 306 that may include beam deflection means, for example a lens array, a stigmator array, or an array of deflection elements. Such beam deflection means may individually deflect each of the multiple primary beams 3.1 , 3.2, 3.3.

Together with the field lens 331 and a second field lens 333, the multi-aperture arrangement 305 focuses each of the primary beams 3.1 , 3.2, 3.3 in such a way that focal points are formed in an intermediate image surface 321 . Alternatively, the beam foci and the intermediate image surface 321 can be virtual. The intermediate image surface 321 can be curved to pre-compensate a field curvature of the imaging system arranged downstream of the intermediate image surface 321.

The at least one field lens 103 and the objective lens system 102 provide a first imaging particle optical unit for imaging the surface 321 , in which the beam foci are formed, onto the sample plane 101 such that a second pattern of focus spots 5,1 5.2, 5.3 of the primary beams 3.1 , 3.2, 3.3 is formed there. Typically, the surface 25 of the sample 7 is arranged in the sample plane 101 , and the focal spots 5,1 5.2, 5.3 are correspondingly formed on the object surface 25. The plurality of primary beams 3.1 ,

3.2, 3.3 form a crossover point 108, in the vicinity of which a first deflection scanner 110 is arranged. The first deflection scanner 110 is used to deflect the plurality of primary beams 3.1 , 3.2, 3.3 collectively and synchronously such that the plurality of focus spots 5,1 5.2, 5.3 are scanned jointly and contemporaneously over the surface 25 of the sample 7. Raster scanning is implemented, thereby imaging the sample 7. The first deflection scanner 110 is driven by a scanning control unit 860 such that in an inspection mode of operation, a plurality of two-dimensional image data of the surface is acquired. Additionally, the MSEM 1 can include further static deflectors configured to adjust the position of the plurality of the primary beams 3.1 , 3.2, 3.3.

The objective lens system 102 and the projection lenses 205 provide a secondary electron imaging system 200 for imaging the sample plane 101 onto an imaging plane 225. The objective lens system 102 is thus a lens or a lens system that is part of both the first and the second particle optical unit, while the field lenses 103, 331 and 333 belong only to the first particle optical unit 100, and the projection lenses 205 belongs only to the secondary electron imaging system 200.

A beam divider 400 is arranged in the beam path of the first particle optical unit 100 between the field lens 103 and the objective lens system 102. The beam divider 400 is also part of the second optical unit in the beam path between the objective lens system 102 and the projection lenses 205.

The first deflection scanner 110 is arranged in a primary electron beam path or in a joint electron beam path. In the example shown in FIG. 1 , the secondary beams 9.1 ,

9.2, 9.3 transmit during use the first deflection scanner 110 in opposite direction and the scanning movement of the secondary beams 9.1 , 9.2, 9.3 is partially compensated. The secondary electrons have typically a different kinetic energy compared to the primary electrons. Therefore, the scanning movement of the moving irradiation positions is only partially compensated. To fully compensate the scanning movement of the secondary beams 9.1 , 9.2, 9.3, the collective beam deflector 222 is arranged in the secondary electron beam path.

The secondary electron imaging system 200 includes the second, collective beam deflector 222 which is arranged in the vicinity of a crossover point of the secondary beams 9.1 , 9.2, 9.3. The second, collective beam deflector 222 is operated synchronously with the first deflection scanner 110 and compensates during use a beam deflection of the secondary beams 9.1 , 9.2, 9.3 such that spots 15 of the beams 9 remain at constant position on the imaging plane 225. Thereby, each secondary beam 9 is kept within the area of a set of detection elements, which is assigned to the individual secondary beam 9.

The secondary electron imaging system 200 includes electron-optical lenses 205.1 to 205.5 to adjust a focus plane of the secondary beams 9.1 , 9.2, 9.3. A defocus can be applied. The electron-optical lenses 205.1 to 205.5 can thus implement corrective elements to correct the focus plane. The electron-optical lenses 205.1 to 205.5 are shown as magneto-optical elements but are not limited to magneto-optical elements and can comprise also electro-static lens elements or stigmators. With the electron- optical lenses 205.1 to 205.5, the secondary beams 9.1 , 9.2, 9.3 can be focused into the imaging plane 225 of the secondary electron imaging system 200.

The secondary electron imaging system 200 can include a plurality of further corrective elements, for example at least one of a multi-aperture array element, a deflector or an exchangeable aperture stop. Together with the objective lens system 102, the lenses serve to focus the secondary beams 9.1 , 9.2, 9.3 on the spatially resolving detector system 600 and, in the process, allow to correct or compensate the magnification and rotation of the pattern of the secondary beams 9.1 , 9.2, 9.3 in the imaging plane 225. Thereby, the pattern of the plurality of secondary beams 9.1 , 9.2, 9.3 can stabilized. For example, a first and second magnetic lenses 205.4 and 205.5 (as further examples of corrective elements) are designed in reversed order to one another and have oppositely directed magnetic fields. A Larmor rotation of the secondary beams 9.1 , 9.2, 9.3 can be compensated by suitably applying control signals to (driving) the magnetic lenses 205.4 and 205.5. The secondary electron imaging system 200 - in the illustrated example - includes further corrective elements, specifically a multi-aperture plate 216.

The MSEM 1 furthermore is associated with a processing device 800 configured both for controlling the individual particle optical components of the multiple particle beam system and for evaluating and analyzing the signals obtained by the detector system 600. The processing device 800 can be separated from the MSEM 1 or can be part of the MSEM 1 . For example, the processing device 800 can be configured to acquire pairs of test images and then evaluate the test images to determine values of one or more imaging parameters. In this case, the control or processing device 800 can be constructed from a plurality of individual electronic computers or electronic components. By way of example, the processing device 800 includes a control processor 880, a control module 840 for the control of the electro-optical elements of the secondary electron imaging system 200, and a control module 830 for the control of the electro-optical elements of the primary beam generation unit. The processing device 800 is further connected to a control module 503 for supplying a voltage to the sample 7, said voltage also being referred to as extraction voltage. Thereby, during use, an extraction field is generated between the objective lens system 102 and the surface 25 of the sample 7. During use, the extraction field decelerates the primary electrons of the primary beams 3.1 , 3.2, 3.3 before the object surface 25 is reached and generates an additional focusing effect on the plurality of primary beams 3.1 , 3.2, 3.3. At the same time, the extraction field serves during use to accelerate the secondary particles out of the surface 25 of the sample 7. Further, the processing device 800 includes the scanning control unit 860 for the raster scanning.

The detector system 600 includes a plurality of sets of detection elements with one set of detection elements for each secondary beam 9, for providing strongly magnified images for each FOV. During use, each set of detection elements is configured to record the intensity signal of the assigned secondary beam 9. The plurality of intensity signals for the plurality of secondary beams 9.1 , 9.2, 9.3 is transferred to the image data acquisition unit 810, where the image data is processed and stored in memory 890. Accordingly, multiple images are acquired, one for each imaging subsystem. These multiple images (or an aggregated image determined based on images of respective sequences) can be combined to a composite image having a composite FOV.

As explained above, the MSEM 1 includes means for generating multiple primary beams 3.1 , 3.2, 3.3 which are arranged in a first pattern. This first pattern is defined by the apertures of the aperture plate 304 (also referred to as multi-aperture plate). An example of the first pattern 41.1 is illustrated in FIG 2.

FIG. 2 shows the first aperture plate 304 with apertures 85 forming the first pattern

41.1. In this example, the first pattern 41.1 is a hexagonal raster with a raster pitch p1 of for example 100pm. The first pattern 41 .1 defines a reference for the pattern of the multiple primary beams 3.1 , 3.2, 3.3 in the sample plane.

FIG. 3 shows the origins of the secondary beams 9.1 , 9.2, 9.3, formed by the focus spots 5.1 , 5.2, 5.3 (cf. FIG. 1 ) of the primary beams 3.1 , 3.2, 3.3. At each irradiation position of a surface 25 of a sample 7 with a primary beam 3.1 , 3.2, 3.3, secondary electrons are generated which form the plurality of secondary beams 9.1 , 9.2, 9.3. The endpoints of the primary beams 3.1 , 3.2, 3.3 and the origins of the plurality of secondary beams 9.1 , 9.2, 9.3 therefore form a second pattern 41 .2 which is impacted by the first pattern 41.1 of the primary beams 3.1 , 3.2, 3.3. The second pattern 41 .2 may be affected by aberrations of the primary beams 3.1 , 3.2, 3.3.

FIG. 4 shows the spots 15 of the secondary beams 9.1 , 9.2, 9.3 in the imaging plane 225. During use, the spots 15 of the secondary beams 9.1 , 9.2, 9.3 form a third pattern 41.3. A third pitch 1061 of p3 = 1000pm is illustrated.

Without stabilization, the third pattern 41 .3 can differ with respect to the first and second patterns 41.1 and 41.2. It can be, e.g., distorted, translated, rotated, skewed, and/or magnified. Also, a defocus can be present. This changes the size (i.e. , the width) of the secondary beams 9.1 , 9.2, 9.3 in the imaging plane. Such deviations decrease the image quality or even lead to total loss of signal. Furthermore, it is also possible that the second pattern 41 .1 differs from the first pattern 41 .1 . It can be, e.g., distorted, translated, rotated, skewed, and/or magnified. In other words, the primary beams 3.1 , 3.2, 3.3 and the secondary beams 9.1 , 9.2, 9.3 can be distorted. Such distortion is often-times rooted in a charge buildup at the sample 7. Also, individual spots 15 may be brighter or darker than other spots 15. Some spots 15 may be offset if compared to their reference position defined by the first pattern 41 .1 . Some spots 15 may have a larger size than other spots. Also, such properties of the individual spots 15 may be dependent on the charge buildup of the sample 7. Since the charge buildup may be spatially dependent, individual spots 15 may be affected differently.

FIG. 5 schematically illustrates the effect of a charge buildup 1050 at the sample 7. Illustrated in FIG. 5 is a cross-section through the sample 7. A charge buildup 1050 occurs within the illumination region 1060 due to the extraction of secondary electrons. The charge buildup 1050 results in at least one aberration of the secondary electron beams 9.1 , 9.2, 9.3. The charge buildup 1050 also results in at least one aberration of the primary electron beams 3.1 , 3.2, 3.3. As illustrated in FIG. 5, such at least one aberration of the primary electron beam 3.1 , 3.2, 3.3 can, in particular, include a dislocation/deflection of the scanning position of each of the primary electron beams 3.1 , 3.2, 3.3 on the sample 7. I.e., the focus points 5.1 , 5.2, 5.3 are offset. This can cause a distortion of the acquired images. For example, an exemplary image 2020 - e.g., in the FOV of the primary electron beam 3.1 - is shown in FIG. 6. The distortion increases from bottom to top, due to a timedependency of the charge buildup. The undistorted corresponding image 2021 is illustrated in FIG. 7, as reference. A charge buildup is typically dependent, both, on the spatial position on the sample 7 (as previously illustrated in connection with FIG. 2); a charge buildup is also dependent on time. With progressing illumination, the charge buildup increases and then typically settles to an equilibrium value after a certain time.

Various techniques are based on the finding that it is possible to determine an estimate of the charge buildup 1050 based on one or more characteristics of the secondary beams 9.1 , 9.2, 9.3 measured at the detector side of the MSEM 1 . In particular, a multi-pixel image of the spots of the secondary beams 9.1 , 9.2, 9.3 in the imaging plane can be acquired. For this purpose, an auxiliary monitoring system for monitoring the one or more characteristics of the secondary beams 9.1 , 9.2, 9.3 may be available in the detector system 600 of the MSEM 1 . This is shown in FIG. 8.

An example of the detector system 600 of the MSEM 1 is illustrated in FIG. 8. The detector system 600 includes an electron-to-light conversion element 602, arranged in the imaging plane 225. The electron-to-light conversion element 602 is configured to convert the secondary electrons of the secondary beams 9.1 , 9.2, 9.3 into light. The detector further includes an optical relay system with optical elements 605 and 611 for imaging and guiding the excited light from the electron-to-light conversion element 602 to detection elements 623. The signals of the detection elements 623 are used for determining pixel values of pixels of images acquired for each FOV, for eventually determining a microscopic, strongly magnified compositive image of the sample 7. For that purpose, the optical relay system can include a zoom lens system 611 , mirrors 607, rotating prisms (not shown) and light guiding fibers 615. In the example of FIG. 8, the detector system 600 is configured to image the excited light from the electron-to-light conversion element 602 into an image plane of a primary detector 612, in which a plurality of entrance openings 613 of optical fibers 615 are arranged. Each entrance opening 613 is associated with a secondary beam. A fourth pattern 41 .4 of these entrance openings 613 is shown in FIG. 9. The fourth pattern 41 .4 is thereby defined by the arrangement of the entrance openings 613 of the optical fibers 615, and by the magnification by the optical system comprising lens 605 and zoom lens 611 . If the third pattern 41 .3 changes over time, e.g., due to charge buildup, it is possible that the secondary beams drift in-between the entrance openings 613; which can degrade the image quality. This corresponds to inter-beam crosstalk.

To mitigate effects of the charge buildup, the detector system 600 further includes a monitoring system 230 with a multi-pixel detector 232 including multiple pixels 626; the monitoring system 230 also includes an optical relay lens 235 of the monitoring system 230. The monitoring system 230 is coupled by a beam divider 237. The multipixel detector 232 typically operates at a slow frame rate of for example of 0.1 to 1 kHz and is thus not capable to collect the intensity signals at raster-scanning speed of about 20MHz to 80MHz. The multi-pixel detector 232 acquires multi-pixel images of the secondary beams 9.1 , 9.2, 9.3. The third pattern 41 .3 can be measured relatively fast. The multi-pixel detector 232 has a higher measurement bandwidth that the detector elements 623; but typically lower sensitivity. The images of the spots of the secondary beams 9.1 , 9.2, 9.3 acquired using the multi-pixel detector 232 are still sufficiently accurate for determining an estimate of the charge buildup 1050.

FIG. 10A is a flowchart of a method according to various examples. FIG. 10A is a flowchart of a method of operating a multi-beam scanning electron microscope. The method of FIG. 10A can be executed by a processing device. The method of FIG. 10A may be executed by a processor upon loading program code from a memory and upon executing the program code. For instance, the method of FIG. 10A may be executed by the processing device 800 of the MSEM 1 illustrated in FIG. 1 . The method of FIG. 10A will be explained with reference to the MSEM 1 ; but may be executed by other types of multi-beam scanning electron microscopes as well.

At box 3005, a sample (cf. FIG. 1 : sample 7) is loaded into an imaging position of the MSEM. The imaging position corresponds to a positioning of the sample in a vacuum chamber of the MSEM in which the imaging mode can be executed, i.e. , images of the sample could be acquired in this position. Sometimes, a sample stage can be moved with in the vacuum chamber of the MSEM between multiple positions, e.g., the imaging position and the calibration position. In the calibration position, imaging may not be possible. On the other hand, a calibration sample may then be positioned so that the multiple primary charged electron beams can impinge on the calibration sample, while the sample under investigation/the sample stage is offset from the imaging position.

Box 3005 may include placing the sample in a load lock of a vacuum chamber. The load lock can then be evacuated and subsequently, the sample can be moved by a motorized stage (cf. FIG. 1 : sample holder 500) into the imaging position at which it can be illuminated by the primary electron beams.

For instance, the sample may be a semiconductor sample, e.g., a chip or die or a wafer carrying semiconductor structures. For instance, the semiconductor structures could be logic gates, supply lines, memory cells, etc., to give just a few examples.

At box 3010, the sample - then in the imaging position - is illuminated using multiple primary electron beams. Box 3010 can include raster scanning the multiple primary electron beams across the sample, using a collective deflection scanner (cf. FIG. 1 , deflection scanner 110). Since the sample is illuminated, a charge buildup may occur.

Box 3010 may include imaging the sample. This includes raster scanning the primary electron beams across the sample and for each scanning position acquiring a respective intensity value, using the primary detection modality having a high sensitivity but relatively small measurement bandwidth, e.g., the detection elements 623 in the example detector system 600 illustrated in FIG. 10A. Based on one or more images per field of view, a composite image is stitched together. Upon obtaining the composite image, the sample stage may be moved to obtain a further composite image for another location on the sample.

Box 3010 may include acquiring a sequence of multiple images for each FOV and combining the multiple images for each field of view to an aggregate image. Frameaggregation can be executed.

It is not required in all scenarios that the sample is illuminated for the purpose of imaging the sample. For instance, prior to executing the imaging of the sample, it would be possible to execute a calibration routine to determine the charge buildup on the sample. In such case, a smaller exposure dose may be used at box 3010 if compared to the previous case in which the samples imaged. Next, at box 3015, one or more characteristics of the secondary electron beams associated with the primary electron beams of box 3010 are measured. This measurement takes place at the detector side of the MSEM.

As a general rule, the one or more characteristics of the secondary electron beams may include one or more characteristics of individual spots of the secondary electron beams and/or may include one or more characteristics of the pattern of the secondary electron beams, cf. FIG. 4: pattern 41.3.

The measurement(s) at box 3015 may be based on an image or a sequence of images acquired by an auxiliary multi-pixel detector such as the multi-pixel detector 232 discussed in connection with FIG. 10A.

Example characteristics include a magnification of the pattern 41.3 of the secondary beams. I.e., a scaling factor of the pattern 41.3 if compared to a reference pattern can be determined. It has been found that such magnification of the pattern 41 .3 is indicative of the global charge buildup across the illumination area. Local variations of the charge buildup, i.e., a spatial dependency of the charge buildup within the illumination area may be more difficult to determine based on such magnification of the pattern 41 .3. The magnification/scaling factor of the pattern 41 .3 may be associated with the average inter-beam pitch. For larger magnifications, larger average inter-beam pitches are observed.

Alternatively or additionally, an inter-beam pitch change may be determined (cf. FIG. 4: pitch 1061 ), e.g., for each of multiple secondary electron beams. Such inter-beam pitch change means that the distance between the adjacent spots 15 is deviating from a reference value. For this, an image acquired using the multi-pixel detector 232 may be analyzed. For instance, such image analysis may based on a peak detection identifying bright spots in the multi-pixel image. A predefined reference pattern may be fitted and deviations of the spot locations may be determined based on the fit. Alternatively or additionally, an inter-line pitch (cf. FIG. 4: inter-line pitch 1062) may be determined. Such pitch changes as discussed above are indicative of local variations of the charge buildup in those regions.

As discussed above, image domain analysis of an image acquired using the multipixel detector 232 can yield such characteristics of individual secondary electron beams. For instance, a machine-learning model may be employed that operates based on individual images acquired for the pattern of secondary electron beams. The machine-learning model may be providing a scalar output, quantifying the magnification of the pattern 41 .3. For instance, such machine-learning model may be implemented as a deep neural network including multiple convolutional layers.

As a general rule, in addition or alternative to such image-domain analysis of an image acquired using the multi-pixel detector 232, Fourier-domain analysis of the image would be possible. I.e., a Fourier transformation can yield a Fourier-domain representation of such image. The Fourier domain is also sometimes referred to as k- space or spatial frequency domain. Due to the geometric shape of the pattern 41 .3, such Fourier-domain representation of the image is particularly helpful for determining one or more characteristics of the pattern 41.3. For instance, a fast technique to determine one or more characteristics of the pattern 41.3 based on a sparse matrix multiplication in Fourier-domain is disclosed in PCT/EP2024/051248 filed on January 19, 2024 as well as German patent application 102023 101 358.0 filed on January 19, 2023, the disclosure of which is incorporated herein by reference.

Alternatively or additionally to one more characteristics of the pattern 41 .3, one more characteristics of individual secondary electron beams can be considered. For this, each individual spot 15 may be analyzed. Examples include a beam spot displacement of one or more of the secondary electron beams 9.1 , 9.2, 9.3 (e.g., with respect to an absolute reference position, rather than with respect to its neighbors), a beam spot size of one or more of the secondary electron beams 9.1 , 9.2, 9.3, a beam spot shape of one or more of the secondary electron beams 9.1 , 9.2, 9.3, and/or a beam brightness of one or more of the secondary electron beams 9.1 , 9.2, 9.3. These are beam-specific characteristics that can be used for identifying a local charge buildup at the position of the sample at which that secondary electron beam 9.1 , 9.2, 9.3 originates.

As a general rule, such measurement may be executed in a time-resolved manner. I.e., it would be possible that while the sample is being illuminated, e.g., while the primary electron beams are scanned across the sample for acquiring one or more images per field of view, multiple multi-pixel images of the secondary beams are acquired. For instance, FIG. 10B illustrates a time dependency of the magnification 7001 of the pattern 41.3. Clearly visible is the initial increase of the magnification (and along with it of the charge buildup) and the subsequent saturation. However, changes in magnification depend on the charging sample. I.e. , magnification might be increased or decreased when different charging samples are used. FIG. 10B furthermore illustrates the shift 7002 (for each x- and y-direction) of a given beam spot of one of the secondary electron beams; as well as the spot size 7003 of the given beam spot. Further, FIG. 10C illustrates a time dependency of the inter-beam pitch 7004.

Note that while in FIG. 10C an increase of the inter-beam pitch 7004 is illustrated - similar to the increase of the magnification 7001 as shown in FIG. 10B - in other scenarios, depending on the sample and/or the polarity of the charged particles of the charge-particle microscope - a decrease of the inter-beam pitch 7004 and magnification 7001 may be observed.

These characteristics 7001 , 7002, 7003, 7004 can be matched to a charge buildup of the sample. Since these characteristics 7001 , 7002, 7003, 7004 exhibit a time dependency, also the charge buildup of the sample exhibits a time dependency.

Referring again to FIG. 10A: At box 3016, a prediction of the at least one characteristic may be optionally determined; this is based on the measurement of box 3015. The prediction may be associated with one or more future time points. As explained in connection with box 3015 and FIG. 10B as well as FIG. 10C, various characteristics of the secondary charged particle beams - e.g., a magnification of the pattern of secondary electron beams that is associated with an average inter-beam pitch - may exhibit characteristic time dependencies. These time dependencies may be reproducible, since they depend on well-defined charging effects of the sample. According to examples, a machine-learning model (prediction model) may be used for providing such prediction. The prediction model may obtain an input sequence and provide one or more outputs, e.g., each output being associated with a prediction of a respective characteristic at the respective future point in time.

For instance, a sequence-to-sequence prediction model may be used that provides predictions of a characteristic of the secondary electron beams at multiple future points in time.

The input to such prediction model may include a sequence of images depicting the pattern of secondary electron beams. The output of such prediction model may include a sequence of inter-beam pitches or magnification/scaling factors of the pattern of secondary electron beams, at least some of these entries of the sequence pertaining to future points in time. An image-to-scalar prediction is illustrated in connection with FIG. 10D. Here, during a measurement interval 910, multiple images 905, 906, 907, 908 depicting the pattern of secondary electron beams at respective measurement points in time 911-914 are acquired. These images 905, 906, 907, 908 are jointly processed in the prediction model, to provide, as output, scalar values indicative of the magnification factor 7001 for future points in time 921-924 distributed across a prediction horizon 920. The output is provided at the current point in time 930; the processing latency 931 is also illustrated.

Beyond such an image-to-scalar implementation of the prediction model, a scalar-to- scalar implementation of the prediction model is another option. For instance, a sequence of inter-beam pitches as determined based on acquired images of the secondary electron beams may be input to the prediction model and the machinelearning model may provide a sequence of inter-beam pitches determined for future point in time. This is illustrated in FIG. 10E, where the images 905-908 are first processed to obtain respective values of the magnification 7001 . Then, the time sequence of these values of the magnification 7001 are processed in the prediction model (e.g., a regression model) In order to obtain the prediction of the magnification 7001 for the future point in time 921-924 during the prediction horizon 920. It has been found that the processing latency 931 may be smaller for such processing is illustrated in FIG. 10E if compared to the scenario of FIG. 10D, because the prediction model may be smaller, i.e., employ fewer weights and operate faster.

Depending on the particular implementation of the prediction model, various architectures or prediction methods are possible. Examples include linear regression, random forest, recurrent deep neural networks, to give just a few examples.

Regression may refer to a statistical method used to establish relationships between variables, such as time-dependent changes in secondary electron beam characteristics and the corresponding charge buildup on the sample. For example, linear or nonlinear regression techniques can be applied to model how characteristics like magnification, inter-beam pitch, or spot displacement evolve over time due to charging effects. Random forest may refer to an ensemble learning method that combines multiple decision trees to improve prediction accuracy and robustness. In the context of charge buildup estimation, a random forest model can analyze patterns in multi-pixel images acquired by the monitoring system, such as spatial variations in secondary electron beam spot brightness or displacement.

Recurrent deep neural networks may refer to a class of artificial neural networks designed to process sequential data, such as time-dependent changes in secondary electron beam characteristics. These networks can capture temporal patterns in the evolution and predict future states based on the historical data. For example, a recurrent neural network can model how the magnification or inter-beam pitch of the secondary electron beam pattern changes over time, enabling predictions of aberrations caused by charge buildup at future points in time.

According to various examples, multiple frames I images may be acquired, for building a training dataset. The data may comprise the current frame, the last frame, two frames back, and so on, up to m frames back. Such data acquisition allows for capturing temporal and spatial variations in the characteristics of the secondary electron beams, which can be indicative of charge buildup. The dataset thus obtained may be split into training samples and validation samples. A prediction model including a regression architecture may then be fitted on the training samples, where the prediction model predicts one or more characteristics of the secondary electron beams based on historical frames. Unsupervised learning is thus possible.

The performance of the prediction model may be evaluated on the validation samples to assess its predictive accuracy. The evaluation process may involve comparing predicted and actual values of the characteristics of interest, such as beam spot displacement, inter-beam pitch, or magnification. Based on this evaluation, the prediction model's performance may be optimized by adjusting parameters or selecting the most suitable prediction model.

The method may further include comparing the performance of multiple prediction models to determine the best-performing one. This comparison may be based on metrics such as mean squared error, R-squared value, or other relevant performance indicators. Referring again to FIG. 10A: Based on one more such - measured and/or predicted - characteristics of the secondary electron beams, it is then possible to determine an estimate of a charge buildup of the sample, at box 3020. The charge buildup may be estimated for the particular points in time for which the one or more characteristics are estimated. These may be future point in times in case a prediction is implemented at box 3016.

Box 3020 is based on the finding that such characteristics of the secondary electron beams as disclosed above are typically primarily or exclusively rooted in the charge buildup.

For instance, it would be possible to determine the estimate of the charge buildup based on a predefined lookup table that links values of the one or more characteristics of the secondary electron beams to the charge buildup. For instance, a lookup table may link the magnification of the pattern 41 .3 of the secondary electron beams to a charge buildup across the illumination area. In such a scenario, the charge buildup may not be considered to vary within the illumination area. Such look-up table is shown in TAB. 1 .

TAB. 1 : example lookup table mapping a magnification of the pattern 41 .3 of the secondary electron beams in the imaging plane to a charge buildup at the sample. The charge buildup is considered to be constant within the illumination area.

Even when the charge buildup is considered to be constant within the illumination area, i.e., the estimate of the charge buildup is not determined spatially dependent, still different primary and secondary electron beams will be affected differently by such charge buildup, depending on their position within the illumination area. For instance, primary and secondary electron beams arranged closer to an edge of the respective pattern may be differently affected if compared to those primary and secondary electron beams arranged closer to the center of the pattern, due to edge effects.

Such lookup table as the example TAB. 1 may be determined in a calibration process or based on ray-tracing simulations of the secondary electron beams in the presence of varying charge buildups. If a calibration process is used, samples with well-defined charge dissipation properties may be used as a calibration tool; for instance, metal printing of a test structure is one option. Alternatively, micro-electromechanical structures - e.g., free-standing structures - can be used, because these MEMS structures have well-defined charge dissipation properties. MEMS structures may also be fabricated with capacitors for in-situ sensing the charge buildup. For instance, a known charge up - e.g., using a secondary measurement technique - may thereby be linked to different values of the magnification of the pattern.

As will be appreciated, such lookup table linking the magnification of the pattern 41.3 to the estimate of the charge buildup, is a relatively simple technique that enables fast determining of the estimation of the charge buildup. Accordingly, this approach is particularly useful if fast compensation, e.g., parallel to an imaging of the sample, e.g., for an in-situ physical compensation, is required. However, scenarios are conceivable in which accuracy of the determination of the estimate of the charge buildup is more important than speed. Such scenarios could, e.g., pertaining to retrospective off-line compensation, i.e., after the imaging has been completed. In such a scenario, parallel to acquiring data for imaging, the multi-pixel images of the secondary electron beams can be acquired as auxiliary information based on which the retrospective off-line compensation is later on executed. In such retrospective offline compensation, more complex techniques for determining the estimate of the charge buildup can be employed.

For instance, the estimate of the charge buildup based on a predefined model. Such predefined model can link values of the at least one characteristic of the secondary electron beams to the charge buildup. The model may include a functional dependency. This functional dependency may be based on heuristics, e.g., derived from a raytracing simulation. Alternatively, a machine-learning model may be used, e.g., a deep neural network. Such machine learning model may accept, as an input, and image acquired using the multi-pixel detector imaging the pattern of the secondary beams. Such machine learning model may output a charge buildup map that quantifies, for the composite field of view, the estimate of the local charge buildup. Training data for training such machine-learning model may have been obtained from ray-tracing simulations of the multiple secondary electron beams through the detector systems of the multi-beam scanning electron microscope, in the presence of varying charge buildups. Irrespective of the particular implementation of a model, using such model, a regression inference for determining the estimate of the charge buildup in a continuous result space can be executed, rather than only inferring discrete values as in the look-up table.

In some scenarios, it would even be possible that the estimate of the charge buildup of the sample is determined based on a ray-tracing simulation of the multiple secondary electron beams through the detector system of the multi-beam scanning electron microscope. Then, parameters of the ray-tracing simulation can be iteratively varied until the pattern observed in the ray-tracing simulation matches the measured image of the pattern of the multiple secondary electron beams. Such a scenario tends to provide comparatively accurate results, however, on the other hand, requires significant computational resources. This may limit its applicability to off-line scenarios in which the estimate of the charge buildup is determined after finalizing the imaging of the sample.

As discussed above in connection with FIG. 10B, the one or more characteristics of the secondary electron beams can be measured resolved in time domain.

Accordingly, at box 3020, it would be optionally possible to determine the estimate of the charge buildup of the sample with a time resolution. For instance, a model may be inferred multiple times, once for each time sample of the one or more characteristics of the secondary electron beams.

At optional box 3025, the charge buildup of the sample may be matched to the structure types of multiple structures of the sample. For instance, the sample may include an array of structures, wherein certain structures of a given type are reappearing at different positions of the sample. A typical example would be a semiconductor sample in which memory elements, e.g., filled trenches, are arranged in a certain pattern. Then, based on such matching of the spatially-dependent estimate of the charge buildup to the structure types of the multiple structures, it is possible to augment the spatially-dependent estimate of the charge buildup of the sample based on a repetitive arrangement of the structures of the same type of the sample surface. Simply speaking, if the structure of a certain type - e.g., a transistor gate oxide - shows a relatively high charge buildup due to its insulating nature, it may be possible to interpolate such finding that is based on the measurement for a given transistor gate oxide to multiple other transistor gate oxides arranged in other sample positions. Such repetitive arrangement of the structures of the same type may be derived from prior knowledge of the sample. For instance, for semiconductor structures the repetitive arrangement may be available in mask design data of a lithography mask. Such augmentation has the advantage that it is not required to determine - based on a respective measurement box 3015 - the charge buildup for each position on the sample. For instance, by moving the sample stage, multiple composite images of the sample may be acquired for different regions on the sample. Then, it is not required, for each stage position, to execute box 3015 and box 3020.

Next, at box 3030, an estimate of at least one aberration of the multiple primary electron beams resulting from the charge buildup of the sample is estimated. This is for one or more points in time for which the charge buildup is estimated. A predictive estimation is possible, e.g., if box 3016 is executed.

Such estimate may be determined using a predefined model that provides a charged- induced electrical stray field at a sample surface of the sample for each of the multiple primary electron beams, i.e. , at each of their positions across the sample surface. The further away the spot position of a given one of the multiple primary electron beams is from a local charge buildup, the smaller the amplitude of the electrical stray field.

In other words, local charges or potentials can be determined for discretized finite elements on the sample surface, based on the estimate of the charge buildup determined at box 3020. Then, using electrostatic equations, the electrical stray field effected by these local charges or potentials and their impact on each primary electron beam can be derived. This is illustrated in FIG. 11 , here specifically for a deflection-type aberration. FIG. 11 illustrates a model for calculating a deflection dp of the primary electron beam 3.1 . The sample is discretized into such charged discs (finite elements) of size A and having a potential U. Then, based on electrostatic physical laws, the deflection outside of a given charged disc and inside that given charged disc is calculated, a as function of A, U, the landing energy LE and the distance p to the charged disc. An example dependency of the deflection as a function of the distance to the charged disc-type charge buildup 1050 (inside and outside of the disc-type charge buildup 1050) is shown in FIG. 12. Such calculation can be repeated for each finite element, e.g., each charge disc (inner loop). Such calculation may be repeated for each of the primary electron beams (outer loop).

The model presented above is a relatively simple example. More complex implementations would be possible. For instance, the model may include charge diffusion effects. A charge diffusion term of the model may model a spatial diffusion of the local charges across the surface. This may provide a time-dependency of the charge buildup. The model may include a time dependency that models a timedependent buildup of the local charges on the sample surface as a function of an exposure of the sample to the multiple primary electron beams.

Furthermore, the model is not limited to determining the deflection of each of the multiple primary electron beams. For instance, also a distortion of the beam spot 15 on the sample surface may be modeled. An aberration may be modeled. Other examples include defocus and astigmatism. Attenuation of the electron flux may be modeled. Furthermore, it is not only possible to model beam-specific aberrations of the charge buildup. Alternatively or additionally, it would be possible to model at least one pattern-specific aberration. Here, the pattern of the primary electron beams at the sample surface may experience, e.g., a magnification change, rotation or translation, or may be skewed.

Next, some specific examples of dependencies between local charge buildup and other types of aberrations that may be modeled are provided. For instance, it has been found that for a homogenous charge buildup of the sample, the primary beam defocus introduced by the local electric fields at the sample is most prominent for small landing energies. There is a clear correlation between the surface voltage of the sample and the amount of defocus. The amount of defocus per voltage of charging also depends on the size of the charged area at the sample surface, i.e., on how the charge is distributed due to diffusion or previous scans: The larger the charged area, the larger the amount of defocus per voltage of charging. Similar relationships exist between magnification change or beam astigmatism and the charge distribution at the sample surface. Such and other dependencies may be captured by a model or a look-up table. Based on the knowledge of at least one aberration of the primary electron beams, it is optionally possible, at box 3035, to perform a discharge of the sample. This may be conditional, e.g., by executing box 3034. Example trigger criteria to be checked at box 3034 may include whether the estimated charge buildup exceeds a certain threshold. The sample discharge at box 3035 can be based on illuminating the sample with neutralizing charged particles of the opposite charge as the charged particles used in the primary and secondary charged particle beams in box 3010. The sample discharge at box 3035 may also be based on providing a neutralizing gas, e.g., including free radicals, to the sample. The discharge may be based on a mirror mode operation of the MSEM. In the mirror mode operation, the acceleration voltage applied to the electrons close to the source is reduced. Accordingly, the primary electrons do not have sufficient energy to impinge on the sample. The sample can however still influence the secondary electron beams based on electrostatic forces.

The discharging using the mirror mode operation is briefly explained next. For neutral samples (no surface charge, zero charge buildup) primary electrons in the so-called mirror mode operation do not interact with the sample but are reflected closely above the sample. If the sample surface is charged positively, the surface electric field attracts those electrons and the positive surface charge can be neutralized. The process stops when the surface charge is zero since the electrons will then no longer interact with the sample.

The sample discharge can be configured based on the estimate of the charge buildup at box 3020. The sample discharge may not completely compensate the charge buildup but may rather reduce the charge buildup. A residual charge buildup may then be compensated in the subsequent box 3040.

It is noted that boxes 3034, 3035 are both optional.

At box 3040, a (residual) charge buildup can be compensated.

This compensation may be executed preemptively, e.g., if the charge buildup and the at least one aberration of the primary electron beams are predicted based on a prediction obtained from box 3016 for the one or more characteristics of the secondary electron beams. A preemptive compensation has the advantage that processing latencies for determining the one or more characteristics of the secondary electron beams at box 3015, determining an estimate of the charge buildup at box 3020, determining an aberration at box 3030 and determining the appropriate compensation action at box 3040 can be accounted for. In particular, a prediction horizon may be in the same order of magnitude as the processing latency or larger. Thereby, it is possible to estimate the particular charge buildup and aberration to be compensated at the precise point in time of execution of box 3040 - even if the measurement at box 3015 has been executed earlier. For instance, a typical processing latency has been found to be on the order of 400 milliseconds to 1 second for a MSEM device with standard hardware. This time is required for executing image analysis to determine the magnification factor of the pattern of secondary electron beams, due to the significant image size. Thus, by predicting the magnification factor within a prediction horizon of 0.5 seconds to 2 seconds, the processing latency can be accounted for.

Box 3040 may include a physical compensation (box 3045), in which one or beam control elements of the beam generation apparatus (cf. FIG. 1 : beam generation apparatus 300) of the multi-beam scanning electron microscope or controlled based on the estimate of the at least one aberration of the multiple primary electron beams. For instance, a field lens such as the field lens 333 may not have the capability of individually affecting the primary electron beams 3.1 , 3.2, 3.3. It rather acts coherently on all of the primary electron beams 3.1 , 3.2, 3.3. For instance, a patternspecific aberration may be compensated by a field lens. For instance, for a magnification change of the pattern of the primary electron beams 3.1 , 3.2, 3.3, lens excitations or multipole excitations may be used for compensation. As a magnification change manifests itself as a kind of distortion in the images, also a numerical correction in the image may be feasible (as will be described below). For a beam-specific aberration that varies from primary electron beam to primary electron beam, such global compensation may only be able to compensate a baseline of a given aberration observed across all primary electron beams or possibly a linear gradient; a residual contribution of varying size may be observed for each individual primary electron beam after such global compensation. Sometimes, a MAP such as the MAP 306 may have the capability of individually affecting each of the primary electron beams. I.e., each aperture of the multi-aperture plate may be associated with a respective beam shaping unit that locally acts on that particular primary electron beam. In such a case, it would be possible that one or more beam properties of the primary electron beams are locally compensated for each beam at the multi- aperture plate. For example, microoptics acting as multipoles on the individual beams may be used to correct astigmatism of individual beams. Such physical compensation of the at least one aberration of the multiple primary electron beams may also be referred to as a pre-compensation. I.e. , the multiple primary electron beams are affected, e.g., deflected, distorted, changed in spot size, etc., prior to the multiple primary electron beams being affected by the local charge buildup. Thus, each of the multiple primary electron beams can be pre-conditioned before reaching the sample: E.g., a pre-deflection may be applied that is approximately inverse to the deflection affected by the charge buildup further downstream along the particle beam at the sample. Then, the net deflection is zero (or at least close to zero).

Such pre-compensation of the at least one aberration may be performed timedependent. I.e., scenarios are conceivable in which the measurement at box 3015 is executed in a time-resolved manner. Then, the estimate of the charge buildup determined at box 3020 can be time-dependent. This time dependency can be used to estimate a time-dependent at least one aberration of the primary electron beams at box 3030. For longer illumination durations, typically a higher charge buildup is observed. However, the charge buildup may saturate to a certain value.

The at least one aberration may alternatively or additionally be compensated in digital postprocessing (cf. box 3050) of an image of the sample that is acquired using the multi-beam scanning electron microscope. For instance, a - e.g., pixel-wise - transformation may be applied to such image, wherein transformation parameters are determined based on the at least one aberration - which may comprise one or more beam-specific aberrations.

The digital postprocessing may be executed for each image of a sequence of images acquired for each field of view. Typically, a sequence of images is acquired for each field of view. Each of the images is acquired using a relatively short dwell time of the primary electron beams at each scan position. This is done to limit the charge buildup. Nonetheless, certain positions on the sample are scanned multiple times for the multiple subsequent images so that from image to image in the sequence of images the charge buildup can increase. As discussed previously in connection with FIG. 10B, it is possible that the estimate of the charge buildup is time-dependent (based on a time-dependent measurement at box 3015). Then, each of the images in the sequence of images - prior to combining them as part of the frame-averaging process - can be associated with a respective charge buildup and, furthermore, can be associated with a respective estimate of the at least one aberration of the multiple primary electron beams. Thus, different strengths/values of the at least one aberration can be considered in the compensation at box 3050 for each of the images in the sequence of images acquired for each field of view.

As a general rule, combinations of box 3045 and box 3050 are conceivable. For instance, it would be possible to execute box 3045 parallel to imaging a sample. Then, the residual effect of the charge buildup can be compensated after completing the imaging, when executing box 3050.

Box 3010 through box 3035 may be re-executed, e.g., for multiple different positions of the sample stage or different (parts of) a composite FOV. This is shown by the dashed arrow and the multiple iterations 3009

Summarizing, techniques have been disclosed that enable to infer at least one aberration, specifically a deflection, of multiple primary charged particle beams based on measurements on multiple secondary charged particle beams in a multi-beam scanning charged particle microscope. The type of the aberration is, however, not limited to deflections of the multiple primary charged particle beams. Other types of aberrations can be considered, e.g., an astigmatism, a defocus, etc. This calculation is executed by the intermediate step of determining a charge buildup of the sample. A physical pre-compensation of each of the at least one aberration is enabled, in an online process including in situ measurements. Alternatively or additionally, a postprocessing of images acquired using the multi-beam scanning charged particle microscope for post-compensating each of the at least one aberration is possible.

Further summarizing, at least the following EXAMPLES have been disclosed:

Although the invention has been shown and described with respect to certain preferred embodiments, equivalents and modifications will occur to others skilled in the art upon the reading and understanding of the specification. The present invention includes all such equivalents and modifications and is limited only by the scope of the appended claims.

For illustration, various examples have been specifically disclosed in connection with an MSEM. However, similar techniques may also be employed for other multi-beam scanning charged particle microscope, e.g., using helium ions. For further illustration, various examples have been discussed in the context of a deflection-type aberration. However, the disclosed techniques are not limited to determining an estimate of a deflection of each of multiple primary charged particle beams. Other types of aberrations can be estimated.

For still further illustration, various examples have been previously disclosed in connection with determining at least one aberration of multiple primary charged particle beams in a multi-beam scanning charged particle microscope. However, similar techniques may be readily employed for determining at least one aberration of a single primary charged particle beams for a single beam scanning charged particle microscope such as a conventional SEM.

For still further illustration, various examples have been disclosed in which, in a first step, an estimate of a charge buildup of the sample is determined, followed by a second step of determining an estimate of at least one aberration of multiple primary charged particle beams. As a general rule, the second step of determining the estimate of the at least one aberration of the multiple primary charged particle beams is optional. It may suffice to estimate the charge buildup of the sample and, based on the charge buildup of the sample, perform a compensation of an associated aberration. For instance, techniques have been disclosed above in which a characteristic of the pattern of secondary charged particle beams is predicted based on respective measurements. These techniques of predicting the characteristic of the pattern of secondary charged particle beams may be employed for predicting the charge buildup. In such a scenario, based on the predicted charge buildup, a compensation may be applied, e.g., pre-emptively and even without explicitly or implicitly determining an estimate of the aberration of the multiple primary charged particle beams stemming from the charge buildup.

Claims

C L A I M S

1 . A method of operating a multi-beam scanning charged particle microscope (1 ), the method comprising:

- loading (3005) a sample (7) into an imaging position of the multi-beam scanning charged particle microscope (1 ),

- when the sample (7) is in the imaging position, illuminating (3010) the sample (7) with multiple primary charged particle beams (3.1 , 3.2, 3.3),

- measuring at least one characteristic of secondary charged particle beams (9.1 , 9.2, 9.3) associated with the multiple primary charged particle beams (3.1 , 3.2, 3.3),

- based on the at least one characteristic (7001 , 7002, 7003, 7004) of the secondary charged particle beams, determining (3020) an estimate of a charge buildup (1050) of the sample (7),

- determining (3030) an estimate of at least one aberration of the multiple primary charged particle beams resulting from the charge buildup (1050) of the sample (7), and

- based on the estimate of the at least one aberration of the multiple primary charged particle beams, performing (3040, 3045, 3050) a compensation of the at least one aberration.

2. The method of claim 1 , wherein the estimate of the at least one aberration is determined using a predefined model that provides a charge-induced electrical stray field at a sample surface of the sample (7) at positions of each of the multiple primary charged particle beams.

3. The method of claim 2, wherein the model comprises a charge diffusion term to model a spatial diffusion of the local charges across the surface.

4. The method of claim 2 or 3, wherein the model comprises a time dependency that models a timedependency of the charge buildup as a function of an exposure of the sample (7) to the multiple primary charged particle beams during said illuminating.

5. The method of any one of the preceding claims, wherein the estimate of the charge buildup (1050) is time-dependent, wherein the estimate of the at least one aberration is time-dependent.

6. The method of claim 5, wherein the compensation is performed time-dependent.

7. The method of any one of the preceding claims, further comprising:

- predicting the at least one characteristic of the secondary charged particle beams based on said measuring,

Wherein the estimate of the charge buildup is predicted for a future point in time based on said predicting of the at least one characteristic.

8. The method of claim 7, wherein said predicting is based on a machine-learning model operating based on an input sequence.

9. The method of any one of the preceding claims, wherein the at least one characteristic of the secondary charged particle beams comprises one or more characteristics of a pattern (41 .3) of the secondary charged particle beams (9.1 , 9.2, 9.3).

10. The method of claim 9, wherein the one or more characteristics of the pattern (41 .3) of the secondary charged particle beams comprises a magnification of the pattern (41.3).

11. The method of claim 9 or 10, wherein the one or more characteristics of the pattern (41 .3) of the secondary charged particle beams (9.1 , 9.2, 9.3) comprise at least one of an inter-beam pitch (1061 ) or an inter-line pitch (1062).

12. The method of any one of the preceding claims, wherein the at least one characteristic of the secondary charged particle beams comprises at least one of a beam spot (15) displacement of one or more of the secondary charged particle beams, a beam spot (15) size of one or more of the secondary charged particle beams, a beam spot (15) shape of one or more of the secondary charged particle beams, or a beam spot (15) brightness of one or more of the secondary charged particle beams.

13. The method of any one of the preceding claims, wherein the estimate of the charge buildup (1050) of the sample (7) is determined based on a pre-defined look-up table linking values of the at least one characteristic to the charge buildup (1050).

14. The method of any one of the preceding claims, wherein the estimate of the charge buildup (1050) of the sample (7) is determined based on a predefined model linking values of the at least one characteristic to the charge buildup (1050).

15. The method of any one of the preceding claims, wherein the estimate of the charge buildup (1050) of the sample (7) is determined based on a ray-tracing simulation of the secondary charged particle beams.

16. The method of any one of the preceding claims, wherein the estimate of the charge buildup (1050) is spatially dependent.

17. The method of claim 16, further comprising:

- matching the spatially-dependent estimate of the charge buildup (1050) of the sample (7) to structure types of multiple structures of the sample (7).

18. The method of claim 17, further comprising: - augmenting (3025) the spatially-dependent estimate of the charge buildup (1050) of the sample (7) based on an repetitive arrangement of the structures of the same type on a sample surface of the sample (7).

19. The method of any one of the preceding claims, wherein the at least one aberration comprises at least one beam-specific aberration.

20. The method of any one of the preceding claims, wherein the at least one aberration comprises at least one pattern aberration of a pattern (41 .2) of the multiple primary charged particle beams (3.1 , 3.2, 3.3).

21 . The method of any one of the preceding claims, wherein the at least one aberration comprises a deflection of each of the multiple primary charged particle beams.

22. The method of any one of the preceding claims, wherein the at least one aberration comprises a spot distortion of each of the multiple primary charged particle beams.

23. The method of any one of the preceding claims, wherein the at least one aberration comprises a defocus of each of the multiple primary charged particle beams.

24. The method of any one of the preceding claims, wherein the at least one aberration comprises an astigmatism of each of the multiple primary charged particle beams.

25. The method of any one of the preceding claims, wherein the at least one aberration comprises a magnification change of a pattern of the multiple primary charged particle beams.

26. The method of any one of the preceding claims, wherein the at least one aberration comprises a rotation of each of the multiple primary charged particle beams.

27. The method of any one of the preceding claims, wherein said performing of the compensation comprises:

- controlling (3045) one or more beam control elements of the multi-beam charged particle microscope to deflect the multiple primary charged particle beams, thereby counteracting the at least one aberration.

28. The method of claim 27, wherein at least one of the one or more beam control elements coherently acts on the multiple primary charged particle beams.

29. The method of claim 27 or 28, wherein at least one of the one or more beam control elements individually acts on each of the multiple primary charged particle beams.

30. The method of any one of the preceding claims, wherein said performing of the compensation comprises:

- digitally postprocessing (3050) an image of the sample (7) acquired using the multi-beam scanning charged particle microscope.

31 . The method of any one of the preceding claims, further comprising:

- based on the estimate of the charge buildup (1050) of the sample (7), discharging (3035) the sample (7).

32. A circuitry for controlling operation of a multi-beam scanning charged particle microscope (1 ), the circuitry being configured to:

-control the multi-beam scanning charged particle microscope to load (3005) a sample (7) into an imaging position of the multi-beam scanning charged particle microscope (1 ),

- control the multi-beam scanning charged particle microscope to illuminate, when the sample (7) is in the imaging position, the sample (7) with multiple primary charged particle beams (3.1 , 3.2, 3.3), - measure at least one characteristic of secondary charged particle beams (9.1 , 9.2, 9.3) associated with the multiple primary charged particle beams (3.1 , 3.2, 3.3),

- based on the at least one characteristic (7001 , 7002, 7003, 7004) of the secondary charged particle beams, determine (3020) an estimate of a charge buildup (1050) of the sample (7),

- determine (3030) an estimate of at least one aberration of the multiple primary charged particle beams resulting from the charge buildup (1050) of the sample (7), and

- based on the estimate of the at least one aberration of the multiple primary charged particle beams, perform (3040, 3045, 3050) a compensation of the at least one aberration.

33. The circuitry of claim 32, wherein the circuitry is configured to execute the method of any one of claims 1 to 31 .

34. A method of operating a multi-beam scanning charged particle microscope (1 ), the method comprising:

- predicting the at least one characteristic at a future point in time based on said measuring,

- based on the at least one characteristic (7001 , 7002, 7003, 7004) of the secondary charged particle beams predicted at the future point in time, determining (3020) an estimate of a charge buildup (1050) of the sample (7) at the future point in time,

- based on the estimate of the charge buildup of the sample at the future point in time, performing (3040, 3045, 3050) a compensation of the at least one aberration.