Simulation method and system for irradiation defect evolution in semiconductor devices

Abstract

The present invention provides a simulation method and system for the evolution of radiation defects in semiconductor devices, belonging to the field of simulation technology. The method includes: obtaining PKA information generated by incident particle radiation; meshing the semiconductor device and counting the PKA information in the grid; establishing a system model of the same size as the grid; using the PKA information in the grid as an input parameter, simulating the defect evolution in the LAMMPS software, and obtaining a steady-state structure under the MD time scale; using the defect information in the steady-state structure as an input file of KMC, and outputting the final steady-state structure; counting the defect information in the final steady-state structure, and summarizing the defect information in all grids to obtain the comprehensive defect information of the semiconductor device. The present invention realizes the spatiotemporal cross-scale simulation calculation of the entire process of radiation defect generation and evolution in semiconductor devices based on MD and KMC, and the calculation method has clear logic, simple steps and is easy to operate.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a simulation method and system for simulating the evolution of radiation defects in semiconductor devices. Background Art

[0002] Semiconductor electronic devices are exposed to charged ion radiation when in a space environment. Especially under the action of high-energy particle irradiation, semiconductor materials will suffer severe ionization and displacement damage, which will lead to degradation or even failure of the device's optoelectronic performance, causing immeasurable damage to the entire electronic system. High-energy incident particles collide with atoms in the crystal to produce primary collision particles (PKA), which interact with lattice atoms in the material to produce a displacement cascade, causing irradiation damage to the material. Material irradiation damage varies from the atomic scale (nanometers, picoseconds) to the length and time scales of structural components (meters, years), where the length scale spans more than 10 orders of magnitude and the time scale spans more than 22 orders of magnitude. Therefore, it is difficult to accurately describe the entire process of defect evolution in semiconductor devices. Summary of the invention

[0003] The problem solved by the present invention is to provide a convenient and accurate method for simulating the entire process of the generation and evolution of radiation defects in semiconductor devices.

[0004] In order to solve the above problems, the present invention provides a simulation method for the evolution of radiation defects in a semiconductor device, comprising:

[0005] Step S1, obtaining information of PKA generated after incident particles irradiate the semiconductor device;

[0006] Step S2, meshing the semiconductor device, obtaining a mesh containing the PKA, and counting the PKA information in the mesh;

[0007] Step S3, establishing a system model of the same size as the grid;

[0008] Step S4, based on the system model, using the PKA information in the grid as an input parameter, performing defect evolution simulation in LAMMPS software to obtain a steady-state structure under the molecular dynamics time scale;

[0009] Step S5, obtaining defect information in the steady-state structure, and using the defect information in the steady-state structure as an input file of a kinetic Monte Carlo method, continuing to perform defect evolution simulation, and outputting a final steady-state structure;

[0010] Step S6, counting the defect information in the final stable state structure, and summarizing the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

[0011] Preferably, the obtaining of the information of PKA generated after the incident particles radiate the semiconductor device comprises: obtaining the information of PKA generated after the incident particles radiate the semiconductor device using a Monte Carlo method.

[0012] Preferably, the information of the PKA includes the quantity of the PKA and its distribution in the semiconductor device.

[0013] Preferably, the PKA information in the grid includes the quantity, energy, position and incident direction of the PKA.

[0014] Preferably, the establishing of a system model of the same size as the grid comprises: establishing a pure silicon model of the same size as the grid, and introducing doping elements into the pure silicon model, wherein the doping elements comprise at least one of C, O, B and P elements.

[0015] Preferably, the C and O elements are doped into the pure silicon model in the form of interstitial atoms, with a doping concentration of 10 12 / cm 3 —10 14 / cm 3 The B and P elements are doped into the pure silicon model in the form of substitutional atoms, with a doping concentration of 10 18 / cm 3 —10 20 / cm 3 .

[0016] Preferably, the defect evolution simulation performed in the LAMMPS software includes a molecular dynamics primary evolution stage, a molecular dynamics secondary evolution stage and a molecular dynamics ultimate evolution stage, and the time steps of the molecular dynamics primary evolution stage, the molecular dynamics secondary evolution stage and the molecular dynamics ultimate evolution stage are different.

[0017] Preferably, after step S2, the method further includes: classifying the grids according to the PKA information in each grid, extracting grids of the same category at a preset ratio to obtain sampling grids, and establishing a system model of the same size as the sampling grid.

[0018] Preferably, the summarizing the defect information in all grids includes:

[0019] The types of defects and the number of each type of defects in the sampling grid are obtained, and they are summed up in proportion to obtain the defect types and the total number of each type of defects in all the grids, and the total number 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.

[0020] The advantages of the present invention compared to the prior art are:

[0021] The present invention realizes the spatiotemporal cross-scale simulation calculation of the whole process of radiation defect generation and evolution in semiconductor devices based on molecular dynamics method and dynamic Monte Carlo method. In the whole simulation process, the radiation source information and the degree of restoration of the device system are very close to the experimental conditions, the calculation results are also relatively consistent with the experimental data, and the calculation method has clear logic, simple steps and is easy to operate.

[0022] The present invention also provides a defect evolution simulation system for a device irradiated with particles of different energies, comprising:

[0023] An acquisition module, the acquisition module is used to acquire PKA information generated after incident particles irradiate the semiconductor device, and the acquisition module is also used to acquire PKA information in the grid;

[0024] A grid division module, the grid division module is used to perform grid division on the semiconductor device to obtain a grid containing the PKA;

[0025] A model building module, the model building module is used to build a system model of the same size as the grid;

[0026] A simulation calculation module, wherein the simulation calculation module is used to use the PKA information in the grid as an input parameter, perform defect evolution simulation in the LAMMPS software, obtain a steady-state structure under the molecular dynamics time scale, and use the defect information in the steady-state structure as an input file of the dynamic Monte Carlo method, continue to perform defect evolution simulation, and output a final steady-state structure;

[0027] A statistical module is used to collect statistics on defect information in the final stable state structure and to summarize the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

[0028] The advantages of the defect evolution simulation system of the device irradiated with particles of different energies of the present invention over the prior art are the same as the simulation method of irradiation defect evolution in semiconductor devices, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of a simulation method for the evolution of radiation defects in a semiconductor device according to an embodiment of the present invention;

[0030] Figure 2 Schematic diagram of the spatiotemporal cross-scale process of irradiation defect evolution in a semiconductor device according to an embodiment of the present invention;

[0031] Figure 3 The schematic diagram of the PKA distribution and grid division generated after the incident particles irradiate the device in the embodiment of the present invention;

[0032] Figure 4 This is a full-time domain defect evolution diagram after the incident particles irradiate the device in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] See also Figure 1 As shown, a simulation method for the evolution of radiation defects in a semiconductor device according to an embodiment of the present invention includes:

[0035] Step S1, obtaining information of PKA generated after incident particles irradiate the semiconductor device;

[0036] Step S2, meshing the semiconductor device, obtaining a mesh containing the PKA, and counting the PKA information in the mesh;

[0037] Step S3, establishing a system model of the same size as the grid;

[0038] Step S4, based on the system model, using the PKA information in the grid as an input parameter, performing defect evolution simulation in LAMMPS software to obtain a steady-state structure under the molecular dynamics time scale;

[0039] Step S5, obtaining defect information in the steady-state structure, and using the defect information in the steady-state structure as an input file of a kinetic Monte Carlo method (KMC) to continue defect evolution simulation, and outputting a final steady-state structure.

[0040] Step S6, counting the defect information in the final stable state structure, and summarizing the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

[0041] like Figure 2 As shown, Figure 2 a represents the irradiated semiconductor device (hereinafter also referred to as the device), Figure 2 b indicates that PKA is generated in the device after the incident particles irradiate the device, where Figure 2 The box in b represents the device, and the dot with an arrow in the box represents PKA. Figure 2 a to Figure 2 b is the process of incident particles irradiating the device to produce PKA, and it is also the process of interaction between the incident particles and matter.

[0042] Figure 2 c indicates that PKA cascades into collisions in the device to produce displacement defects, where Figure 2The box in c represents the device, and the box shows point defects and cascade defects generated by PKA cascade. Figure 2 b to Figure 2 c is the process of PKA cascade collision producing displacement defects.

[0043] Depend on Figure 2 c to Figure 2 d is the defect evolution process, Figure 2 d shows the stable defects formed in the device after defect evolution and healing.

[0044] According to the simulation method of the present invention, step S1 and step S2 are first performed, specifically:

[0045] When incident particles irradiate the device, the particles enter the interior of the material and generate PKA. In a preferred embodiment, the information of PKA is calculated by the Monte Carlo method. The information of PKA includes the energy of PKA and the distribution of PKA in the device.

[0046] According to the distribution of the generated PKA in the device, the device is meshed. After the meshing is completed, the mesh boxes without PKA are cleared and the mesh boxes containing PKA are retained to obtain the mesh containing PKA (such as Figure 3 As shown in FIG. 1 , the PKA information contained in each grid is counted, that is, the number, energy, position and incident direction of the PKA contained in each grid. Exemplarily, the grid (box) size is set to 70nm*70nm*70nm to make the grid density in the semiconductor device moderate.

[0047] According to the present invention, step S3 is then performed to establish a system model of the same size as the grid. Specifically, a pure Si system model is established in LAMMPS, and the Si unit cell is expanded by appropriate multiples in the three directions of x, y, and z to establish a Si structure of the same size as the box for subsequent simulation processes. Exemplarily, the Si unit cell is expanded by 129 times in the three directions of x, y, and z to obtain a pure Si model of 70nm*70nm*70nm.

[0048] Preferably, according to actual conditions, particles are doped into the pure Si model, for example, trace amounts of C, O or doped atoms such as B and P are doped into the pure Si model to ensure the fit between the simulation model and the actual model. Among them, C and O elements are doped into Si in the form of interstitial atoms, with a doping concentration of 10 12 / cm 3 —10 14 / cm 3 ; B and P elements are doped into Si in substitutional atom form, with a doping concentration of 10 18 / cm 3 —10 20 / cm3 .

[0049] According to the present invention, step S4 and step S5 are then performed. Specifically, the PKA information in the grid is input as an input parameter into LAMMPS to simulate the microscopic damage process of the cascade collision of PKA in the material. After simulation calculation, the steady-state structure under the molecular dynamics time scale is obtained. The steady-state structure is as follows: Figure 2 c shows the statistical information of various defects in the steady-state structure. The defects in the steady-state structure are displacement defects caused by PKA cascade collisions.

[0050] Preferably, after the PKA information in the box is input as a parameter, the system energy is minimized and relaxed first, and then the MD defect evolution calculation is performed. The MD defect evolution calculation is divided into the primary evolution stage, the secondary evolution stage and the ultimate evolution stage. The time steps taken in the calculation of the above three stages are different. The primary evolution stage is 0.01fs, the secondary evolution stage is 0.1fs, and the ultimate evolution stage is 1fs. The total evolution time of the three stages is about 20ps to reach the stable state of the structure under the MD time scale. During the calculation process, the coordinates, kinetic energy, displacement and other parameters of each atom in the output evolution area should be set to facilitate the observation of the evolution process.

[0051] Then the defect information in the steady-state structure is used as the input file of the KMC calculation to simulate the defect evolution on a larger time scale. After the evolution, the structure reaches the final steady state. The output final steady-state structure is as follows: Figure 2 As shown in (d), the defects in the final stable state structure are stable defects formed by the evolution and healing of the cascade defects generated by PKA.

[0052] In the process of KMC, the box size used in KMC simulation is the same as that in MD, but the length unit of KMC data file is nanometer (nm), while the length unit of MD data file is angstrom ( ), the conversion relationship is 1nm = . According to this conversion relationship, the conversion process is performed to obtain the KMC data. In the calculation of KMC, it is necessary to set the capture radius, pre-exponential factor, migration barrier, iteration steps of various defects and the interaction between various defects. During the calculation process, it is necessary to set the output parameters such as the number and coordinates of various defects to facilitate the observation of the evolution process and statistical results.

[0053] Finally, step S6 is performed to count the defect information in the final stable state structure, such as the defect type and the number of each type of defect, and then the defect information in all grids is summarized to obtain the defect information of the entire semiconductor device after the MD and KMC simulation evolution.

[0054] Preferably, summarizing the defect information in all grids includes: classifying the grids according to the PKA information contained in each grid, and then extracting grids of the same category at a preset ratio, such as sampling boxes containing the same number of PKAs in proportion to obtain sampling grids, and obtaining defect information in the sampling grids, such as the defect type and the number of defects in each category, and then summing the defect information in the sampling grids at a preset ratio to obtain the defect information in all grids, namely the defect type and the total number of defects in each category, and then dividing the total number of defects in each category by the total volume of the device to obtain comprehensive information on defects in the device, such as the defect type and the concentration of each defect category.

[0055] like Figure 4 As shown, Figure 4 A graph showing the changing relationship between the number of different types of defects and time, wherein the defect types include vacancy defects and self-interstitial defects, vacancy defects such as single vacancy defects, double vacancy defects, triple vacancy defects, and quadruple vacancy defects, and self-interstitial defects such as single self-interstitial defects and double self-interstitial defects.

[0056] This embodiment realizes a time-space cross-scale simulation calculation method for the entire process of radiation defect generation and evolution in semiconductor devices. During the entire simulation process, the radiation source information and the device system restoration degree are very close to the experimental situation, and the calculation results are also relatively consistent with the experimental data.

[0057] This embodiment implements a time-space cross-scale simulation calculation method for the entire process of radiation defect generation and evolution in semiconductor devices. The calculation method has clear logic, simple steps and is easy to operate. The method is extremely consistent with the actual situation throughout the simulation process, and the simulation results can be compared with experimental data. It has obvious advantages and broad prospects in the design and application research of different semiconductor devices.

[0058] A defect evolution simulation system for a device irradiated with particles of different incident energies according to another embodiment of the present invention includes:

[0059] An acquisition module, the acquisition module is used to acquire information of PKA generated after incident particles irradiate the semiconductor device, and the acquisition module is also used to acquire PKA information in the grid;

[0060] A grid division module, the grid division module is used to perform grid division on the semiconductor device to obtain a grid containing the PKA;

[0061] A model building module, the model building module is used to establish a system model of the same size as the grid;

[0062] A simulation calculation module, wherein the simulation calculation module is used to use the PKA information in the grid as an input parameter, perform defect evolution simulation in the LAMMPS software, obtain a steady-state structure under the molecular dynamics time scale, and use the defect information in the steady-state structure as an input file of the dynamic Monte Carlo method, continue to perform defect evolution simulation, and output a final steady-state structure;

[0063] A statistical module is used to collect statistics on defect information in the final stable state structure and to summarize the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

[0064] 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 evolution of radiation defects in a semiconductor device, characterized in that: include: Step S1, obtaining information of PKA generated after incident particles irradiate the semiconductor device; Step S2, meshing the semiconductor device, obtaining a mesh containing the PKA, and counting the PKA information in the mesh; Step S3, establishing a system model of the same size as the grid; The establishing of a system model having the same size as the grid comprises: establishing a pure silicon model having the same size as the grid, and introducing doping elements into the pure silicon model, wherein the doping elements include at least one of C, O, B and P elements; Step S4, based on the system model, using the PKA information in the grid as an input parameter, performing defect evolution simulation in LAMMPS software to obtain a steady-state structure under a molecular dynamics time scale; Step S5, obtaining defect information in the steady-state structure, and using the defect information in the steady-state structure as an input file of a kinetic Monte Carlo method, continuing to perform defect evolution simulation, and outputting a final steady-state structure; Step S6, counting the defect information in the final stable state structure, and summarizing the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

2. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 1, characterized in that: The obtaining of information on PKA generated after incident particles radiate the semiconductor device comprises: obtaining information on PKA generated after the incident particles radiate the semiconductor device using a Monte Carlo method.

3. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 2, characterized in that: The information of the PKA includes the quantity of the PKA and its distribution in the semiconductor device.

4. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 1, characterized in that: The PKA information in the grid includes the quantity, energy, position and incident direction of the PKA.

5. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 1, characterized in that: The C and O elements are doped into the pure silicon model in the form of interstitial atoms, with a doping concentration of 10 12 / cm 3 —10 14 / cm 3 The B and P elements are doped into the pure silicon model in the form of substitutional atoms, with a doping concentration of 10 18 / cm 3 —10 20 / cm 3 .

6. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 1, characterized in that: The defect evolution simulation in the LAMMPS software includes a molecular dynamics primary evolution stage, a molecular dynamics secondary evolution stage and a molecular dynamics ultimate evolution stage, and the time steps of the molecular dynamics primary evolution stage, the molecular dynamics secondary evolution stage and the molecular dynamics ultimate evolution stage are different.

7. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 1, characterized in that: After step S2, the method further includes: classifying the grids according to the PKA information in each grid, extracting grids of the same category according to a preset ratio to obtain sampling grids, and establishing a system model of the same size as the sampling grid.

8. The method for simulating the evolution of radiation defects in a semiconductor device according to claim 7, characterized in that: The summarizing of defect information in all grids includes: The types of defects and the number of each type of defects in the sampling grid are obtained, and they are summed up in proportion to obtain the defect types and the total number of each type of defects in all the grids, and the total number 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.

9. A defect evolution simulation system for devices irradiated by particles with different energies, characterized in that: include: An acquisition module, the acquisition module is used to acquire PKA information generated after incident particles irradiate the semiconductor device, and the acquisition module is also used to acquire PKA information in the grid; A grid division module, the grid division module is used to perform grid division on the semiconductor device to obtain a grid containing the PKA; A model building module, the model building module is used to build a system model of the same size as the grid; The establishing of a system model having the same size as the grid comprises: establishing a pure silicon model having the same size as the grid, and introducing doping elements into the pure silicon model, wherein the doping elements include at least one of C, O, B and P elements; A simulation calculation module, wherein the simulation calculation module is used to use the PKA information in the grid as an input parameter, perform defect evolution simulation in the LAMMPS software, obtain a steady-state structure under the molecular dynamics time scale, and use the defect information in the steady-state structure as an input file of the dynamic Monte Carlo method, continue to perform defect evolution simulation, and output a final steady-state structure; A statistical module is used to collect statistics on defect information in the final stable state structure and to summarize the defect information in all grids to obtain comprehensive defect information of the semiconductor device.

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