JPH11111633A - Ion implantation simulation method and recording medium storing ion implantation simulation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an ion-implantation simulating method by which the actual ion-implantation distribution immediately after ion implantation can be reproduced with high accuracy, without making the calculation time long. SOLUTION: The concentration distribution of implanted ions is calculated by finding the resting positions of individual implanted ions by calculating the energy lost, when the implanted ions are scattered with atomic nuclei and electrons, while successively tracking the scattering of individual implanted ions in a target material. In an ion-implantation simulating method using the Monte Carlo simulation, a probability of implanted ions passing through a prescribed electron concentration is calculated in advance, and the electron concentration used for the calculation of the electron loss (S191) is decided, based on the probability. Consequently, the effect of the electron concentration distribution is incorporated into the calculation of the electron loss, and the calculation time is not increased.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a method of simulating a semiconductor manufacturing process, and more particularly to a method of simulating an ion implantation process.

[0002]

2. Description of the Related Art In a semiconductor manufacturing process, an ion implantation technique is widely used as a means for introducing impurities such as arsenic, boron and phosphorus into silicon and the like with good controllability. The implanted impurity ions lose energy while being scattered by atoms of the implanted target material (for example, silicon) and stop. Scattering includes random parameters depending on the relative positions of ions and atoms, ion species and ion implantation conditions (acceleration energy, dose, implantation angle, etc.)
To form an implantation distribution determined by Since this distribution can be controlled with good reproducibility once the target material is determined, the ion implantation step can be realized in the simulation by expressing the distribution of the implanted ions in the target material with an appropriate distribution function. By storing the parameters of the distribution function as a table with respect to the representative acceleration energy / dose amount, the distribution of the implanted ions under the conditions to be calculated can be quickly obtained with relatively good accuracy.

However, the above-described matter is effective when the target material has no structure (for example, when the target material is amorphous), and when the target material has a structure, for example, when the target material has a structure. Even when multiple substances with different densities, compositions, constituent elements, etc. are stacked, or even if they are composed of a single element, they have a crystal structure like a silicon substrate and the probability of scattering with atoms is lower than that of ions. The situation will be slightly different if the direction changes significantly depending on the direction of travel. As described above, the distribution of the impurity after implantation cannot be accurately expressed only by the parameters of the simple distribution function.

In order to solve such a problem, in ion implantation simulation, in addition to the above-described method using an analytical distribution function, ions lose energy in a scattering process in which individual ions repeat in a target material. There is a method of calculating the final ion implantation distribution by tracking until a static state is reached, calculating the number of particles, and calculating the final ion implantation distribution. Since the scattering process of each ion is tracked, the calculation time is orders of magnitude longer than that of the method using the distribution function described above. However, this method can calculate a target material of any structure with high accuracy. It is a feature of. This method uses a random number when calculating the relative position between the particle representing the ion and the target atom, so the Monte Carlo method (JP
Biersack and LGHaggmark: Nuclear Instruments and
Methods, 174 (1980) pp. 257-269). The specific calculation method will be described below.

First, as described above, when ions are incident on a target material, they are scattered by nearby atoms. First, ions undergo Coulomb scattering due to the positive charge of the nuclei of the atoms of matter. Since electrons are present around the nucleus, Coulomb scattering of ions by the nucleus can be described by scattering by Coulomb potential shielded by these electrons. In terms of the center of gravity, nuclear scattering is two-body elastic scattering, in which the direction of movement of ions changes but the overall energy is preserved. However, in the laboratory system, ions lose energy as much as the nucleus of the scattering partner undergoes recoil. The energy lost by ions due to the scattering with the nucleus is called nuclear loss. Also, the second
Then, from the time when the ions are scattered by the nucleus of one atom to the time when the ions are scattered by the next nucleus, the ions travel while exciting or ionizing the electrons of the atom. The scattering by the electrons at this time is inelastic scattering, and the ions lose energy, but the electrons are lighter than the ions, so that the movement direction of the ions does not change. The energy lost by electron scattering is called electron loss. The ions implanted while repeating these two types of scattering gradually lose energy and eventually stop.

[0006] Regarding the scattering by the nucleus, first, when the target material is amorphous, that is, when the atomic arrangement is random, the position of the partner atom where ions are next scattered is:
It is obtained using a random number based on the atomic number density of the substance, and is expressed as an impact parameter defined by the length of a perpendicular drawn from the center of the partner atom to the traveling direction of the ion. This parameter is stored in a memory or the like and used for subsequent calculations. On the other hand, when the target material is a silicon crystal or the like in which atoms are regularly arranged, the position of the next scattering partner atom can be obtained based on the position coordinates of the previous scattering partner. However, the relative position between the ion and the nucleus is not uniquely determined because of the thermal vibration of the atom (including the zero-point vibration), and it is necessary to incorporate the effect of this vibration using random numbers. Further, when the target material includes both a portion having a regular atomic arrangement and an amorphous state, the above two treatments may be used together.

On the other hand, regarding the scattering by electrons, it can be considered that ions travel in the sea of electrons without changing the direction of movement between the scattering with one nucleus and the scattering with the next nucleus. . The distance between the scatterings is generally called the free path. The energy that an ion loses due to scattering with electrons during the free path depends on the speed of the ions and the number density of the electrons. Conventionally, a spatially uniform value, that is, an average concentration of electrons, is often used as the electron number density, and this value can be obtained from the number density of atoms and the number of electrons belonging to the atoms.

However, for more microscopic treatment, the electron density should be obtained not along the spatially uniform electron concentration but along the trajectory of the ions, and the energy loss due to the electrons should be calculated according to the value. It is. When the atomic density, that is, the density of the electron density is significantly different depending on the direction, such as a silicon crystal, ions passing through a place with a high electron density lose energy before traveling a long distance, and stop at a relatively shallow depth from the surface, This is because ions passing through a place with a low electron concentration do not lose much energy even if they travel the same distance, and enter a deep place.

Here, since the electron density is a function of the position from the atomic nucleus, it is theoretically possible to sequentially determine the electron density along the ion trajectory. However, in practice, it requires much longer calculation time than ordinary Monte Carlo calculation, which is not practical.

Further, as a method with a somewhat higher accuracy than using the average concentration of electrons, the above-mentioned impact parameter is considered as a representative value of the relative distance between the ion and the nucleus. An electron loss calculation model has been proposed in which the exponential function of the impact parameter value decreases as follows (Gerhard Hobleret)
al., SISDEP'91 PP.389-398). However, even with this method, some implanted ions, called the so-called channeling effect, are not deep enough for the implantation of light ions such as boron, where electron loss has a large effect on the distribution of implanted impurities in the silicon crystal. The phenomena to be reached could not be fully reproduced.

[0011]

As described above, in the simulation of the ion implantation process using the Monte Carlo method, the calculation accuracy of the energy (electron loss) lost by the scattering of the implanted impurity ions with the electrons is not sufficient. . Therefore, the impurity distribution obtained immediately after ion implantation obtained by the simulation was not sufficiently accurate.

In particular, when the target material has a crystal structure, or when the spatial distribution of the electron density greatly affects the range of ions, electron loss is often overestimated. Therefore, there is a problem that it is not possible to calculate a phenomenon in which ions enter deeply, such as a channeling phenomenon.

The present invention has been made in view of the above circumstances, and provides an ion implantation simulation method that can accurately reproduce an actual ion implantation distribution immediately after ion implantation without increasing the calculation time. To provide.

[0014]

In order to achieve the above-mentioned object, the present invention provides a method for sequentially tracking the scattering of individual implanted ions in a target material, while preventing the implanted ions from scattering with nuclei and scattering with electrons. In the ion implantation simulation method that uses the Monte Carlo method to calculate the concentration distribution of the implanted ions by calculating the energy lost and finding the rest position of each implanted ion, when the electron loss is calculated, An ion implantation simulation method characterized in that the probability of passing through a predetermined electron concentration is calculated in advance, the electron concentration through which each implanted ion passes is determined using uniform random numbers, and electron loss is calculated.

According to the above configuration, the electron concentration distribution is taken into the simulation in a more realistic manner without substantially increasing the calculation time, so that the impurity distribution immediately after ion implantation can be obtained more accurately. Become.

That is, conventionally, the electron concentration distribution in the target material is regarded as spatially uniform, and the electron loss is calculated. It is impossible to faithfully reproduce the phenomenon that the implanted ions penetrate deeper into the target material than other ions. Conversely, it was theoretically possible to calculate the electron concentration distribution sequentially along the trajectory of the implanted ions and calculate the electron loss based on it. It was almost impossible to do. Therefore, in the present invention, a model that approximates the electron concentration distribution in the target material is prepared in advance, the space in the target material is divided based on the electron concentration in a predetermined range based on the model, and the implanted ions pass through each divided section. To determine the section through which each implanted ion passes using a uniform random number, and calculate the electron loss based on the electron concentration in the determined section, without increasing the calculation time. In addition, the effect of the density of the electron density can be taken into the simulation. Thereby, the accuracy of the simulation can be improved.

[0017]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing a functional configuration of the ion implantation simulation apparatus according to the present embodiment. In FIG. 1, an ion implantation simulation apparatus 1 includes an input unit 2 for receiving input of data and instructions from an operator, and parameter setting means 10 and particle number repetition calculation control means as functional means for simulating an ion implantation process. 11, initial condition setting means 12, scattering partner position determination means 13, scattering angle calculation means 14, nuclear loss / momentum calculation means 15, electron loss calculation means 16, ion stationary position recording means 17, concentration distribution calculation means 18, It comprises a calculation processing unit 3, an output unit 4 for outputting a calculation result, and a storage unit 5 for storing simulation programs and input data.

Here, the input unit 2 includes a keyboard, a mouse,
It consists of a floppy disk device and the like. Further, the calculation processing unit 3 and the storage unit 5 are configured by a computer system including a CPU and a storage device such as a ROM, a RAM, and a magnetic disk device connected to the CPU. Further, the output unit 4 includes a display device, a printer device, and the like. The data and instructions of each process executed by the calculation processing unit 3 are stored in the storage unit 5 and read into signals as needed to execute the calculation process.
Data such as numerical information generated on the way is stored in a storage device such as a RAM or a magnetic disk.

Next, the operation of the ion implantation simulation apparatus according to the present embodiment (that is, the ion implantation simulation method) will be described.

First, a description will be given of an ion implantation simulation method using the ordinary Monte Carlo method. FIG. 2 is a flowchart showing a processing procedure of an ordinary ion implantation simulation method by the Monte Carlo method.

In FIG. 2, first, in step 1, ion implantation conditions are input as data. What kind of ions are to be implanted into the target material, what kind of structure, the energy, the dose, and the relative position (implantation angle) between the ion beam and the target material are specified. further,
The number of particles N required for Monte Carlo calculation, parameters for setting random numbers, and the like may be input.

Next, in step 2, before starting the Monte Carlo calculation for tracking each particle, the parameters used in advance may be calculated and stored in a memory.

Next, in step 3, one n-th particle is extracted from the N particles used in the calculation specified in step 1, and the energy and angle of the specified ion implantation conditions are set, and the target is set. Incident on the material. Here, the initial conditions are set. The incident position is determined based on the relative position of the implanted ions with respect to a specific atom of the target material using the atomic density of the target and a random number.

Next, in step 4, the position of the nucleus of the next partner to be scattered next by the ion which has started to move with the energy and the angle given from the initial position set in step 3 is determined. If the target material has a crystal structure, for example, calculate the lattice and position around it based on the position of the previous scattering partner and encounter the earliest in the direction of ion travel from the candidate atomic positions Atoms are the scattering partner. On the other hand, when the target material is in an amorphous state, there is no regularity such as a crystal structure. Therefore, the scattering partner determines the coordinates of the nucleus using random numbers based on the atomic number density in the target material.

Next, in step 5, the trajectory of the scattering due to the potential between the traveling ion and the target nucleus of the next scattering partner is calculated.

Next, in step 6, the coordinates of the scattering position, the traveling direction and energy of the scattered ions, and the energy (nuclear loss) given to the target atoms during scattering are calculated.

Next, in step 7, between the scattering with one target nucleus and the scattering with the next target nucleus, the ions are regarded as going straight through the target material while interacting with the electrons. Calculates the energy loss due to electrons from the electron stopping power according to the kinetic energy of, and reduces the ion energy.

Next, in step 8, it is determined whether or not the value of the ion energy in step 3 is considered to be stationary as a result of the decrease in ion energy due to the nuclear loss and electron loss. If the value is not a value that can be regarded as stationary, the process returns to the step 4 and the steps 4 to 4 are performed until the ion energy becomes a value that can be regarded as stationary.
Step 8 is repeated.

Next, when it is determined in step 8 that the energy of the ion has reached a value that can be regarded as stationary, in step 9, the stationary position of the ion is recorded.

Next, in step 10, it is determined whether or not the repetitive calculation of the number of particles N set in step 1 is completed. If particles remain, the process returns to the step 3 to take out the (n + 1) th particle, and the steps 3 to 3 are repeated until the repetitive calculation of the particle number N is completed.
Step 10 is repeated.

Next, if it is determined in step 10 that the calculation of all the particles has been completed, in step 11, the density distribution is obtained from the data of the stationary positions of all the particles, and the density distribution is output and the calculation is terminated. I do.

As described above, the ion implantation simulation method by the ordinary Monte Carlo method has been described. Hereinafter, the calculation method of the electron loss in the above step 7, which is a characteristic part of the present invention, will be described in more detail.

In the above-mentioned prior art, since the electron loss is calculated based on the spatially uniform electron concentration distribution, the accuracy cannot be sufficiently improved. On the other hand, it is theoretically possible to obtain the potential loss along the ion trajectory successively to obtain the electron loss accurately, but it is almost impossible to actually do it due to the huge calculation time. there were. The present invention makes it possible to calculate an electron loss with high accuracy in a short calculation time by approximating an electron concentration distribution, which is a basis of calculation of an electron loss, to a more realistic one.

Therefore, first, the approximation of the electron concentration distribution which is a feature of the present invention will be described. First, the case where the target material is an amorphous material will be described. Here, a case where the target material is polysilicon will be described as an example.

The spatial distribution of electrons in the target material can be approximately expressed as a function of distance from the nucleus.
For example, the electron distribution is regarded as a spherical target distribution centering on the nucleus, and the nucleus position of Si is 0, and the distance from the nucleus is R.
Then, the electron concentration distribution can be expressed as a function of R.

As an example of the function of R, an electron concentration distribution f (R) as shown by a solid line in FIG. Since the target material is amorphous, the spacing between Si nuclei in the target material is determined from the atomic number density. Specifically, it is assumed that all the electrons for one Si exist in a sphere having a radius of 1.4 Å. Then, FIG.
Is divided into n sections as indicated by a dotted line in FIG. 3 (b), for example, and is approximately expressed. The average value f ＾ (R _i ) of the electron concentration of R in a certain range (i = 1 to n + 1) ) Makes it possible to divide the space of the silicon crystal structure by the electron concentration. For example, R = 0 electron concentration from (nuclear position of Si) up to _{R = R 1 f ^ (R} 1) volume occupied area V (R ₁₎ is _{V (R 1) = (4/3} ) πR 1 3 Can be represented by In general, the electron density f ^ volumetric region occupied the _{(R i) V (R i} ) is _{V (R i) = (4/3} ) π
Represented by ^{_{(R 1 3 -R i-1}} 3). Here, R _i
> Electron concentration in R _n is zero.

Further, the volume V occupied by one Si atom in the target material is obtained by V = 1 / d (cm ³ ), which is the reciprocal of the atomic number density d (atom / cm ³ ). Therefore, the probability that the ions pass through the region of the electron concentration f ＾ (R _i ) while traveling can be obtained by V (R _i ) / V.

As described above, in the present embodiment, the electron concentration distribution is not considered to be spatially uniform as in the prior art, and the electron concentration distribution is expressed as a function of the distance from the nucleus position as described above. It was done. Then, by calculating the electron loss of ions based on the electron concentration distribution,
The calculation accuracy can be improved. Specifically, for example, it is possible to reproduce by simulation that ions that have passed through a region with a low electron concentration more deeply penetrate into the target material than other ions. Further, since each region of the electron density through which the traveling ions pass is calculated stochastically, and the region through which the ions pass is determined by using a random number, the calculation time hardly increases.

Next, a description will be given of a simulation method in which the features of the present invention are actually applied to the ion implantation simulation method by the ordinary Monte Carlo method shown in FIG.

First, in step 2 shown in FIG. 1, the calculation of the electron concentration distribution and the calculation of the probability that ions pass through each region of the electron concentration distribution are executed and stored in the memory. Specifically, the procedure is performed as shown in FIG.

As shown in FIG. 4A, in step 12, the electron concentration distribution around the nucleus in each target material is divided into n sections, and f ＾ (R
_i ). Next, in step 13, the volume V of the space occupied by one atom in the target material and the electron concentration f
Using the volume V (R _i ) occupied by the region of ＾ (R _i ), a probability distribution P _i = ΣV (R _i ) / V is obtained and stored in the memory. Next, in step 14, the value of the electron stopping power (electron loss per electron, per unit length) of each target material is determined as a function of ion energy. For example, it may be calculated as ESP (E, k) (k is a suffix representing a target material, E is ion energy), stored in a table and stored in a memory. Then, when actually calculating the electron loss, the value of the electron stopping power can be quickly obtained from the ion energy and the target material simply by referring to the table.

Next, in step 7 shown in FIG. 1, when calculating the amount of energy loss due to electrons, it is determined which electron concentration region has passed by uniformly using random numbers, and the electron concentration f ＾ (R _{Using i} ), the amount of energy loss due to scattering with electrons is determined.

Specifically, as shown in FIG. 4B, first, in step 15, a uniform random number r between [0, 1]
Occurs. Next, in step 16, FIG.
Relative probability distribution P _i obtained in step 13 of, P _i ≦
Find _{i such that} r <P _{i + 1} . Next, in step 17, the electron density f ＾ (R _i ) determined by _i is referred to. Next, in step 18, the electron stopping power ESP is referred to from the table stored in the memory in step 14 based on the ion energy at that time and the type of the target substance to be scattered. Next, in step 19, the energy Δeloss lost as electron loss in the section of the free process L is expressed by Δeloss = ESP (E, k).
・ L · fＬ (R _i )

In the above embodiment, the electron concentration distribution f (R) is a distribution as shown in FIG.
(R) is a Hartree-Fock type muffi
The calculation may be performed assuming atoms having an n-tin structure, or a finer structure may be assumed. In addition, although the radius R _n is greater than R 0 to the electron concentration in the considered in the embodiment described above is clogged with the electronic one atom, and set as the integral value of the total electron concentration as a background electrons is correct , The electron concentration at R _{equal to} or greater than R _n may be a finite value. Further, in the above embodiment, the electron concentration distribution is spherically symmetric for simplicity. However, the spatial distribution of the electron concentration in the substance is calculated in detail by a method such as the molecular orbital method, and some space is calculated based on the result. May be divided beforehand so that the electron concentration through which ions pass can be calculated stochastically in advance.

The case where the target material is an amorphous material has been described above, but the case where the target material has a regular atomic arrangement such as a crystal structure will be further described below.

In the prior art, when the target material is a material having a crystal structure or the like, the phenomenon that some implanted ions reach deep due to the channeling effect cannot be sufficiently reproduced. The present invention enables this channeling phenomenon to be accurately reproduced. Here, a case where the target material is a silicon crystal will be described as an example.

First, in step 2 shown in FIG. 1, the angle formed by the traveling direction of the ions with respect to the atomic arrangement of the target material determines a range particularly effective for the channeling phenomenon.
Store in memory. For example, in a silicon crystal, three directions of a <100> direction, a <110> direction, and a <111> direction are directions deeply related to a phenomenon called "channeling" in which ions enter deeply. When the angle is within a certain range, the traveling direction of the ions is hardly changed by scattering, and the energy loss due to electrons is reduced. As a result, the ions reach a deep position. More specifically, as shown in FIG. 5A, in step 20, all directions in which the channeling phenomenon is likely to occur are obtained. Next, in step 21, the range of angles at which channeling is likely to occur in each direction is determined and set as the width of the angle between the traveling direction of the ions and these directions, that is, the critical angle. Here, the critical angle may be set as a constant, but is a parameter deeply related to the scattering of ions and target nuclei, and the number of protons Z _{1 of the} ion and the number of protons Z _{2 of the} target atom
Alternatively, it may be modeled as a function of the energy value of ions. Next, in step 22, when the traveling direction of the ions falls within the critical angle (direction in which channeling is likely to occur ± critical angle), the electron stopping power is set to a value which is a constant times smaller than the other cases. The parameter (fk) is set so that Next, in step 23, similarly to step 12 in FIG. 4B, the electron concentration distribution around the nucleus of the target material is approximately obtained as a function of the distance from the nucleus position. Here, when obtaining the electron concentration distribution, unlike the case where the above-mentioned target material is an amorphous material, it is assumed that all the electrons for one Si exist in a sphere having a radius of 1.175 Å. If the target material is a silicon crystal, the distance between the nearest Si nuclei in the crystal is 2.35 angstroms. Similarly to the above, this distribution is approximately expressed by dividing it into n sections as shown by the dotted line in FIG. 3B, and the average value f 値 (R
_i ) (i = 1 to n + 1) is obtained. Next, step 24
In the same manner as in step 13, the volume V of the space occupied by one atom in the target material and the electron concentration f ＾
Obtains a probability distribution _{P i = ΣV (R i)} / V with the volume area is occupied by _{(R i) V (R i} ), it is stored in memory. Next, in step 25, as in step 14, the value of the electron stopping power (electron loss per electron, per unit length) of each target material is determined as a function of ion energy.

Next, in step 7 shown in FIG. 1, when calculating the amount of energy loss due to electrons, it is determined which electron concentration region has passed by uniformly using random numbers, and the electron concentration f ＾ (R _{Using i} ), the amount of energy loss due to scattering with electrons is determined. Here, when the traveling direction of the ions falls within the critical angle, the amount of energy loss is obtained by using the parameter fk that makes the electron stopping power a value that is a constant times smaller than the other cases.

More specifically, as shown in FIG. 5B, first, at step 26, the uniform random number r between [0, 1]
Occurs. Next, in step 27, FIG.
Relative probability distribution P _i obtained in step 24 of, P _i ≦
Find _{i such that} r <P _{i + 1} . Next, in step 28, the electron concentration f ＾ (R _i ) determined by the value _i is referred to. Next, at step 29, the electron stopping power ESP is referred to from the table stored in the memory at step 25 based on the ion energy at that time and the type of the target substance to be scattered. Next, in step 30, the traveling direction of the ion at that time is checked, and if the ion is within the critical angle in each direction that facilitates channeling, the parameter fk is obtained. Next, in step 31, the energy Δeloss lost as electron loss in the section of the free process L is represented by Δeloss = fk · ESP (E, n)
・ L · fＬ (R _i ) If the angle is not within the critical angle, the parameter fk may be set to 1.

As described above, by introducing such parameters, the channeling phenomenon in which the implanted ions enter deeply can be accurately reproduced. Therefore, even when the target material has a regular atomic arrangement structure such as a silicon crystal structure, electron loss when the implanted ions advance at a remarkable angle of channeling can be accurately handled, so that the The calculation of the distribution can be performed correctly.

Here, a program for realizing the above-described ion implantation simulation method can be stored in a computer-readable recording medium. The recording medium can be read by a computer system, and the above-described program can be executed to control the computer and realize the above-described ion implantation simulation method. Here, examples of the recording medium include devices capable of recording a program, such as a memory device, a magnetic disk device, and an optical disk device.

[0053]

As described above, according to the present invention,
Since the electron concentration distribution in the target material is taken in a more realistic form and the electron loss is calculated more accurately than before, the impurity distribution immediately after ion implantation can be obtained with higher accuracy than before. In addition, the electron concentration of the implanted ions is calculated stochastically in advance, and the electron concentration of the implanted ions is determined from the probability using a uniform random number. The shading can be taken into the calculation of electron loss. Therefore, even if the target material is a material having a complicated structure, the simulation can be performed in consideration of the electron concentration distribution, so that the impurity distribution can be obtained more accurately than before. As a result, it is possible to accurately predict the impurity distribution after manufacturing in advance, to narrow down the process parameters, reduce the number of trial experiments and the development period, and improve the development efficiency. Finally, the cost of the product can be reduced.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a functional configuration of an ion implantation simulation apparatus according to the present embodiment.

FIG. 2 is a flowchart showing a processing procedure of a typical ion implantation simulation method by the Monte Carlo method.

FIG. 3 is a diagram showing an example of an electron concentration distribution of one atom approximately used as a function of a distance from an nucleus, which is used in the ion implantation simulation method according to the present embodiment.

FIG. 4 is a flowchart showing a processing procedure of the ion implantation simulation method according to the embodiment;
(A) is a flowchart showing a processing procedure for obtaining a parameter relating to electron loss executed in step 2 shown in FIG. 2 when the target material is an amorphous material or the like;
2B is a flowchart illustrating a processing procedure for obtaining an electron loss performed in step 7 shown in FIG. 2 when the target material is an amorphous material or the like.

FIG. 5 is a flowchart showing a processing procedure of the ion implantation simulation method according to the embodiment;
2A is a flowchart showing a processing procedure for obtaining a parameter relating to electron loss performed in step 2 shown in FIG. 2 when the target material is a material having a crystal structure, and FIG. 3 is a flowchart showing a processing procedure for obtaining an electron loss, which is performed in step 7 shown in FIG. 2 when the material is a material having a structure.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Ion implantation simulation apparatus 2 Input part 3 Calculation processing part 4 Output part 10 Parameter setting means 11 Particle number repetition calculation control means 12 Initial condition setting means 13 Scattering partner position determination means 14 Scattering angle calculation means 15 Nuclear loss / momentum calculation means 16 Electron loss calculating means 17 Ion stationary position recording means 18 Concentration distribution calculating means

Claims (2)

[Claims] 1. The method according to claim 1, further comprising calculating a stationary position of each of the implanted ions by sequentially calculating the energy of the implanted ions while tracing the scattering of the implanted ions in the target material. In the ion implantation simulation method using the Monte Carlo method, which calculates the concentration distribution of implanted ions, when calculating the energy lost by scattering of the implanted ions with electrons (hereinafter referred to as electron loss), the An ion implantation simulation method characterized in that the probability of passing through the electron concentration is calculated in advance, the electron concentration through which each implanted ion passes is determined using uniform random numbers, and the electron loss is calculated. 2. The stationary position of each implanted ion is calculated by calculating the energy that the implanted ion loses by scattering with an atomic nucleus and scattering with an electron while sequentially tracing the scattering of each implanted ion in a target material. When calculating the energy that the implanted ions lose due to scattering with electrons in a recording medium that stores an ion implantation simulation program using the Monte Carlo method, which calculates the concentration distribution of the implanted ions, the implanted ions have a predetermined electron concentration. And a process of calculating the electron loss of each implanted ion using uniform random numbers and calculating the electron loss, and causing the computer to execute these processes. A computer-readable recording medium storing an ion implantation simulation program.