Fluence simulation method and system for molecular dynamics simulation of device irradiation defect evolution

Abstract

The present invention provides a fluence simulation method and system for molecular dynamics simulation of device irradiation defect evolution, belonging to the field of simulation technology. The method includes: obtaining the number of PKAs generated by a converged single incident particle irradiating a device; gridding the device to obtain multiple grids containing PKAs; constructing a system model of the same size as the grid; irradiating the device in batches with incident particles of a calibrated fluence, and based on the system model, using molecular dynamics and KMC methods to perform defect evolution on the PKAs in the grids; counting the types and numbers of defects in each grid and assigning them to the device, changing the fluence of the incident particles, repeating the above steps, and obtaining the relationship between incident particles of different fluences and defect information in the device. The present invention combines molecular dynamics and dynamic Monte Carlo methods to realize the simulation calculation of the defect evolution process of the entire semiconductor device under different fluences, and the calculation logic is clear, and the steps are simple and easy to operate.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a method and system for simulating the molecular dynamics simulation of device irradiation defect evolution. Background Art

[0002] When semiconductor electronic devices are in a space environment for a long time, the semiconductor materials in the devices will be damaged by radiation from charged particles in space. Defects caused by radiation damage are mainly generated through ionization damage and displacement damage. In order to clearly understand the generation and formation mechanism of displacement defects, it is necessary to study the entire process of primary knockout particle (PKA) cascade collision, high temperature annealing, and finally stabilization in defect evolution. The time scale of the cascade process caused by PKA occurs at the femtosecond to picosecond level, which is difficult to observe experimentally.

[0003] However, at present, when simulating and calculating the radiation defects of semiconductor devices, the input information about the incident particle flow is usually only the number of particles and particle energy. According to the experimental results, the incident particle fluence has a very important influence on the evolution of radiation defects of semiconductor devices. In the simulation of Geant4, it is impossible to set the fluence of the incident particle flow, so a new method is needed to reflect the influence of fluence on the evolution of semiconductor device defects. Summary of the invention

[0004] The present invention solves the problem that the injection effect cannot be reflected in the simulation of defect evolution of semiconductor devices.

[0005] In order to solve the above problems, the present invention provides a method for simulating the molecular dynamics simulation of device irradiation defect evolution, comprising:

[0006] Step 1, obtaining the number of PKAs generated by the device irradiated by a convergent single incident particle;

[0007] Step 2, gridding the device to obtain a plurality of grids containing PKA, and counting the PKA information in the grids;

[0008] Step 3, constructing a system model of the same size as the grid;

[0009] Step 4, the incident particles with the calibrated fluence irradiate the device in batches according to the set fixed fluence rate, and obtain the number of PKAs generated by the incident particles in each batch in the grid and the total amount of PKAs generated by the incident particles with the calibrated fluence according to the number of PKAs generated by the device irradiated by the converged single incident particle, the PKA information in the grid, the area of ​​the grid, the calibrated fluence and the fixed fluence rate, and based on the system model, use molecular dynamics method and Kinetic Monte Carlo method to perform defect evolution on the PKA in the grid;

[0010] Step 5, counting the types and quantities of defects in each of the grids after the defect evolution and classifying them into the device, so as to obtain defect information in the device under the condition of the incident particles with a calibrated fluence;

[0011] Step six, changing the fluence of the incident particles, repeating steps four and five, and obtaining the relationship between the incident particles with different fluences and the defect information of the device.

[0012] Preferably, in step 4, the amount of PKA generated by each batch of the incident particles in the grid is According to the following formula;

[0013]

[0014] Among them, n i is the number of PKAs generated in the ith grid, and T is the time taken for the incident particles of the calibrated fluence to irradiate the device at the fixed fluence rate;

[0015] The time T for the incident particles of the calibrated fluence to irradiate the device at the fixed fluence rate is obtained according to the following formula:

[0016]

[0017] Wherein, Flu is the calibrated fluence of the incident particles, and FR is the fixed fluence rate.

[0018] Preferably, the time interval between adjacent batches of incident particles irradiating to produce PKA is Δt, wherein:

[0019]

[0020] Preferably, the step 4 further comprises: judging the structural state of the grid when the next batch of PKA is produced according to Δt;

[0021] When the Δt is within the time scale of the molecular dynamics, the defect evolution is performed on the PKA generated by each batch of the incident particles using a molecular dynamics method;

[0022] When Δt exceeds the time scale of the molecular dynamics, then within the Δt time, the PKA generated by each batch of the incident particles first uses the molecular dynamics method to perform the defect evolution. When it evolves to the stable state of molecular dynamics, the Kinetic Monte Carlo method is used to perform the defect evolution, and the defect evolution output structure of the PKA generated by the previous batch of incident particles serves as the defect evolution initial structure of the PKA generated by the next batch of incident particles.

[0023] Preferably, the step of obtaining the number of PKAs generated by the convergent single incident particle irradiation device comprises:

[0024] The number of PKAs generated by irradiating the device with different numbers of incident particles is obtained. When the number of PKAs generated by irradiating the device with incident particles tends to a convergent value, the ratio of the convergent value of the PKA number to the corresponding number of incident particles is obtained to obtain the number of PKAs generated by a converged single incident particle.

[0025] Preferably, obtaining the amount of PKA generated when different numbers of incident particles irradiate the device respectively comprises: using a Monte Carlo method to obtain PKA information generated when different numbers of incident particles irradiate the device.

[0026] Preferably, in step 5, counting the types and quantities of defects in each of the grids and classifying them into the device comprises:

[0027] Classifying the grids according to the PKA information contained in each of the grids, and extracting grids of the same category in proportion to obtain a calculation grid;

[0028] The types of defects and the number of each type of defects in the calculation grid are obtained, and they are summed up in proportion to obtain the defect types and the total amount of each type of defects in all the grids, and the total amount of each type of defects is divided by the total volume of the semiconductor device to obtain the types of defects in the semiconductor device and the concentration of each type of defects.

[0029] Preferably, the PKA information includes the quantity, energy, position and irradiation direction of the PKA. Preferably, the establishment of the simulation system model includes: establishing a pure silicon model in LAMMPS, and introducing doping elements into the pure silicon model, wherein the doping elements include at least one of C, O, B and P elements.

[0030] The advantages of the device irradiation defect evolution molecular dynamics simulation method of the present invention over the prior art are:

[0031] During the space irradiation process, all parameters of the incident particle flow will have an important impact on the evolution of irradiation defects of semiconductor devices. In the prior art, the number and energy of the incident particles are more concerned. The simulation method of the present invention can consider the impact of the fluence rate of the incident particles on the evolution of irradiation defects, thereby solving the problem that the fluence rate effect cannot be reflected in the simulation of defect evolution of semiconductor devices. The simulation method combines molecular dynamics and dynamic Monte Carlo methods to realize the simulation calculation of the defect evolution process of the entire semiconductor device under different fluences. The calculation logic is clear, the steps are simple and easy to operate, and the method is extremely consistent with the actual situation throughout the simulation process, and the simulation results are close to the experimental data.

[0032] The present invention also provides a fluence simulation system for device irradiation defect evolution molecular dynamics simulation, comprising:

[0033] An acquisition module, the acquisition module is used to acquire the amount of PKA generated by the convergent single incident particle irradiation device;

[0034] The acquisition module is also used to acquire PKA information in the grid;

[0035] The acquisition module is also used to acquire the number of PKAs generated by the incident particles in each batch in the grid and the total amount of PKAs generated by the incident particles with the calibrated fluence according to the number of PKAs generated by the converged single incident particle irradiation device, the PKA information in the grid, the area of ​​the grid, the calibrated fluence and the fixed fluence rate;

[0036] The acquisition module is also used to acquire the relationship between the incident particles with different fluences and the defect information of the device;

[0037] A device partitioning module, the device partitioning module is used to perform grid processing on the device to obtain a plurality of grids containing PKA;

[0038] A module construction module, wherein the module construction module is used to construct a system model of the same size as the grid;

[0039] A calculation module, wherein the calculation module is used to perform defect evolution on the PKA in the grid using a molecular dynamics method and a Kinetic Monte Carlo method;

[0040] A statistical module is used to count the types and quantities of defects in each of the grids after the defects evolve and classify them into the device.

[0041] The advantages of the fluence simulation system for molecular dynamics simulation of device irradiation defect evolution of the present invention over the prior art are the same as the fluence simulation method for molecular dynamics simulation of device irradiation defect evolution, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for simulating the amount of radiation in a device by molecular dynamics simulation of defect evolution under irradiation according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the system model structure constructed in an embodiment of the present invention;

[0044] Figure 3 Graph showing defect evolution calculation results at different incident particle injection rates in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] During the process of incident particle irradiation of the device, the incident particles collide with the lattice atoms in the device material to produce primary collision particles (PKA), which trigger cascade collisions and finally stabilize after high-temperature annealing, thereby generating defects. In this process, the parameters of the incident particles have an important influence on the defects, such as the energy and number of the incident particles. The present invention mainly examines the influence of the number of incident particles (hereinafter referred to as the fluence) on the defects.

[0046] It should be noted that when examining the fluence effect, a fixed fluence rate needs to be set. The so-called fluence rate refers to the number of particles irradiated each time, and the fluence is the total amount of irradiated particles. The fluence is equal to the fluence rate multiplied by the time. For example, if 10 incident particles irradiate the device within 10 seconds, the fluence of the incident particles is 10, and the fluence rate is 1 / s.

[0047] It should also be noted that in order to examine the effect of the injection amount on device defects, the implementation method of the present invention is to assume that PKA is produced in batches, or the incident particles irradiate the device in batches, so that PKA is produced in batches, and each batch of incident particle irradiation produces corresponding PKA. For example, the first batch of incident particles irradiating the device will produce the first batch of PKA, the second batch of incident particles irradiating the device will produce the second batch of PKA, and so on.

[0048] Therefore, when a certain number of incident particles irradiate the device, they are irradiated in batches according to a fixed fluence rate. For example, assuming the fluence rate is 3ion / s, that is, 3 particles are irradiated per second, or 3 particles are irradiated per batch. If the particle fluence is 15ion, the particles under this fluence are irradiated in five times, or PKA is produced in five times. By changing the particle fluence and keeping the fluence rate unchanged, the particle irradiation time changes accordingly, or the batches produced by PKA change accordingly. Therefore, in the entire simulation process of the present invention, there is only one variable, fluence, and the other parameters are set values, so as to examine the impact of incident particles with different fluences on device defects.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] See also Figure 1 As shown, a method for simulating the molecular dynamics simulation of device irradiation defect evolution according to an embodiment of the present invention includes:

[0051] Step 1, obtaining the number of PKAs generated by the device irradiated by a convergent single incident particle;

[0052] Step 2, gridding the device to obtain a plurality of grids containing PKA, and counting the PKA information in the grids;

[0053] Step 3, constructing a system model of the same size as the grid;

[0054] Step 4, the incident particles with the calibrated fluence irradiate the device in batches according to the set fixed fluence rate, and obtain the number of PKAs generated by the incident particles in each batch in the grid and the total amount of PKAs generated by the incident particles with the calibrated fluence according to the number of PKAs generated by the device irradiated by the converged single incident particle, the PKA information in the grid, the area of ​​the grid, the calibrated fluence and the fixed fluence rate, and based on the system model, use molecular dynamics method and Kinetic Monte Carlo method to perform defect evolution on the PKA in the grid;

[0055] Step 5, counting the types and quantities of defects in each of the grids after the defect evolution and classifying them into the device, and obtaining the types of defects in the device under the condition of the incident particles with a calibrated fluence and information on each type of defect;

[0056] Step six, changing the fluence of the incident particles, repeating steps four and five, and obtaining the relationship between the incident particles with different fluences and the defect information of the device.

[0057] This embodiment provides a method for simulating the fluence effect based on molecular dynamics and Kinetic Monte Carlo method to reflect the influence of fluence on the evolution of device defects. The simulation method of this embodiment is applicable to various semiconductor devices.

[0058] Specifically, by meshing the device, a grid (box) containing PKA is obtained to facilitate subsequent simulation calculations. After the system model is established, the defect evolution calculation of the box containing PKA is performed by combining the molecular dynamics method and the Kinetic Monte Carlo (KMC) method under the calibrated injection. This embodiment realizes the simulation calculation of the defect evolution of the entire device with different calibrated injections. During the entire simulation process, whether it is the irradiation source information, the device system reduction degree, or the injection value, it is very close to the experimental situation, and the calculation results are also relatively consistent with the experimental data.

[0059] In some embodiments, the number of PKAs generated by the device irradiated by a single incident particle that converges in step 1 is obtained as follows: the number of PKAs generated by the device irradiated by different numbers of incident particles is obtained, and when the number of PKAs generated by the device irradiated by the incident particles tends to a convergent value, the ratio of the convergence value of the number of PKAs to the corresponding number of incident particles is obtained to obtain the number of PKAs generated by the converged single incident particle.

[0060] The number of PKAs generated by a convergent single incident particle refers to the total number of PKAs generated by irradiation for incident particles of the same energy and type, T pka The ratio to the total number of incident particles will not change significantly as the number of incident particles increases. Since the Monte Carlo method is equivalent to single-particle calculation regardless of the number of incident particles, this embodiment uses a convergent normalized value.

[0061] Specifically, for a certain type of incident particle, the Monte Carlo method is used to calculate the number of PKAs generated by the incident particles at a certain energy. In order to ensure the accuracy of the calculation, the calculation is performed separately for different numbers of incident particles. For example, the number of incident particles is continuously increased during the calculation process until the number of PKAs generated by a certain number of incident particles tends to a convergence value, and then normalization is performed, that is, the total number of PKAs tending to the convergence value is divided by the corresponding number of incident particles, that is, the number of PKAs generated by a converged single incident particle is obtained.

[0062] In some embodiments, the device is meshed in step 2. To make the mesh density in the semiconductor device moderate and facilitate subsequent calculations, the mesh box size is set to 70nm*70nm*70nm. After the meshing is completed, the mesh boxes without PKA are cleared, and the mesh boxes containing PKA are retained. The PKA information contained in each box after the incident particle enters the semiconductor device is counted, including the number, energy, position and incident direction of PKA.

[0063] In some implementations, establishing the simulation system model in step three includes:

[0064] A pure silicon model is established in LAMMPS, and the Si unit cell is expanded by appropriate multiples in the x, y and z directions to form a silicon structure equal to the grid size. In this embodiment, since the grid size is 70nm*70nm*70nm, the Si unit cell is expanded 129 times in the x, y and z directions to form a 70nm*70nm*70nm Si structure.

[0065] Preferably, in order to ensure the fit between the simulation model and the actual situation, doping elements are introduced into the pure silicon model. Specifically, various existing doping elements, such as C, O, B and P, are introduced into the pure Si system according to the actual doping method and doping concentration. Figure 2 The figure shows a schematic diagram of a Si architecture with the same size as the box and considering real doping. C and O elements are doped into Si as interstitial atoms with a doping rate of 10 12 / cm 3 —10 14 / cm 3 , B and P elements are doped into Si in substitutional atom form, with a doping rate of 10 18 / cm 3 —10 20 / cm 3 .

[0066] In some embodiments, in step 4, the defect evolution of PKA generated by each batch of incident particle irradiation is first performed by molecular dynamics method and then by Kinetic Monte Carlo method, and the initial structure of defect evolution of PKA generated by the next batch of incident particle irradiation device is the output structure of defect evolution of PKA generated by the previous batch of incident particle irradiation device. And according to the number of PKA generated by the converged single incident particle irradiation device, the calibrated fluence of the incident particles and the area of ​​the grid, the number of PKA generated by each batch of incident particle irradiation in the grid and the total amount of PKA generated by the incident particle irradiation of the calibrated fluence in the grid are obtained.

[0067] Specifically, according to the PKA value generated by a single incident particle calculated in step 1, combined with the calibrated fluence, fixed fluence rate and box area, the total number of PKAs generated in the simulation and the number of PKAs generated in each batch can be obtained. For each batch of PKA, the defect evolution is first performed using molecular dynamics and then using the Kinetic Monte Carlo method. The defect structure after the evolution of the previous batch of PKA is used as the initial structure for the defect evolution process of the next batch of PKA until all PKAs are simulated.

[0068] The method for achieving the fluence in this embodiment is to divide all PKA into multiple batches according to the number of PKAs, and the time interval Δt between the generation of two adjacent batches of PKAs is the same. Assuming that the fixed fluence rate is FR and the fluence is Flu, the time required to complete the fluence particles under the fixed fluence rate is T,

[0069]

[0070] T is the time taken for the incident particles of the calibrated fluence to irradiate the device at the fixed fluence rate, where T is in seconds. Since the incident particles are irradiated in batches at a fixed fluence rate, T here can also be understood as the number of batches. For example, when the fixed fluence rate is 3 ion / s and the incident particle fluence is 15 ion, since 3 particles are irradiated in each batch, the number of batches is 5, i.e., the irradiation time.

[0071] Let the number of PKA generated in the i-th grid be n i , which is the PKA information in the grid obtained from the statistics in step 2. For this grid, the number of PKAs generated by each batch or each second of incident particle irradiation at this fluence is for:

[0072]

[0073] Furthermore, the time interval between adjacent batches of incident particles irradiating to produce PKA is Δt, where:

[0074]

[0075] The structural state of the next batch of PKA when it falls is determined based on Δt. Generally speaking, the time scale of molecular dynamics is mostly in the order of picoseconds. If Δt is within this order of magnitude, the evolution results of the molecular dynamics method need to be used to continue the calculation; if Δt is in the order of nanoseconds or even seconds, it is necessary to use the molecular dynamics method to evolve to a state first, and then evolve the structure to the Δt moment under the Kinetic MonteCarlo method, and then return the structure evolved by the Kinetic MonteCarlo method to the molecular dynamics method to calculate the defect evolution process of the second batch of PKA. If the number of PKA batches increases, the above process can be repeated.

[0076] In some implementations, due to computing power reasons, it may not be possible to calculate all boxes in step five. Only boxes containing the same number of PKAs can be sampled proportionally and statistically calculated. After the proportional sum is divided by the total volume of the device, the defect types of the entire device and the concentration of each defect can be obtained.

[0077] Specifically, the types and quantities of defects in each grid are counted and classified into devices including:

[0078] Classifying the grids according to the information of PKA contained in each of the grids, and classifying the grids according to the information of PKA contained in each of the grids;

[0079] Extract the grids of the same category in proportion to obtain the computational grid;

[0080] The defect types and the number of each type of defects in the calculation grid are obtained, and they are summed up in proportion to obtain the defect types and the total amount of each type of defects in all grids, and the total amount of each type of defects is divided by the total volume of the semiconductor device to obtain the defect types and the concentration of each type of defects in the semiconductor device.

[0081] like Figure 3 As shown, it shows the defect concentration produced after the device is irradiated with incident particles of different injection amounts ( Figure 3 ) It can be seen that the number of defects generated under different injection doses is different. Therefore, this embodiment realizes the simulation calculation of defect evolution under different injection doses for the entire semiconductor device by combining Monte Carlo, molecular dynamics and Kinetic Monte Carlo. By simulating the injection effect, the influence of injection on the defect evolution of semiconductor devices can be understood.

[0082] A fluence simulation system for molecular dynamics simulation of device irradiation defect evolution according to another embodiment of the present invention includes:

[0083] An acquisition module, the acquisition module is used to acquire the number of PKAs generated by the converged single incident particle irradiation device;

[0084] A device partitioning module is used to perform grid processing on the device to obtain multiple grids containing PKA;

[0085] Modular building module, which is used to build a system model of the same size as the grid;

[0086] A calculation module is used to perform defect evolution of PKA generated by incident particle irradiation of each batch in the grid using molecular dynamics method and Kinetic Monte Carlo method;

[0087] Statistics module,The statistics module is used to count the types and quantities of defects in each grid and classify them into devices.

[0088] Although the disclosure is disclosed as above, the protection scope of the disclosure is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for simulating the molecular dynamics of device irradiation defect evolution, characterized in that: include: Step 1, obtaining the number of PKAs generated by the device irradiated by a convergent single incident particle; Step 2, gridding the device to obtain a plurality of grids containing PKA, and counting the PKA information in the grids; Step 3, constructing a system model of the same size as the grid; Step 4, the incident particles with the calibrated fluence irradiate the device in batches according to the set fixed fluence rate, and obtain the number of PKAs generated by the incident particles in each batch in the grid and the total amount of PKAs generated by the incident particles with the calibrated fluence according to the number of PKAs generated by the device irradiated by the converged single incident particle, the PKA information in the grid, the area of ​​the grid, the calibrated fluence and the fixed fluence rate, and based on the system model, use molecular dynamics method and Kinetic Monte Carlo method to perform defect evolution on the PKA in the grid; Step 5, counting the types and quantities of defects in each of the grids after the defect evolution and classifying them into the device, so as to obtain defect information in the device under the condition of the incident particles with a calibrated fluence; Step six, changing the fluence of the incident particles, repeating steps four and five, and obtaining the relationship between the incident particles with different fluences and the defect information of the device.

2. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 1, characterized in that: In step 4, the amount of PKA generated by each batch of incident particles in the grid According to the following formula; Among them, n i is the number of PKAs generated in the ith grid, and T is the time taken for the incident particles of the calibrated fluence to irradiate the device at the fixed fluence rate; The time T for the incident particles of the calibrated fluence to irradiate the device at the fixed fluence rate is obtained according to the following formula: Wherein, Flu is the calibrated fluence of the incident particles, and FR is the fixed fluence rate.

3. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 2, characterized in that: The time interval between adjacent batches of incident particles irradiating to produce PKA is Δt, where:

4. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 3, characterized in that: The step 4 also includes: Determining the structural state of the grid when the next batch of PKA is produced according to Δt; When the Δt is within the time scale of the molecular dynamics, the defect evolution is performed on the PKA generated by each batch of the incident particles using a molecular dynamics method; When Δt exceeds the time scale of the molecular dynamics, then within the Δt time, the PKA generated by each batch of the incident particles first uses the molecular dynamics method to perform the defect evolution. When it evolves to the stable state of molecular dynamics, the Kinetic Monte Carlo method is used to perform the defect evolution, and the defect evolution output structure of the PKA generated by the previous batch of incident particles serves as the defect evolution initial structure of the PKA generated by the next batch of incident particles.

5. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 1, characterized in that: The number of PKAs generated by the single incident particle irradiation device for obtaining convergence includes: Obtaining the amount of PKA generated by irradiating the device with different numbers of incident particles; When the number of PKAs generated by incident particles irradiating the device tends to a convergent value, the ratio of the convergent value of the PKA number to the corresponding number of incident particles is obtained to obtain the convergent number of PKAs generated by a single incident particle.

6. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 5, characterized in that: The obtaining of the PKA quantity generated by irradiating the device with different numbers of incident particles respectively comprises: obtaining PKA information generated by irradiating the device with different numbers of incident particles using a Monte Carlo method.

7. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 1, characterized in that: In the step 5, counting the types and quantities of defects in each of the grids and classifying them into the device includes: Classifying the grids according to the PKA information contained in each of the grids, and extracting grids of the same category in proportion to obtain a calculation grid; The types of defects and the number of each type of defects in the calculation grid are obtained, and they are summed up in proportion to obtain the defect types and the total amount of each type of defects in all the grids, and the total amount of each type of defects is divided by the total volume of the semiconductor device to obtain the types of defects in the semiconductor device and the concentration of each type of defects.

8. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 7, characterized in that: The PKA information includes the quantity, energy, position and irradiation direction of the PKA.

9. The method for simulating the molecular dynamics simulation of device irradiation defect evolution according to claim 1, characterized in that: The establishment of the simulation system model includes: establishing a pure silicon model in LAMMPS, and introducing doping elements into the pure silicon model, wherein the doping elements include at least one of C, O, B and P elements.

10. A fluence simulation system for molecular dynamics simulation of device irradiation defect evolution, characterized in that: include: An acquisition module, the acquisition module is used to acquire the amount of PKA generated by the convergent single incident particle irradiation device; The acquisition module is also used to acquire PKA information in the grid; The acquisition module is also used to acquire the number of PKAs generated by the incident particles in each batch in the grid and the total amount of PKAs generated by the incident particles with the calibrated fluence according to the number of PKAs generated by the converged single incident particle irradiation device, the PKA information in the grid, the area of ​​the grid, the calibrated fluence and the fixed fluence rate; The acquisition module is also used to acquire the relationship between the incident particles with different fluences and the defect information of the device; A device partitioning module, the device partitioning module is used to perform grid processing on the device to obtain a plurality of grids containing PKA; A module construction module, wherein the module construction module is used to construct a system model of the same size as the grid; A calculation module, wherein the calculation module is used to perform defect evolution on the PKA in the grid using a molecular dynamics method and a Kinetic Monte Carlo method; A statistical module is used to count the types and quantities of defects in each of the grids after the defects evolve and classify them into the device.

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