Identification and statistical methods of different types of vacancy defects in materials

Abstract

The present invention provides a method for identifying and counting different types of vacancy defects in a material. It belongs to the field of simulation technology. The method includes establishing a system model corresponding to the material; setting simulation parameters, and using molecular dynamics methods to perform defect evolution simulation calculations on the system model; obtaining the defect evolution process until each system structure after the defect evolution is completed, and obtaining the vacancy defects in the system structure and their coordinate information; using cluster analysis to divide the vacancy defects into different types of vacancy clusters; counting the number of vacancy clusters of the same type, and according to the information of the vacancy clusters in each system structure, obtaining the relationship between the evolution of different types of vacancy defects over time. The present invention can intuitively and accurately characterize the existence of various types of vacancy defects in semiconductor materials after irradiation, and the 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 method for identifying and counting different types of vacancy defects in a material. Background Art

[0002] When semiconductor electronic devices are exposed to space for extended periods, the semiconductor materials within them are damaged by radiation from charged particles in space. The fundamental mechanisms by which radiation damages materials and components are ionization damage and displacement damage. Defects caused by displacement damage are primarily lattice vacancy defects generated in semiconductors through elastic collisions caused by high-energy, high-mass particle streams. The generation and formation mechanism of displacement defects can be roughly described as follows: high-energy particle streams impact the material's lattice atoms, causing them to gain energy and overcome their dislocation threshold energy to become primary collision atoms (PKAs). The PKAs then collide with other lattice atoms in the material, causing them to dislocate. This cycle continues, resulting in a cascade of collisions that ultimately stabilizes after high-temperature annealing.

[0003] The PKA-induced cascade process occurs on a femtosecond to picosecond timescale, making it difficult to observe experimentally. However, this timescale is within the range that can be handled by molecular dynamics (MD) simulations. Therefore, molecular dynamics can be used to simulate the PKA cascade process and count the number of defects in the steady-state structure. This allows for quantitative analysis of how the number of defects changes over time during the defect evolution process in semiconductor devices. However, using only the number of defects in the steady-state structure to characterize defect information in the structure of a semiconductor device after irradiation is too simplistic and fails to consider the impact of different defect types on the device, resulting in low accuracy in current simulation methods. Summary of the Invention

[0004] The problem solved by the present invention is that currently, only the number of defects in the steady-state structure obtained by molecular dynamics simulation is used to characterize the defect information in the structure of a semiconductor device after irradiation, which is relatively simple and does not consider the impact of factors such as the defect type on the device, resulting in low simulation accuracy.

[0005] To solve the above problems, the present invention provides a method for identifying and counting different types of vacancy defects in a material, comprising:

[0006] Step S1, establishing a system model corresponding to the material;

[0007] Step S2, setting simulation parameters, and performing defect evolution simulation calculation on the system model using molecular dynamics method;

[0008] Step S3, obtaining each system architecture during the defect evolution process until the defect evolution is completed, and obtaining vacancy defects and coordinate information in the system architecture;

[0009] Step S4, dividing the vacancy defects into different types of vacancy clusters using a cluster analysis method;

[0010] Step S5 , counting the number of vacancy clusters of the same type, and obtaining the relationship between the evolution of different types of vacancy defects over time based on the information of the vacancy clusters in each system architecture.

[0011] Preferably, obtaining vacancy defects and coordinate information thereof in the system architecture includes:

[0012] The system architecture is compared with a reference configuration to obtain coordinate information of defects in the system architecture, and the type of the defect is analyzed using a Wigner-Seitz defect analysis method to obtain coordinate information of the vacancy defect in the system architecture.

[0013] Preferably, the method of dividing the vacancy defects into different types of vacancy clusters using a cluster analysis method comprises:

[0014] The vacancy defects within the cutoff radius are divided into a vacancy cluster, the number of vacancy defects in the vacancy cluster is counted, and the vacancy clusters are divided into different types according to the number of vacancy defects in the vacancy cluster to obtain different types of vacancy clusters.

[0015] Preferably, dividing the vacancy defects within the cutoff radius into a vacancy cluster comprises:

[0016] When there are no other vacancy defects within the cutoff radius of the calibrated vacancy defect, the calibrated vacancy defect is a single vacancy defect;

[0017] When the distance between two adjacent vacancy defects is less than or equal to the cutoff radius, the two adjacent vacancy defects are divided into a divacancy cluster;

[0018] When there are n other vacancy defects within the cutoff radius of any vacancy defect in the divacancy cluster, the n+2 vacancy defects are divided into n+2 vacancy clusters;

[0019] When there are m other vacancy defects within the cutoff radius of any vacancy defect in the n+2 vacancy cluster, the n+2+m vacancy defects are divided into n+2+m vacancy clusters, where n≥1 and m≥1;

[0020] This method is carried out sequentially until no other vacancy defects exist within the cutoff radius of any vacancy defect in the calibrated vacancy cluster, and the type of the calibrated vacancy cluster is determined according to the number of vacancy defects in the calibrated vacancy cluster.

[0021] Preferably, the establishing of the system model corresponding to the material includes: meshing the irradiated device to obtain simulation boxes, acquiring PKA information in each of the simulation boxes, and establishing a system model of the same size as the simulation box.

[0022] Preferably, the simulation parameters include the PKA information, ensemble, simulation step size and evolution time.

[0023] Preferably, the defect evolution process includes a primary evolution stage, a secondary evolution stage and an ultimate evolution stage, and the simulation step is a variable step, wherein the time step of the primary evolution stage is 0.01fs, the time step of the secondary evolution stage is 0.1fs, and the time step of the ultimate evolution stage is 1fs.

[0024] Preferably, the evolution time is 20 ps.

[0025] Preferably, the ensemble is an NVE ensemble, and the temperature of the simulation system is controlled using a TTM model.

[0026] Preferably, before performing the defect evolution simulation calculation, the method further includes: minimizing and relaxing the system energy by a conjugate gradient method.

[0027] The advantages of the present invention over the prior art are:

[0028] This paper uses molecular dynamics to simulate the evolution of defects in a material after particle irradiation of a device. By capturing each architecture during the evolution process and identifying and counting defects within each architecture, the paper identifies the types of vacancy defects generated by the irradiation and the temporal evolution of different types of vacancy defects throughout the entire evolution process. Compared to existing methods that only capture the number of defects in the steady-state structure after evolution is complete, this paper examines the evolution of defects in the architecture throughout the entire evolution process, as well as the evolution of different types of vacancy defects. This results in a more accurate simulation. Throughout the simulation, both the irradiation source information and the degree of device system reduction are close to those observed in the experiment, and the calculated results closely align with the experimental data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Flowchart of a method for identifying and counting different types of vacancy defects in a material according to an embodiment of the present invention;

[0030] Figure 2 Schematic diagram of vacancy defect distribution in the irradiated structure according to an embodiment of the present invention;

[0031] Figure 3 for Figure 2 Schematic diagram of the single vacancy defect structure;

[0032] Figure 4for Figure 2 Schematic diagram of the divacancy defect structure;

[0033] Figure 5 This is the evolution of single vacancy defects and double vacancy defects over time in the embodiment of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] See also Figure 1 As shown, a method for identifying and counting different types of vacancy defects in a material according to an embodiment of the present invention includes:

[0036] Step S1, establishing a system model corresponding to the material;

[0037] Step S2, setting simulation parameters, and performing defect evolution simulation calculation on the system model using molecular dynamics method;

[0038] Step S3, obtaining each system architecture during the defect evolution process until the defect evolution is completed, and obtaining vacancy defects and coordinate information in the system architecture;

[0039] Step S4, dividing the vacancy defects into different types of vacancy clusters using a cluster analysis method;

[0040] Step S5 , counting the number of vacancy clusters of the same type, and obtaining the relationship between the evolution of different types of vacancy defects over time based on the information of the vacancy clusters in each system architecture.

[0041] This embodiment uses molecular dynamics methods to simulate the evolution of defects in the material after incident particle irradiation of the device. By obtaining each system structure during the evolution process and identifying and counting the defects in each system structure, where the defects include vacancy defects and interstitial atomic defects, the types of vacancy defects generated by the irradiation and the evolution of different types of vacancy defects over time during the entire evolution process are obtained. Compared to the existing technology that only obtains the number of defects in the steady-state structure after the evolution is completed, this embodiment not only examines the evolution of defects in the system structure during the entire evolution process, but also examines the evolution of different types of vacancy defects. This simulation is more accurate. Throughout the simulation process, the irradiation source information and the degree of device system reduction are close to the experimental conditions, and the calculated results are relatively consistent with the experimental data.

[0042] The method of this embodiment enables intuitive and accurate identification and counting of different types of vacancy defects in semiconductor devices. The method has clear logic, simple steps, and is easy to use. The method closely matches actual conditions throughout the simulation process, and the simulation results can be compared with experimental data. This method has significant advantages and broad prospects in the design and application research of various semiconductor devices.

[0043] According to the present invention, a system model corresponding to the material is first established. This involves meshing the irradiated device, setting up simulation boxes of appropriate size based on the simulation scenario, obtaining the PKA information within each simulation box, and establishing a system model of the same size as the simulation box. The PKA information includes parameters such as PKA energy and type. For example, in LAMMPS, the Si unit cell can be expanded by a factor of 50 in each of the x, y, and z directions to create a Si structure of the same size as the box for subsequent simulations.

[0044] In some of the implementation methods, the PKA information in the box is used as an input parameter, and simulation parameters such as the ensemble, simulation step size, and evolution time are set. The incident velocity of PKA is given by energy, and random numbers are used to determine the angles between the PKA velocity and the three directions of x, y, and z. The NVE (system particle number, volume, and energy remain unchanged) ensemble is selected for simulation, the TTM model is used to control the temperature of the simulation system, and the appropriate force field is selected according to the different corresponding systems of the semiconductor device, and LAMMPS is used to perform defect evolution simulation calculations.

[0045] In addition, after the PKA information in the box is input as a parameter, the system energy is first minimized and relaxed, and then the MD defect evolution calculation is performed. Preferably, the system energy is minimized and relaxed using the conjugate gradient method.

[0046] The defect evolution process consists of a primary, secondary, and final evolution phase. A variable time step is used to visualize the defect evolution process: 0.01 fs for the primary phase, 0.1 fs for the secondary phase, and 1 fs for the final phase. This variable time step allows for capturing both peak and plateau values ​​of the defect count while saving computational time. The three phases take a total of approximately 20 ps to reach the stable state of the structure on the MD timescale. During the calculation, parameters such as the coordinates, kinetic energy, and displacement of each atom in the output evolution region are set to facilitate observation of the evolution process.

[0047] After the molecular dynamics simulation, the defect evolution during the simulation calculation process is statistically analyzed. The vacancy defects and their coordinate information in the system structure during the evolution process are obtained, such as Figure 2The figure shows the distribution of vacancy defects in a certain architecture obtained during the evolution process. The vacancy defects are then distinguished and classified, and the number of different types of vacancy defects is further obtained, for example, Figure 3 The single vacancy defect structure shown and Figure 4 The double vacancy defect structure shown in Figure 3 and Figure 4 The darker colored areas represent vacancy defects. We then perform statistical differentiation on the entire evolution process to obtain the temporal evolution of various defects.

[0048] In some implementations, obtaining vacancy defects and coordinate information in the architecture includes:

[0049] The system architecture is compared with the reference configuration to obtain the coordinate information of defects in the system architecture. The defect type is analyzed using the Wigner-Seitz defect analysis method to obtain the coordinate information of vacancy defects in the system architecture. The reference configuration refers to the intact structure before irradiation, that is, the system model in step S1. However, in actual operation, the structure of the system model after relaxation during the MD simulation calculation is usually used as the reference configuration.

[0050] After obtaining information about all vacancy defects, such as the xyz coordinates of the vacancy defects, by comparing with the reference configuration, the vacancy defects are classified into different types of vacancy clusters using cluster analysis, including:

[0051] After obtaining information on all vacancy defects, a cutoff radius is set. Generally, the cutoff radius is set to the length of a bond. Vacancy defects within the cutoff radius are then divided into vacancy clusters. The number of vacancy defects within the vacancy clusters is counted, and the vacancy clusters are classified into different types based on the number of vacancy defects within the clusters. This yields different types of vacancy clusters.

[0052] In one embodiment, dividing the vacancy defects within the cutoff radius into a vacancy cluster includes:

[0053] When the distance between two adjacent vacancy defects is less than or equal to the cutoff radius, the two adjacent vacancy defects are divided into a divacancy cluster;

[0054] When there are n other vacancy defects within the cutoff radius of any vacancy defect in a divacancy cluster, n+2 vacancy defects are divided into n+2 vacancy clusters, where n≥1; for example, if n is 1, 3 vacancy defects are divided into a trivacancy cluster;

[0055] When there are m other vacancy defects within the cutoff radius of any vacancy defect in an n+2 vacancy cluster, the n+2+m vacancy defects are divided into n+2+m vacancy clusters, where m≥1; for example, if n is 1 and m is 2, then the five vacancy defects are divided into five vacancy defects;

[0056] The above method is carried out sequentially until no other vacancy defects exist within the cutoff radius of any vacancy defect in the calibrated vacancy cluster, and the type of the calibrated vacancy cluster is determined directly according to the number of vacancy defects in the calibrated vacancy cluster.

[0057] For example, if the cutoff radius is set to the length of one bond length, then as long as the distance between two adjacent vacancy defects satisfies the length of one bond length, they will be regarded as a vacancy cluster, and the number of vacancy defects in the vacancy cluster will be counted. For example, if there are only two vacancy defects in the vacancy cluster, then this vacancy cluster can be called a divacancy cluster. If there are three vacancy defects in the vacancy cluster, then the vacancy cluster is called a trivacancy cluster, and so on. In this way, the vacancy defects in a certain system structure during the evolution process are classified into divacancy clusters, trivacancy clusters, tetravacancy clusters, etc. Of course, if there are no other vacancy defects within the cutoff radius of the calibrated vacancy defect, then the calibrated vacancy defect is called a single vacancy defect.

[0058] After dividing the vacancy defects in a certain system structure during the evolution process into different types of vacancy clusters in the above manner, the number of each type of vacancy clusters is counted. For example, the number of divacancy clusters is a, the number of trivacancy clusters is b, and so on, to obtain the number of different types of vacancy clusters in the system structure.

[0059] Using the cluster analysis method, the vacancy defects in a certain system structure during the evolution process are divided into different types of vacancy clusters and their number is counted. Then, the vacancy clusters in each system structure during the entire evolution process are classified and counted. In this way, the relationship between the number of different types of vacancy clusters and time can be obtained.

[0060] Since the present embodiment uses the type of vacancy cluster to characterize the type of vacancy defect, for example, a divacancy cluster represents the type of defect as a divacancy defect, the relationship between the number of different types of vacancy clusters and time can be obtained to determine the relationship between the evolution of different types of vacancy defects over time. Figure 5 As shown in the figure, the evolution of single vacancy defects and double vacancy defects over time during the entire evolution process. It can be seen that as the evolution time progresses, the number of single vacancy defects and double vacancy defects first increases and then decreases. The increase mainly occurs in the PKA cascade collision stage. With the high-temperature annealing in the later stage and tending to the stable stage, the vacancies will undergo a certain recombination, so the vacancy defects will be reduced. Figure 5It can also be seen that the number of single vacancy defects is greater than that of divacancy defects, and the arrival of the peak of single vacancy defects is later than that of divacancy defects.

[0061] Therefore, according to the method of this embodiment, the presence of various types of vacancy defects in the semiconductor material after irradiation can be intuitively and accurately characterized, providing a basis for the development of semiconductor device materials.

[0062] Although the present disclosure is disclosed as above, the protection scope of the present 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 present disclosure, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for identifying and counting different types of vacancy defects in a material, characterized in that: include: Step S1, establishing a system model corresponding to the material; Step S2, setting simulation parameters, and performing defect evolution simulation calculation on the system model using molecular dynamics method; Step S3, obtaining each system architecture during the defect evolution process until the defect evolution is completed, and obtaining vacancy defects and coordinate information in the system architecture; Step S4, using a cluster analysis method to divide the vacancy defects into different types of vacancy clusters, including: dividing the vacancy defects located within a cutoff radius into one vacancy cluster, counting the number of vacancy defects in the vacancy cluster, and dividing the vacancy clusters into different types according to the number of vacancy defects in the vacancy cluster to obtain different types of vacancy clusters, wherein the dividing the vacancy defects located within the cutoff radius into one vacancy cluster includes: when there are no other vacancy defects within the cutoff radius of the calibrated vacancy defect, the calibrated vacancy defect is a single vacancy defect; when the distance between two adjacent vacancy defects is less than or equal to the cutoff radius, the two adjacent vacancy defects are separated by a single vacancy cluster. The vacancy defects are divided into divacancy clusters; when there are other n vacancy defects within the cutoff radius of any vacancy defect in the divacancy cluster, the n+2 vacancy defects are divided into n+2 vacancy clusters; when there are other m vacancy defects within the cutoff radius of any vacancy defect in the n+2 vacancy cluster, the n+2+m vacancy defects are divided into n+2+m vacancy clusters, where n≥1 and m≥1; this method is carried out sequentially until there are no other vacancy defects within the cutoff radius of any vacancy defect in the calibrated vacancy cluster, and the type of the calibrated vacancy cluster is determined according to the number of vacancy defects in the calibrated vacancy cluster; Step S5 , counting the number of vacancy clusters of the same type, and obtaining the relationship between the evolution of different types of vacancy defects over time based on the information of the vacancy clusters in each system architecture.

2. The method for identifying and counting different types of vacancy defects in a material according to claim 1, characterized in that: The obtaining of vacancy defects and coordinate information thereof in the system architecture includes: The system architecture is compared with a reference configuration to obtain coordinate information of defects in the system architecture, and the type of the defect is analyzed using a Wigner-Seitz defect analysis method to obtain coordinate information of the vacancy defect in the system architecture.

3. The method for identifying and counting different types of vacancy defects in a material according to claim 1, characterized in that: The establishment of a system model corresponding to the material includes: meshing the irradiated device to obtain simulation boxes, acquiring PKA information in each of the simulation boxes, and establishing a system model of the same size as the simulation box.

4. The method for identifying and counting different types of vacancy defects in a material according to claim 3, characterized in that: The simulation parameters include the PKA information, ensemble, simulation step size and evolution time.

5. The method for identifying and counting different types of vacancy defects in a material according to claim 4, characterized in that: The defect evolution process includes a primary evolution stage, a secondary evolution stage and an ultimate evolution stage. The simulation step is a variable step, wherein the time step of the primary evolution stage is 0.01fs, the time step of the secondary evolution stage is 0.1fs, and the time step of the ultimate evolution stage is 1fs.

6. The method for identifying and counting different types of vacancy defects in a material according to claim 5, characterized in that: The evolution time is 20 ps.

7. The method for identifying and counting different types of vacancy defects in a material according to claim 4, characterized in that: The ensemble is an NVE ensemble, and the temperature of the simulation system is controlled using the TTM model.

8. The method for identifying and counting different types of vacancy defects in a material according to claim 1, characterized in that: Before performing the defect evolution simulation calculation, the method further includes: minimizing the system energy and relaxing the system energy by using a conjugate gradient method.

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