Ion implantation simulation method, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention provides an ion implantation simulation method, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining a grid cell corresponding to an injection position by utilizing grid cell data of a device to be injected; determining, based on lattice constants at the determined grid cells, locations of atoms in a plurality of unit cells, the plurality of unit cells including a central unit cell at the injection location and adjacent unit cells within a predetermined range around the central unit cell, the lattice constants relating to elemental components of the multi-component material at the determined grid cells; determining the types of atoms in the plurality of unit cells based on the element components of the multi-component material at the determined grid units; and generating a simulation result of ion implantation at the implantation position based on the positions and types of the atoms in the plurality of unit cells.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to a design technology of semiconductor and integrated circuit, and more particularly, to a method of simulating an ion implantation process, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] Ion implantation is an important step and link in the process of semiconductor and integrated circuit manufacturing, which can implant charged ions with certain energy into semiconductor materials. By precisely implanting accelerated high-energy charged ion beams into the base of crystalline materials, the spatial distribution of doping elements can be controlled and adjusted. In this way, the electrical properties of semiconductor materials can be changed in a precise and controllable manner, thereby helping integrated circuits to achieve complex functions.

[0003] In order to optimize semiconductor processes such as ion implantation, computer aided design technology (TCAD) can be used to simulate physical processes such as ion implantation, diffusion and oxidation, thereby providing result prediction for real processes. TCAD helps to improve the process parameters of semiconductor manufacturing processes, thereby reducing experimental and research and development costs. However, current TCAD performs poorly for some ion implantation scenarios. For example, for the scenario of ion implantation into multi-component materials, the current simulation process has poor accuracy and is difficult to provide effective prediction for real ion implantation. SUMMARY

[0004] Based on the above problems, according to an example embodiment of the present disclosure, a simulation method of ion implantation, an electronic device and a computer readable storage medium are provided.

[0005] In a first aspect of the present disclosure, a method of simulating an ion implantation process is provided, which includes: determining a grid cell corresponding to an implantation position by using grid cell data of a device to be implanted; determining positions of atoms in a plurality of unit cells based on a lattice constant at the determined grid cell, the plurality of unit cells including a central unit cell at the implantation position and adjacent unit cells within a predetermined range around the central unit cell, the lattice constant being related to element components of a multi-component material at the determined grid cell; determining types of the atoms in the plurality of unit cells based on the element components of the multi-component material at the determined grid cell; and generating a simulation result of ion implantation at the implantation position based on the positions and types of the atoms in the plurality of unit cells.

[0006] In a second aspect of the disclosure, an electronic device is provided, comprising: a processor; and a memory coupled with the processor, the memory having stored therein instructions that, when executed by the processor, cause the electronic device to perform acts comprising: determining, with grid cell data of a device to be implanted, a grid cell corresponding to an implant location; determining, based on a lattice constant at the determined grid cell, positions of atoms in a plurality of unit cells, the plurality of unit cells including a central unit cell at the implant location and neighboring unit cells within a predetermined range around the central unit cell, the lattice constant being related to elemental compositions of a multi-component material at the determined grid cell; determining, based on the elemental compositions of the multi-component material at the determined grid cell, types of the atoms in the plurality of unit cells; and generating, based on the positions and the types of the atoms in the plurality of unit cells, a simulation result of ion implantation at the implant location.

[0007] In some embodiments of the disclosure, the lattice constant at the determined grid cell is determined by at least one of: calculating the lattice constant based on elemental lattice constants of elements included in the multi-component material at the determined grid cell and the elemental compositions, and / or determining the lattice constant by looking up a table based on the elemental compositions of the elements included in the multi-component material at the determined grid cell.

[0008] In some embodiments of the disclosure, the elemental compositions include molar compositions of the elements.

[0009] In some embodiments of the disclosure, the acts further comprise: generating a mapping relationship between coordinate parameters of each grid cell in the device and grid information, the grid information including elemental compositions of the multi-component material at a corresponding grid cell and lattice constants determined based on the elemental compositions at the corresponding grid cell, and wherein the lattice constant and the elemental compositions at the determined grid cell are obtained based on a coordinate parameter of the determined grid cell and the mapping relationship.

[0010] In some embodiments of the disclosure, the mapping relationship includes a hash mapping.

[0011] In some embodiments of the disclosure, determining the positions of the atoms in the plurality of unit cells comprises: determining a position of the central unit cell based on a coordinate parameter of the implant location and the lattice constant at the determined grid cell; determining positions of the neighboring unit cells based on the position of the central unit cell and the predetermined range; and determining the positions of the atoms in the plurality of unit cells based on the positions of the central unit cell and the neighboring unit cells.

[0012] In some embodiments of the disclosure, determining the types of the atoms in the plurality of unit cells comprises: determining, by a probabilistic sampling manner, an atomic type of each lattice site in the plurality of unit cells based on the elemental compositions of the multi-component material at the determined grid cell.

[0013] In some embodiments of the present disclosure, generating the simulation result of ion implantation at the implantation position comprises: selecting atoms in the plurality of unit cells that are less than a predetermined distance from the implantation position; and calculating, based on positions and types of the selected atoms, a result of collision between the selected atoms and the implantation ions.

[0014] In a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to the first aspect is implemented.

[0015] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0016] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail the following embodiments with reference to the attached drawings. In the drawings, the same or similar reference numerals refer to the same or similar elements, and:

[0017] Figure 1 A schematic diagram of a scenario of an ion implantation process is shown, according to an embodiment of the present disclosure.

[0018] Figure 2 A schematic diagram of a scenario of an ion implantation process is shown, according to an embodiment of the present disclosure.

[0019] Figure 3 A schematic flowchart of a method of simulating an ion implantation process is shown, according to an embodiment of the present disclosure.

[0020] Figure 4A A schematic diagram of a mesh cell structure of a device is shown, according to an embodiment of the present disclosure.

[0021] Figure 4B A schematic diagram of a mesh cell structure representing a moiré component of a device is shown, according to an embodiment of the present disclosure.

[0022] Figure 5A 、 Figure 5B and Figure 5C A schematic diagram of determining positions of atoms in a plurality of unit cells is shown, according to an embodiment of the present disclosure.

[0023] Figure 5D A schematic diagram of a constructed single unit cell is shown, according to an embodiment of the present disclosure.

[0024] Figure 6A schematic diagram showing a process of generating ion implantation simulation results based on positions and types of atoms in a plurality of unit cells according to an embodiment of the present disclosure is shown.

[0025] Figure 7 A schematic flowchart showing a process of generating a mapping relationship between coordinate parameters of each mesh cell in a device and mesh information according to an embodiment of the present disclosure is shown.

[0026] Figure 8 A schematic flowchart showing a process of determining positions of atoms in a plurality of unit cells according to an embodiment of the present disclosure is shown.

[0027] Figure 9 A schematic flowchart showing a process of determining types of atoms in a plurality of unit cells according to an embodiment of the present disclosure is shown.

[0028] Figure 10 A schematic flowchart showing a process of generating simulation results of ion implantation at an implantation position according to an embodiment of the present disclosure is shown.

[0029] Figure 11 A schematic block diagram of an example device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure will be described in more detail with reference to the drawings. While certain embodiments of the present disclosure will be shown and described, it is to be understood that the present disclosure is not limited to the embodiments to be shown and described, but is to only be limited as provided in the claims. It is to be understood that the drawings and embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure.

[0031] In the description of embodiments of the present disclosure, the term "includes" and its derivatives are to be construed as open-ended, meaning "including, but not limited to." The term "based on" is to be construed as "based, at least in part, on." The term "one embodiment" or "an embodiment" are to be construed as "at least one embodiment." The terms "a" or "an" are to be construed as "one or more" unless otherwise indicated. Other definitions can be provided in the detailed description.

[0032] As mentioned above, TCAD can be used to simulate semiconductor process steps such as ion implantation, diffusion, and oxidation, thereby providing result predictions for the actual process. Ion implantation simulations can predict the distribution of ions in the material and lattice damage, thereby helping to optimize ion implantation process parameters, reduce experimental and R&D costs, and provide theoretical guidance for a deeper understanding of the physical process of ion implantation. Simulations of ion implantation currently show high accuracy and efficiency when dealing with single-component crystalline materials. However, the objects of ion implantation may also be multi-component materials. When dealing with multi-component materials, current simulation methods cannot accurately capture the interaction between ions and material atoms, and due to the use of overly simplified models, it is difficult to truly reflect the physical properties of the material, resulting in inaccurate simulation results.

[0033] The embodiments of the present disclosure provide an improved scheme for simulating the ion implantation process. In the improved scheme, the grid cells corresponding to the ion implantation positions in the device are determined, and the elemental composition and the lattice constants related to the elemental composition at the grid cells are obtained to determine the positions and types of atoms in multiple unit cells near the ion implantation positions, thereby generating simulation results of the ion implantation based on this. In this way, the crystal structure within the multi-component material can be simulated more accurately, thereby providing high-precision simulation and result prediction for the ion implantation of the multi-component material. As a result, the ion implantation process of the multi-component material can be effectively optimized based on accurate simulation, thereby reducing experimental and R&D costs.

[0034] Figure 1 FIG. 1 is a schematic diagram of a scene 100 of an ion implantation process according to an embodiment of the present disclosure. Figure 1 As shown, in scenario 100, device 110 is implanted with ions 120. As an example, device 110 may be a semiconductor device, such as a transistor, memory, or other type of device in the manufacturing process. By performing ion implantation, the electrical characteristics of device 110 may be changed to achieve desired performance. For example, in the case where device 110 is a transistor in the manufacturing process, specific regions such as a well region, a source region, or a drain region may be formed by performing ion implantation. Device 110 may include a multi-component material, such as Si x Ge 1-x 、Al x Ga 1-x As and Al x Ga 1-x N (where x is the mole fraction), etc.

[0035] Figure 2 FIG. 2 is a schematic diagram of a scene 200 of an ion implantation process according to an embodiment of the present disclosure. Figure 2 As shown, as an example, Figure 1The device 110 in the middle can have a three-dimensional crystal structure 210 (only a planar view structure is shown schematically in the figure), which has atoms 211 at each lattice site. When performing ion implantation, an implantation ion can be at an implantation location 220. It can be understood that, Figure 2 Only an example implementation of the crystal structure 210 is shown, but the specific structure of the crystal structure 210 is not limited thereto, but can be any other structure.

[0036] Figure 3 A schematic flowchart of a method 300 of simulating an ion implantation process according to an embodiment of the present disclosure is shown. The ion implantation process involved in the method 300 can be implemented in the example scenarios 100 and 200. In addition, the method 300 can be performed by a computing device. The computing device can be any device with computing capability. In one example, the computing device can be any type of fixed, mobile, or portable computing device, for example, the computing device includes but is not limited to a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a multimedia computer, a mobile phone, etc. In another example, all or part of the components of the computing device can be distributed in the cloud. Figure 1

[0037] At block 301, a grid cell corresponding to an implantation location is determined using grid cell data of a device 110 to be implanted.

[0038] As an example, the computing device simulating the ion implantation process can obtain or read the grid cell data of the device 110. For example, the grid cell data can be data of a three-dimensional finite element structure model generated by modeling the device 110, and includes node coordinates, element topological relationships, and the like. After determining the implantation location to be implanted, the grid cell corresponding to the implantation location can be determined based on the implantation location, for example, by determining the coordinate position of the implantation location in the model, the corresponding grid cell can be found.

[0039] At block 302, the positions of atoms in a plurality of unit cells are determined based on a lattice constant at the determined grid cell, the plurality of unit cells including a central unit cell at the implantation location and neighboring unit cells within a predetermined range around the central unit cell, the lattice constant being related to element components of a multi-component material at the determined grid cell.

[0040] ​The atoms in a crystal usually periodically repeat in a regular manner in three-dimensional space, and the crystal structure can be regarded as a stack of identical parallelepiped units, and such a smallest basic unit is called a unit cell. The lattice constant refers to a parameter describing the size of the unit cell, which can include length parameters (such as the lengths of the three edges of the unit cell) and angle parameters (such as the angles between the three edges of the unit cell). The element composition is the composition of various elements of the material of the device region corresponding to the grid cell, for example, Si x Ge 1-x where x represents the composition of the Si element, and 1-x represents the composition of the Ge element. The relevant data information of the device 110 can be stored on the grid cell or the grid node or can be associated with the corresponding grid cell. For example, information such as the lattice constant and the element composition at the region corresponding to the grid cell can be stored at the corresponding grid cell or grid node. Thus, after obtaining the grid cell data of the device, the required various information such as the material information of the grid cell, the element composition, the lattice constant, etc. can be obtained from the corresponding grid cell or grid node. In the case where the device 110 includes a multi-component material, different grid cells corresponding to different regions in the device 110 can have different lattice constants. In one example, the lattice constant at each grid cell can be determined in advance according to the element composition at the corresponding grid cell, and stored at the corresponding grid cell or associated with the corresponding grid cell. Thus, after determining the grid cell corresponding to the implantation position, the lattice constant corresponding thereto can be extracted according to the grid cell. In another example, after determining the ion implantation position and the grid cell, the information related to the element composition can be obtained from the grid cell and the lattice constant can be determined based on the information. That is, the lattice constant of the grid cell can be obtained in various ways as long as it is associated with the element composition of the grid cell. Thus, a more accurate lattice constant near the implantation position can be obtained. After determining the lattice constant, the lattice constant can be used to generate or determine the crystal structure near the implantation position, which can include a unit cell at the implantation position, called a central unit cell, and unit cells around the central unit cell, called neighboring unit cells. The central unit cell and the neighboring unit cells can form a set of N*N*N (N is a positive integer) unit cells. Each unit cell includes one or more lattice sites, and one atom can be arranged on each lattice site. The structure and lattice sites of these unit cells can be determined or constructed using the lattice constant, so as to determine the positions of the atoms in these unit cells.

[0041] In some embodiments of the present disclosure, the lattice constant can be calculated based on the element lattice constant and the element composition of the elements contained in the multi-component material at the determined grid cell. For example, if the multi-component material at the grid cell is Si x Ge1-x (x represents the component of Si element, and 1-x represents the component of Ge element), the lattice constant of the grid cell can be calculated by the following equation:

[0042] where a(Si x Ge 1-x ) represents the lattice constant of Si x Ge 1-x , a(Si) represents the lattice constant of Si element, and a(Ge) represents the lattice constant of Ge element. In an embodiment, the element component includes the molar component of the element. That is, x in equation (1) can represent the molar component of Si element, and 1-x can represent the molar component of Ge element. The calculation of equation (1) utilizes the Vegard law, which can accurately calculate the lattice constant of the material at the grid cell in the case of the material being a multi-component material, thereby reflecting the lattice change law in the actual material.

[0043] In some embodiments of the present disclosure, the lattice constant can be determined by table lookup based on the element component of the elements contained in the multi-component material at the determined grid cell. For example, the lattice constant of the multi-component material under different element components can be measured in an experimental manner, and a table can be made according to the relationship between the element component and the lattice constant in the multi-component material. Thus, after the element component of the multi-component material is determined, the required lattice constant can be obtained by looking up the lattice constant corresponding to the element component in the table. In an embodiment, the element component includes the molar component of the element. That is, the table can be formed by utilizing the relationship between the molar component of the element and the lattice constant, and the corresponding lattice constant can be obtained by table lookup based on the actual molar component of the element. For example, if the multi-component material at the grid cell is Si x Ge 1-x , and x represents the molar component of Si element, and 1-x represents the molar component of Ge, the experimental measurement value of the lattice constant can be obtained by table lookup using x as an index.

[0044] Since the lattice constant obtained by experimental measurement has relatively higher accuracy, in the case that the lattice constant can be determined by both calculation and table lookup, the table lookup is more preferred to determine the lattice constant. However, it can be understood that either one of calculation and table lookup or a combination of both can be used to determine the lattice constant, and the embodiments of the present disclosure do not limit this.

[0045] At block 303, based on the element component of the multi-component material at the determined grid cell, the type of atom in the plurality of unit cells is determined.

[0046] For example, as mentioned above, the elemental composition or material information associated therewith can be pre-stored in the grid cells or grid nodes, or stored in an associated manner with the grid cells or grid nodes. Thus, upon determining the grid cell corresponding to the implantation location, the elemental composition and other material information associated with the grid cell can be obtained. Further, the distribution of different element atoms can be estimated based on the elemental composition, so as to determine the type of atoms at each lattice site within the plurality of unit cells. In this way, the type of atoms within the plurality of unit cells can be determined more accurately, so as to improve the accuracy of the constructed crystal structure and its atomic density.

[0047] At block 304, a simulation result of ion implantation at the implantation location is generated based on the positions and types of atoms in the plurality of unit cells.

[0048] After determining the positions and types of atoms in the plurality of unit cells near the ion implantation location, the crystal structure near the ion implantation location of the device 110 is determined. Thus, the ion implantation process can be simulated based on the determined crystal structure. For example, the Monte Carlo method or algorithm can be employed to simulate the ion implantation and achieve efficient and accurate calculation. The Monte Carlo method can randomly simulate the movement process of a large number of ions, so as to simulate the movement trajectory of the ions in the target device or target material, and thus predict the distribution of the ions in the material and the lattice damage. In the case where the device 110 is formed of or includes multi-component materials, the components of different regions can be different, resulting in different lattice constants and atomic densities from those of single-component materials. When the ions collide with atoms in the material where the components vary, the components of one region change from those of another region, so that the parameters such as the lattice constant and the atomic density change, which affects the collision results of the ions and the atoms of the material, and further can affect the ion distribution results after ion implantation. The conventional simulation method generally only considers the case of single-component materials and employs a simplified model that is difficult to reflect the physical characteristics of the material, resulting in poor simulation accuracy. By introducing the grid cell data and considering the elemental composition of the multi-component material and the lattice constant associated with the elemental composition, the positions and types of atoms that collide with the ions to be implanted can be determined more accurately, which enables the ion implantation process to be accurately simulated, so as to provide accurate prediction results. In addition, since the generation and determination of the crystal structure only involve a few unit cells near the ion implantation location, the amount of calculation can be controlled to a low level, so as to improve the simulation accuracy without excessively increasing the computing resources. After obtaining the prediction results of the ion implantation, the process parameters of the ion implantation can be optimized. For example, by adjusting the implantation energy, dose, angle, and other parameters, the ion concentration distribution such as the junction depth and lateral diffusion can be made to meet the device design requirements. In addition, by optimizing the process parameters, the lattice defect density (e.g., vacancies, interstitial atoms) can be reduced, and the annealing efficiency can be improved.

[0049] Figure 4A A schematic diagram of a mesh cell structure 400A of the device 110 is shown according to an embodiment of the present disclosure. As shown, the device 110 can be modeled to generate a three-dimensional model having the mesh cell structure 400A, and the mesh cell structure 400A can be composed of mesh cells 401 as basic units. As an example, a modeling software (e.g., a geometry modeling module) can be used to model the structure of the device 110 that needs to be ion implanted, and a mesh engine included in the software is utilized to construct the mesh cells of the structure. In one example, the mesh cells 401 can be tetrahedrons. Generally speaking, when modeling a device, the mesh cells of a two-dimensional structure can be chosen to be triangles, and the mesh cells of a three-dimensional structure can be chosen to be tetrahedrons. However, it can be appreciated that other shapes of mesh cells can also be employed, and embodiments of the present disclosure are not limited in this regard. Figure 4A

[0050] Figure 4B A schematic diagram of a mesh cell structure 400B of the device 110 representing the molar composition is shown according to an embodiment of the present disclosure. As shown, each mesh cell in the mesh cell structure 400B can store or associate material information related to the molar composition. In addition, each mesh cell in the mesh cell structure can also store or associate material identifiers and key physical parameters such as lattice constants, material densities, and any other relevant information. In this way, after obtaining the mesh cell data or the mesh cell structure 400B of the device 110, a computing device for performing ion implantation process simulation can easily obtain and determine the information and parameters related to each mesh cell. As a result, the crystal structure near the ion implantation location in a multi-component material can be more accurately determined so as to accurately simulate the ion implantation process. Figure 4B

[0051] In some embodiments of the present disclosure, prior to block 301, a mapping relationship can be generated between the coordinate parameters of each mesh cell in the device 110 and the grid information including the element composition of the multi-component material at the corresponding mesh cell and the lattice constant determined based on the element composition at the corresponding mesh cell. As a result, the lattice constant at the determined mesh cell of block 302 and the element composition at the determined mesh cell of block 303 can be obtained based on the coordinate parameters of the determined mesh cell and the mapping relationship.

[0052] ​​As an example, Cartesian coordinates can be used to establish a crystal coordinate system and determine the coordinates of each grid cell in the device 110 in the crystal coordinate system before the ion implantation locations are determined or before the simulation of the ion implantation process is started, so as to determine the correspondence between the grid cells and the crystal structure. In one example, the coordinate parameters of all grid cells in the device 110 can be determined in the crystal coordinate system. In another example, the coordinate parameters of the partial grid cells in the device that can need ion implantation can be determined in the crystal coordinate system. After the coordinate parameters of all or partial grid cells are determined, a mapping relationship can be established between the coordinate parameters of all or partial grid cells and the grid information. The grid information refers to the information related to the corresponding grid cell, such as the elemental composition of the material, the lattice constant, etc. When the grid information needs to include the lattice constant, the lattice constant of all or partial grid cells can be determined in advance based on the material information of the grid cells and stored in association with the grid cells. Alternatively, the grid information can also not include the lattice constant, and the lattice constant can be determined again after the ion implantation location and the corresponding grid cell are determined by means such as calculation and table lookup. By establishing such a mapping relationship, the data association and correspondence between the grid cells and the lattice information can be more effectively and accurately established, so that the lattice information related to the grid cell, such as the elemental composition and the lattice constant, can be determined more quickly after the ion implantation location and the grid cell are determined, thereby minimizing the time required to establish the crystal structure and simulate the ion implantation process. In one embodiment, the mapping relationship includes a hash mapping. As an example, the following shows the mapping relationship stored in the form of a hash mapping table:

[0053] wherein the material data 1, 2, 3 can be the elemental composition, the lattice constant, etc. information described above. The time complexity of the hash mapping is O(l), which means that even if the amount of input data continues to increase, the algorithm execution time can be maintained as a constant. Thus, the algorithm execution time does not change with the increase of the input size, which ensures that after the ion implantation location and the grid cell are determined, the information and parameters corresponding to the grid cell can be quickly determined using the mapping relationship, without affecting the processing speed due to the large amount of data. It can be understood that other mapping relationships (such as linear lookup table) other than hash mapping can also be used as long as the access speed can be effectively improved to a certain extent.

[0054] In addition, as described above, the grid cells in the grid cell data are associated with information such as material information. Therefore, without establishing the above-mentioned mapping relationship, it is also possible to determine and find information related to the grid cells after determining the ion implantation position and grid cell. However, this determination and search process takes a long time, so the additional step of establishing the mapping relationship is more preferred because it helps to quickly find the element composition and lattice constant corresponding to the determined grid cell from a large amount of data, thereby significantly improving the processing speed.

[0055] Figure 5A 、 Figure 5B and Figure 5C FIG. 2 shows a schematic diagram of determining the positions of atoms in a plurality of unit cells according to an embodiment of the present disclosure. Figure 5A 、 Figure 5B and Figure 5C As shown, the position of the central unit cell 511 can be determined based on the coordinate parameters of the ion implantation position 520 and the lattice constant at the determined grid unit, and the positions of the adjacent unit cells can be determined based on the position of the central unit cell 511 and a predetermined range, thereby determining the positions of the plurality of unit cells 510 including the central unit cell and the adjacent unit cells. After the positions of the plurality of unit cells 510 are determined, the positions of the atoms in the plurality of unit cells 510 can be determined.

[0056] As an example, it can be assumed that the coordinate origin is (0, 0, 0) and the ion implantation position is (Ix, Iy, Iz). If the lattice constant of the grid cell corresponding to the ion implantation position is determined to be La (i.e., the lengths of the three sides of the unit cell in the grid cell are all La), the position (i, j, k) of the central unit cell corresponding to the ion implantation position 520 can be calculated according to the following equation:

[0057] where i represents an index of the center unit in the x-axis direction of the coordinate system, j represents an index of the center unit in the y-axis direction of the coordinate system, k represents an index of the center unit in the z-axis direction of the coordinate system, and int is an integer function. Thus, the position (i, j, k) actually represents that the center unit is the i-th unit in the x-axis direction, the j-th unit in the y-axis direction, and the k-th unit in the z-axis direction. Thus, the specific position of the center unit relative to the coordinate origin (0, 0, 0) can be determined, and the specific positions of the adjacent units can be determined with the position of the center unit as the center. For example, the plurality of units composed of the center unit and the adjacent units can be a unit set of N*N*N (N is a positive integer, for example, 3). For example, in the case where the plurality of units is a unit set of 3*3*3 and one unit contains 8 atoms, 216 atom positions need to be determined. In this way, the structure of the plurality of units 510 can be constructed, thereby determining the distribution and position of the atoms within the plurality of units 510.

[0058] Figure 5D A schematic diagram of a constructed single unit according to an embodiment of the present disclosure is shown. In some embodiments, the atomic type of each lattice site of each unit in the plurality of units can be determined by probabilistic sampling based on the elemental components of the multi-component material at the determined grid cells. As an example, a Monte Carlo driven probabilistic lattice construction technique can be employed to dynamically generate atomic configurations based on the distribution of the material's local mole fraction. For example, if the multi-component material of the grid cell is Si x Ge 1-x , then the atomic type of each lattice site in the unit can be randomly assigned by a probabilistic sampling algorithm based on the mole fraction x, for example, the probability of assigning a Si atom P(Si) = x and a Ge atom P(Ge) = 1-x for each lattice site, and the unit will maintain the tetrahedral bonding topology and lattice symmetry of the diamond structure. As Figure 5D shown, by probabilistically determining the atomic type of each atomic position based on the elemental or mole fraction, the mixing and arrangement of atoms under different components in the real multi-component material can be effectively simulated, thereby improving the reality and accuracy of the simulation.

[0059] Figure 6A schematic diagram of generating ion implantation simulation results based on the positions and types of atoms in a plurality of unit cells is shown according to an embodiment of the present disclosure. In some embodiments of the present disclosure, atoms in the plurality of unit cells 510 that are less than a predetermined distance R from the implantation location 520 can be selected, and based on the positions and types of the selected atoms, the results of the selected atoms colliding with the implanted ions are calculated. Specifically, since the interatomic force continuously decays with distance, when the ion and the material atom distance reaches a certain extent, the force is approximately zero, and it can be considered that there is no interaction between the ion and the material atom. Based on this, instead of calculating the interaction of the ion with all the surrounding material atoms, only the interaction of the near-neighbor atoms within the predetermined distance R (also referred to as the cutoff radius) range with the implanted ion needs to be calculated. For example, the predetermined distance or cutoff radius R can be 1.5 times the lattice constant. In this way, the computational load of ion and atom collision calculation can be effectively reduced, thereby improving the calculation efficiency. In one embodiment, the step of determining the type of atom in block 303 can only determine the type of atom within the range of less than the predetermined distance R from the implantation location 520, without determining the type of all atoms within the plurality of unit cells 510. In this way, the computational load required for simulating the ion implantation process can be further reduced, the calculation efficiency can be improved, and the required calculation time and computing resources can be reduced. By calculating the collision of the ion with the atom after ion implantation, the energy loss, deflection angle, and other parameters of the ion and atom collision can be determined, thereby obtaining and updating the state of the ion (such as energy, velocity, and position, etc.) and the final stopping position after multiple collisions. After the final stopping positions of all ions are counted, the concentration distribution of the ions can be determined and output.

[0060] Through the embodiments of the present disclosure, in the process of simulating the ion implantation process, the component concentration of the multi-component material can be accurately reflected (which can resolve the component gradient change within the nanoscale), and the precise simulation of the multi-component material can be achieved. In addition, in the simulation process, only a small number of unit cells and atoms near the ion implantation location can be calculated, thereby being able to greatly improve the precision of simulating the ion implantation process of the multi-component material without requiring excessive computational load and calculation time.

[0061] Figure 7 A schematic flowchart of a process 700 of generating a mapping relationship between the coordinate parameters of each grid cell in the device 110 and the grid information according to an embodiment of the present disclosure is shown. The process 700 can be implemented before block 301 of the method 300.

[0062] At block 701, the coordinate parameters of each grid cell in the device 110 are determined.

[0063] At block 702, a mapping relationship is generated between the coordinate parameters of each mesh cell in the device 110 and mesh information, the mesh information including element components of the multi-component material at the corresponding mesh cell and a lattice constant determined based on the element components at the corresponding mesh cell. In some embodiments, the lattice constant and the element components at the determined mesh cell are obtained based on the coordinate parameters of the determined mesh cell and the mapping relationship. In some embodiments, the mapping relationship includes a hash mapping.

[0064] Figure 8 A schematic flowchart of a process 800 of determining positions of atoms in a plurality of unit cells is shown, according to an embodiment of the present disclosure. The process 800 can be implemented at block 302 of the method 300.

[0065] At block 801, a position of a central unit cell is determined based on the coordinate parameters of the implantation position and the lattice constant at the determined mesh cell.

[0066] At block 802, a position of a neighboring unit cell is determined based on the position of the central unit cell and a predetermined range.

[0067] At block 803, positions of atoms in the plurality of unit cells are determined based on the positions of the central unit cell and the neighboring unit cell.

[0068] Figure 9 A schematic flowchart of a process 900 of determining types of atoms in a plurality of unit cells is shown, according to an embodiment of the present disclosure. The process 900 can be implemented at block 303 of the method 300.

[0069] At block 901, element components of the multi-component material at the determined mesh cell are determined.

[0070] At block 902, types of atoms at each lattice site in the plurality of unit cells are determined by means of probabilistic sampling based on the element components of the multi-component material at the determined mesh cell.

[0071] Figure 10 A schematic flowchart of a process 1000 of generating simulation results of ion implantation at an implantation position is shown, according to an embodiment of the present disclosure. The process 1000 can be implemented at block 304 of the method 300.

[0072] At block 1001, atoms in the plurality of unit cells that are less than a predetermined distance R from the implantation position are selected.

[0073] At block 1002, results of collisions of the selected atoms with the implantation ions are calculated based on the positions and types of the selected atoms.

[0074] Figure 11A schematic block diagram of an example device 1100 that can be used to implement embodiments of the present disclosure is shown. The device 1100 can be used to implement Figure 3 the method 300, Figure 7 the process 700, Figure 8 the process 800, Figure 9 the process 900, and Figure 10 the process 1000.

[0075] As shown, the device 1100 includes a central processing unit (CPU) 1101 that can perform various suitable actions and processes in accordance with computer program instructions stored in a read-only memory (ROM) 1102 or computer program instructions loaded into a random access memory (RAM) 1103 from a storage unit 1108. Various programs and data required by the device 1100 for operation, e.g., the measurement data mentioned above, can also be stored in the RAM 1103. The CPU 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0076] A plurality of components in the device 1100 are connected to the I / O interface 1105, including an input unit 1106, e.g., a keyboard, a mouse, etc., an output unit 1107, e.g., various types of displays, speakers, etc., a storage unit 1108, e.g., a magnetic disk, a magneto-optical disk, etc., and a communication unit 1109, e.g., a network card, a modem, a wireless communication transceiver, etc. The communication unit 1109 allows the device 1100 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0077] The processing unit 1101 performs the method or process described above, the method 300. For example, in some embodiments, the method 300 can be implemented as a computer software program or computer program product that is tangibly embodied in a machine-readable medium, such as a non-transitory computer-readable medium, such as the storage unit 1108. In some embodiments, portions or all of the computer program can be loaded and / or installed onto the device 1100 via the ROM 1102 and / or the communication unit 1109. When the computer program is loaded onto the RAM 1103 and executed by the CPU 1101, one or more steps of the method 300 described above can be performed. Alternatively, in other embodiments, the CPU 1101 can be configured to perform the method 300 by any other suitable means, e.g., by means of firmware.

[0078] Those skilled in the art will understand that the various steps of the methods of the present disclosure described above can be implemented by general computing devices, which can be centralized on a single computing device or distributed on a network of multiple computing devices, and optionally, they can be implemented by program codes executable by computing devices, which can be stored in storage devices and executed by computing devices, or they can be implemented by individual integrated circuit modules, or a plurality of modules or steps among them can be implemented by a single integrated circuit module. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0079] It should be understood that, although several means or sub-means of the apparatus are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. Indeed, according to embodiments of the present disclosure, features and functions of two or more means described above can be embodied in one means. Conversely, features and functions of one means described above can be further divided into means embodied by multiple means.

[0080] The above description is merely optional embodiments of the present disclosure and is not intended to limit the present disclosure. The present disclosure can have various modifications and changes for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for simulating an ion implantation process, comprising: Determining a grid cell corresponding to an implantation location using grid cell data of a device to be implanted; determining positions of atoms in a plurality of unit cells, the plurality of unit cells including a central unit cell at the implantation location and neighboring unit cells within a predetermined range around the central unit cell, based on a lattice constant at the determined grid cell, the lattice constant being related to an elemental composition of the multi-component material at the determined grid cell; determining types of atoms in the plurality of unit cells based on the elemental composition of the multi-component material at the determined grid cells; as well as A simulation result of ion implantation at the implantation location is generated based on the positions and types of atoms in the plurality of unit cells.

2. The method according to claim 1 , wherein the lattice constant at the determined grid cell is determined by at least one of the following: Calculating the lattice constant based on the element lattice constants and element compositions of the elements contained in the multi-component material at the determined grid cells, and / or The lattice constant is determined by looking up a table based on the elemental composition of the elements contained in the multi-component material at the determined grid cell.

3. The method of claim 2, wherein the elemental composition comprises a molar composition of an element.

4. The method according to claim 1, further comprising: generating a mapping relationship between coordinate parameters of each grid cell in the device and grid information, the grid information including elemental compositions of the multi-component material at the corresponding grid cell and lattice constants determined based on the elemental compositions at the corresponding grid cell, and The lattice constant and element composition at the determined grid cell are acquired based on the coordinate parameters of the determined grid cell and the mapping relationship. The method according to claim 4 , wherein the mapping relationship comprises a hash map.

6. The method of claim 1 , wherein determining positions of atoms in a plurality of unit cells comprises: determining a position of the central unit cell based on coordinate parameters of the implantation position and the lattice constant at the determined grid unit; determining the positions of the adjacent unit cells based on the position of the central unit cell and the predetermined range; as well as Positions of atoms in the plurality of unit cells are determined based on positions of the central unit cell and the neighboring unit cells.

7. The method of claim 1 , wherein determining the types of atoms in the plurality of unit cells comprises: The atomic type of each lattice site in the plurality of unit cells is determined by probabilistic sampling based on the elemental composition of the multi-component material at the determined grid cell.

8. The method according to claim 1 , wherein generating a simulation result of ion implantation at the implantation location comprises: selecting atoms in the plurality of unit cells that are less than a predetermined distance from the implantation location; as well as Based on the position and type of the selected atoms, the results of collisions between the selected atoms and the implanted ions are calculated.

9. An electronic device comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, the instructions causing the electronic device to perform actions when executed by the processor, the actions comprising: Determining a grid cell corresponding to an implantation location using grid cell data of a device to be implanted; determining positions of atoms in a plurality of unit cells, the plurality of unit cells including a central unit cell at the implantation location and neighboring unit cells within a predetermined range around the central unit cell, based on a lattice constant at the determined grid cell, the lattice constant being related to an elemental composition of the multi-component material at the determined grid cell; determining types of atoms in the plurality of unit cells based on the elemental composition of the multi-component material at the determined grid cells; and A simulation result of ion implantation at the implantation location is generated based on the positions and types of atoms in the plurality of unit cells.

10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images