Simulation method of semiconductor device, electronic device, and computer-readable storage medium

Abstract

The present disclosure relates to a simulation method of a semiconductor device, an electronic device and a computer readable storage medium. The method comprises: determining a current position of a particle in a simulation region of the semiconductor device, wherein the simulation region comprises a local extended region containing at least one complete virtual lattice; simulating a motion of the particle based on the current position of the particle using the local extended region. Embodiments of the present disclosure can advantageously improve the physical precision of ion / particle implantation simulation.

Description

Technical Field

[0001] The embodiments of this disclosure primarily relate to the field of semiconductor devices, and more specifically, to simulation methods for semiconductor devices, electronic devices, and computer-readable storage media. Background Technology

[0002] Particle implantation is a critical process in semiconductor manufacturing, involving the precise injection of a beam of charged particles with a certain energy into a crystalline material matrix to achieve controllable adjustment of the spatial distribution of dopant elements. In advanced processes (e.g., below 7nm), the requirements for ion implantation simulation accuracy are even higher. For example, junction depth tolerance must be less than 1nm; doping concentration gradients must be precisely controlled within ±2%; and traditional analytical models (such as the Dual Pearson distribution) fail at the nanoscale. The Monte Carlo method, due to its ability to accurately simulate the physical processes of particle scattering, energy loss, and lattice damage in crystalline materials, is widely used for particle implantation simulation and has become the gold standard.

[0003] However, in existing Monte Carlo ion implantation simulations, a finite simulation region is typically defined to balance computational resources and simulation accuracy. Different boundary conditions need to be set at the boundaries of this region to simulate different physical environments in the actual chip. Taking mainstream tools (SRIM / TRIM, Sentaurus MC) as examples, boundary handling has fundamental flaws. For instance, existing simulation tools often directly apply the aforementioned boundary conditions when handling boundaries, ignoring the fact that the boundaries of the simulation region often cut through the atomic lattice, resulting in incomplete lattice units at the boundaries. When simulated ions move to these boundary regions, their collision environment with target atoms suddenly changes from a "complete lattice" to an "incomplete lattice." This geometric discontinuity leads to particle trajectory distortion, doping distribution errors, and physical model failure. Summary of the Invention

[0004] According to exemplary embodiments of this disclosure, a simulation method for semiconductor devices, an electronic device, and a computer-readable storage medium are provided to at least partially address the above-described or other potential drawbacks.

[0005] In a first aspect of this disclosure, a method for simulating a semiconductor device is provided, comprising: determining the current position of a particle within a simulation region of the semiconductor device, wherein the simulation region includes a locally extended region containing at least one complete virtual lattice; and simulating the motion of the particle using the locally extended region based on the particle's current position.

[0006] In a second aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and 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 include determining the current position of a particle within a simulation region of a semiconductor device, said simulation region including a locally extended region containing at least one complete virtual lattice; and simulating the motion of the particle using the locally extended region based on the particle's current position.

[0007] In some embodiments, constructing a local extended region comprising at least one complete virtual lattice at the boundary of the simulation region based on the current position includes: dynamically constructing a local extended region at the boundary in response to determining that the distance between the current position of the particle and the boundary of the simulation region to be reached is less than a predetermined threshold, wherein the local extended region comprises at least one complete virtual lattice; and continuing the motion simulation of the particle within the local extended region.

[0008] In some embodiments, constructing a local extended region comprising at least one complete virtual lattice at the boundary of the simulation region based on the current position includes: dynamically generating the local extended region at the boundary near the current position of the particle or the expected trajectory of the particle determined based on the current position.

[0009] In some embodiments, dynamically generating the local extended region includes: supplementing the outer side of the boundary with virtual atoms for a virtual lattice based on the current position of the particle, so as to extend the incomplete lattice at the boundary of the simulation region into at least one complete lattice including no cuts.

[0010] In some embodiments, after simulating the motion of the particle using the local extended region, the method further includes: determining the final state of the particle based on preset boundary conditions.

[0011] In some embodiments, the preset boundary conditions include at least one of the following: reflective boundary conditions; periodic boundary conditions; extended boundary conditions; and transparent boundary conditions.

[0012] In some embodiments, determining the final state of the particle based on the preset boundary conditions includes: under the reflection boundary condition, in response to the particle crossing the boundary, resetting the particle's position and momentum back to the simulation region according to the law of reflection; under the periodic boundary condition, in response to the particle crossing the boundary, resetting the particle back to the corresponding periodic position within the simulation region by translation; under the expansion boundary condition, determining the particle's position in the locally expanded region as the particle's new position; or under the transparency boundary condition, in response to the particle crossing the boundary, marking the particle as permanently leaving the simulation region.

[0013] In some embodiments, resetting the particle back to the corresponding periodic position within the simulation area by translation includes: changing the position of the particle while keeping the particle's direction of motion unchanged.

[0014] In some embodiments, the method further includes, in response to determining that the distance between the current position of the particle and the boundary of the simulation region to be reached is not less than a predetermined threshold, performing flight and collision simulations of the particle within the simulation region according to the Monte Carlo method.

[0015] In some embodiments, the predetermined threshold is determined based on the lattice constant of the material.

[0016] In a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0017] The solutions employed in the embodiments of this disclosure can advantageously improve the physical accuracy of ion / particle implantation simulations. Specifically, the embodiments of this disclosure dynamically supplement the imperfect lattice at the boundary of the simulation region into a perfect lattice, ensuring the continuity of the flight trajectory and collision physics of particles when traversing different boundary conditions, thereby significantly improving the accuracy of doping concentration distribution prediction. Furthermore, the solutions of this disclosure belong to the field of computer-aided design (TCAD) for semiconductor manufacturing processes, specifically involving high-precision simulation of ion implantation processes, such as Monte Carlo simulation techniques. The technical solutions of this disclosure are particularly suitable for ion implantation process simulation of advanced processes below 14nm (such as GAA transistors and 3D-NAND memory cells). By solving the collision distortion problem of particles in the boundary region of the simulated structure, the physical accuracy of doping distribution prediction is improved, providing core technical support for the performance optimization of nanoscale devices.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements.

[0020] Figure 1 A schematic diagram of an example environment in which several embodiments of the present disclosure can be implemented is shown.

[0021] Figure 2 A flowchart illustrating a simulation method for a semiconductor device according to some embodiments of the present disclosure is shown.

[0022] Figures 3A to 3D The illustrations illustrate examples of simulating particles based on different boundaries according to some embodiments of the present disclosure.

[0023] Figure 4 A flowchart of a simulation method for a semiconductor device according to some embodiments of the present disclosure is shown.

[0024] Figure 5A and Figure 5B The illustration schematically shows improvements in the simulation of semiconductor devices according to embodiments of the present disclosure.

[0025] Figure 6 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0026] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0027] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0028] Monte Carlo methods track the trajectories of a large number of particles and statistically analyze their distribution in materials, providing a theoretical basis for process optimization and significantly reducing experimental costs and development cycles. Transported ion implantation (TCAD) tools use Monte Carlo methods to simulate the scattering process of a large number of ions in a target material to predict the final doping distribution, which is crucial for device performance and yield evaluation.

[0029] However, as mentioned earlier, in particle implantation simulations of complex crystal structures (e.g., compound semiconductors), lattice discontinuities at the boundary regions of the simulation area lead to particle trajectory distortion, doping distribution errors, and physical model failure. Particle trajectory distortion occurs because abrupt changes in the scattering barrier and collision probability experienced by particles at the boundary cause non-physical jumps in the calculation of their flight direction and energy loss. Doping distribution errors occur because these non-physical trajectory jumps accumulate and are reflected in the final doping concentration distribution map, especially near the boundary and in regions with significant channel effects, causing problems such as distribution tailing and inaccurate peak concentration. Physical model failure occurs because the core physical model based on continuity and periodicity (such as the binary collision approximation) fails at lattice discontinuities, reducing the predictive reliability of the entire simulation tool.

[0030] Furthermore, the distortion boundary effects are amplified in advanced processes. For example, the simulated region of a 3nm GAA structure is only 20×20nm. 2 The proportion of boundary particles is greater than 40% (compared to less than 10% at 28nm). Furthermore, boundary lattice fractures and incompleteness can lead to electron cloud density distortion, affecting nonlocal scattering calculations. A 1nm doping offset may cause GAA threshold voltage fluctuations greater than 50mV.

[0031] Therefore, an improvement scheme is needed to enhance the performance of particle injection simulation for semiconductor devices.

[0032] According to embodiments of this disclosure, a method for simulating a semiconductor device is provided, comprising: determining the current position of a particle within a simulation region of the semiconductor device, wherein the simulation region includes a locally extended region; and simulating the motion of the particle using the locally extended region based on the current position of the particle.

[0033] The method of this disclosure, by dynamically reconstructing the lattice structure of the simulation region boundary, ensures that particles can interact with a physically reasonable and continuous lattice environment under any boundary conditions, thereby eliminating non-physical effects caused by boundary-cutting lattice. Specifically, when a particle moves to the boundary region and boundary condition judgment is required, it is first determined whether the local lattice at the boundary is a complete, uncut lattice. If the local lattice at the boundary is not a complete lattice, a pre-determined local extended region including at least one uncut complete lattice can be used to continue the particle motion simulation. The local extended region can be pre-constructed (e.g., calculated in a previous Monte Carlo iteration) or newly constructed in the current iteration. Constructing the local extended region can include dynamically supplementing the local lattice at the boundary into a complete, extended virtual lattice, then continuing to calculate the particle's trajectory on this complete virtual lattice, and finally performing final processing on the particle state according to the specific boundary conditions. Therefore, the embodiments of this disclosure decouple the geometric problem and the physical problem of boundary processing, first solving the trajectory continuity problem through geometric supplementation, and then solving the physical intent problem through boundary condition application.

[0034] Compared with the prior art, the solutions of the present disclosure embodiments have at least the following significant advantages:

[0035] With high physical precision, the embodiments disclosed herein fundamentally eliminate non-physical collision distortion at the boundary, making the particle scattering process at the interface more consistent with real physical laws, and significantly improving the prediction accuracy of doping distribution, especially channeling effect and distribution tail.

[0036] With its high versatility, the embodiments disclosed herein, as a pre-processing geometry, can be seamlessly integrated into existing Monte Carlo ion implantation simulators that support multiple boundary conditions without altering the core scattering physics model.

[0037] The computational overhead is controllable. The embodiments of this disclosure only trigger the operations of "dynamically using the local extended region" and / or "dynamically supplementing the local extended region" when the particle approaches the boundary, minimizing the impact on the overall data structure of the simulation region. The supplemented virtual lattice is local and temporary, and the additional consumption of computational resources is limited to the boundary, resulting in high cost-effectiveness.

[0038] Improving the performance of simulation tools by addressing fundamental accuracy issues makes the TCAD tools using embodiments of this disclosure more predictive and reliable in ion implantation simulations at advanced process nodes (such as FinFET, GAA), which has significant commercial value.

[0039] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0040] Figure 1A schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented is shown. The computing device 10 in the example environment 100 can be any device with computing capabilities. As a non-limiting example, the computing device 10 can be any type of fixed computing device, mobile computing device, or portable computing device, including but not limited to desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, multimedia computers, mobile phones, etc.; all or some components of the computing device 10 can be distributed in the cloud.

[0041] In this example environment 100, computing device 10 may include or be deployed with acquisition module 1, tracking module 2, and extension module 3. The actions described below with respect to computing device 10 may be performed by at least one of acquisition module 1, tracking module 2, and extension module 3.

[0042] The acquisition module 1 can be configured to acquire simulation information of the semiconductor device and at least one particle for which simulation calculations are required. This simulation information can be acquired from a client (not shown). As used herein, the simulation information may also be referred to as simulation data. Semiconductor device data in the simulation data may include one or more of the following: the geometry, material, size, etc. of the semiconductor device. Particle simulation information in the simulation data may include one or more of the following: the geometry, material, size, initial velocity, etc. of the particle. In some embodiments, the simulation data may also include a pre-stored local extended region for the simulation of the semiconductor device.

[0043] The tracking module 2 can be configured to track the positional relationship of the particle relative to the simulated semiconductor device in the simulated space. Specifically, in some embodiments, the tracking module 2 can be configured to iteratively and in real time update the relative positional relationship between the particle and the simulated semiconductor device as the particle continues to move.

[0044] Extension module 3 is configured to pause the dynamic simulation of the particle when its motion in the simulation space is about to reach a distance from the boundary of the simulation region below a predetermined threshold, and to construct a local extended region comprising at least one complete virtual lattice at the boundary of the simulation region based on the particle's current position. Extension module 3 is also configured to continue the dynamic simulation of the particle after constructing the extended region comprising at least one complete virtual lattice.

[0045] It should be understood that Figure 1 The module divisions shown are for illustrative purposes only and are not intended to be limiting. Furthermore, it should be understood that example environment 100 can also have various other implementations. To more clearly explain the principles of this disclosure, reference will be made below. Figure 2 Let's describe it in more detail.

[0046] The following is for reference. Figure 2 This describes a simulation method 200 for semiconductor devices according to embodiments of the present disclosure. Figure 2 The illustration shows a flowchart of a simulation method for a semiconductor device according to some embodiments of the present disclosure. In this method, the position and motion state of particles can be updated. For example, method 200 can be performed using computing device 10 or other computing devices.

[0047] like Figure 2 As shown, at box 21, the current position of the particle is determined within the simulation region of the semiconductor device. The simulation region may include a local extended region.

[0048] In some embodiments, the simulation area can be determined by acquiring simulation data of the semiconductor device using the acquisition module 1. In some embodiments, the simulation data may include at least one of the following: overall structural size parameters of the semiconductor device, polyhedral position information, unit topological relationships, initial particle motion information, etc. Information including the simulation data of the semiconductor device can be input into the acquisition module 1 in any way. Then, the acquisition module 1 can output the simulation data of the semiconductor device to the tracking module 2, and the tracking module 2 performs particle motion tracking. It should be understood that the above description is merely illustrative, and the simulation method of the embodiments of this disclosure can be executed by inputting simulation data into simulation software in any way.

[0049] In some embodiments, the simulation region can be defined as needed, the material and lattice type (e.g., silicon as diamond lattice) can be set, and the boundary conditions (reflection, periodicity, extension, transparency, etc.) of each boundary can be specified.

[0050] In some embodiments, the position of particles is monitored in real time to determine whether they are about to cross or enter a preset critical distance near the boundary. Appropriate processing can be performed based on the particle's position. For example, in some embodiments, the injected ions can be simulated using the Monte Carlo method within the simulation area, performing conventional flight and collision simulations. If a particle is about to cross or enter a preset critical distance near the boundary, it can be processed according to the methods described below.

[0051] Next, at box 22, the particle's motion is simulated using a locally extended region based on the particle's current position. For example, if the particle's current position is determined to be more than a threshold distance from the boundary of the simulation region, method 200 may not use the locally extended region, thereby reducing the computational resources required for the simulation process. Conversely, if the particle's current position is determined to be less than a threshold distance from the boundary of the simulation region, method 200 uses the locally extended region to simulate the particle's motion, thereby improving the accuracy of the particle motion simulation. In this way, method 200 can dynamically use the locally extended region of the simulation area.

[0052] In some embodiments, dynamically using a local extended region of the simulation region based on the particle's current position may include constructing a local extended region comprising at least one complete virtual lattice at the boundary of the simulation region based on the current position, provided that the distance between the particle's current position and the boundary of the simulation region to be reached is less than a predetermined threshold. For example, the predetermined threshold may be determined based on the lattice constant of the material. This disclosure is not limited thereto, and other thresholds may be used as needed. According to some embodiments of this disclosure, the predetermined threshold distance may be 1, 2, 5, or more angstroms (Å). In other embodiments, the predetermined threshold distance may be 1, 2, 5, or more nanometers. In some embodiments, the local extended region may be dynamically generated based on the particle's current position or the intersection of the particle's expected trajectory determined based on the current position and the boundary. According to some embodiments, the local extended region generated by method 200 may be stored, for example, by computing device 10, for use in future simulations. According to some embodiments, method 200 may read a predetermined local extended region stored by computing device 10.

[0053] In some embodiments, dynamically using the locally expanded region may further include adding virtual atoms outside the boundary based on the particle's current position or its intersection with the boundary of the simulation region, to expand the incomplete lattice at the boundary of the simulation region into an uncut integer multiple of a complete lattice. The virtual atoms described herein can be simulated atoms representing the lattice of a semiconductor device used for simulation. When calculating particle trajectories in a simulation region with an expanded complete lattice, collisions between the particle and the added virtual atoms are included in the calculation, thereby improving simulation performance.

[0054] In some embodiments, when a particle enters a critical boundary region, its motion calculation based on the original incomplete simulation structure is paused. A local extended region is dynamically generated, centered on the particle's current position or with reference to the intersection of its trajectory and the boundary. The size of this extended region is guaranteed to accommodate at least one complete lattice unit cell. Based on the actual atomic positions within the simulation region, atoms are "added" to the outer edge of the boundary, making the lattice structure within this local extended region a complete, uncut integer multiple of a lattice.

[0055] In some embodiments, in response to determining that the distance between the particle's current position and the boundary of the simulation region to be reached is not less than a predetermined threshold, the particle's flight and collision simulation is performed within the simulation region according to the Monte Carlo method. That is, the particle's flight and collision simulation can be performed according to the traditional Monte Carlo method.

[0056] In some embodiments, one or more steps of particle flight and collision calculations are performed on a dynamically generated complete virtual lattice. At this point, collisions between the particle and the "supplementary atoms" are calculated normally, and their trajectories are geometrically smooth and continuous. After the calculations on the virtual lattice are completed, the final state of the particle can be processed according to preset boundary conditions.

[0057] In some embodiments, method 200 may further include, after simulating the motion of the particle within the local extended region, determining the final state of the particle based on preset boundary conditions.

[0058] Next reference Figures 3A to 3D This illustrates various preset boundary conditions according to this disclosure. Specifically, Figure 3A The reflective boundary condition 301 has been applied. Figure 3B The periodic boundary condition 302 is shown. Figure 3C Extended boundary condition 303 is shown. Figure 3D Transparent boundary condition 304 is shown.

[0059] As mentioned earlier, different boundary conditions need to be set for the boundaries of the simulation region to simulate different physical environments in the actual chip. Common boundary conditions include:

[0060] Reflection boundary: Simulates total internal reflection of particles at the interface of insulating materials.

[0061] Periodic boundary: simulates the infinite periodic repetition of the structure in the horizontal direction.

[0062] Extended Boundary: Treats the material outside the simulation region as the same as the material inside the boundary.

[0063] Transparent boundary: Particles disappear upon passing through it, simulating an open boundary.

[0064] Traditional approaches suffer from fundamental flaws in handling the aforementioned boundary conditions. For example, for periodic boundaries (such as DRAM arrays), direct coordinate translation is used, which disrupts lattice continuity, resulting in a channel injection error of up to 28%. For symmetric boundaries (FinFET fin sidewalls), a rigid reflection model is used, leading to incomplete lattice material construction at small scales, with dopant surface deposition deviations exceeding 15%. For extended boundaries (superjunction devices), simulation region extension is used, which also disrupts lattice continuity, resulting in a breakdown voltage prediction error of 22%. For transparent boundaries, particle penetration disappears, leading to a lack of depth-direction statistics.

[0065] The following is for reference. Figure 3A ,like Figure 3AAs shown, under the reflective boundary condition 301, in response to particle 31 crossing the reflective boundary 30a, the position and momentum of particle 31 can be reset back to the reflected position 31a in the simulation area according to the law of reflection.

[0066] like Figure 3B As shown, under periodic boundary condition 302, in response to particle 31 crossing periodic boundary 30b, particle 31 is reset (i.e. mapped back) to the corresponding periodic position 31b within the simulation region by translation. For example, in the example of a two-dimensional system, after the particle crosses one side boundary of the simulation region, it returns to a mirror-symmetric position within the simulation region with that side boundary as the axis of symmetry. For example, in the case of periodic boundary 30b, the position of particle 31 can be changed while keeping the direction of motion of particle 31 unchanged.

[0067] like Figure 3C As shown, under the extended boundary condition 303, in response to particle 31 crossing the extended boundary 30c, the position of particle 31 in the locally extended region is determined as the new position 31c of particle 31. That is, the final position of particle 31 in the virtual lattice is directly taken as its new position in the extended material. For example, the new position 31c of particle 31 remains in the newly formed, extended lattice structure.

[0068] like Figure 3D As shown, under transparent boundary condition 304, in response to particle 31 crossing transparent boundary 30d, it is marked as terminated, that is, particle 31 is marked as permanently leaving the simulation area and will not return. In other words, when method 200 determines that particle 31 has crossed transparent boundary 30d, method 200 can determine that particle 31 will not return to the simulation area, and therefore can stop tracking particle 31. In some embodiments, method 200 may also mark the position 31d of particle 31 that has crossed transparent boundary 30d for use in subsequent simulation steps.

[0069] Back Figure 2 Method 200, after determining the final position and motion state of the particle based on the simulation region with added local expansion, can return the final position and motion state of the particle to the main simulation flow. For example, the final position and motion state of the particle can be updated in the main simulation flow that executes Monte Carlo stepping, and the Monte Carlo method can then perform the next stepping calculation.

[0070] Figure 4The process of performing a semiconductor device simulation method 400 according to some embodiments of the present disclosure is illustrated. For example, method 400 may be performed using computing device 10 or other computing devices. In step 41, method 400 performs a step-by-step calculation of the main simulation flow. For example, step 41 may perform a step-by-step calculation of the iterative calculation of the Monte Carlo method. In particle Monte Carlo simulation, "step-by-step" refers to the complete process of a particle moving from one physical event point to the next physical event point. In other words, "step-by-step" means that particle 31 moves directly from one collision (or interaction) to the next collision. In step 42, method 400 determines whether the particle will collide with the boundary of the simulation region in the current iteration. If it is determined that the particle will not collide with the boundary, method 400 returns to step 41 and performs the calculation of the main simulation flow; if it is determined that the particle will collide with the boundary, method 400 proceeds to step 43. In step 43, the execution of the main simulation flow is paused, and the updated position and updated motion state of the particle in the current iteration are determined. Next, in step 44, method 400 resumes the execution of the main flow. For example, in step 44, method 400 can proceed to the next step of iterative calculation of the Monte Carlo method based on the updated position and updated motion state of particle 31. As described above, the updated position and updated motion state of particle 31 can be obtained based on method 200.

[0071] The following is for reference. Figure 5A and Figure 5B This is to illustrate the improvement in the simulation of semiconductor devices using embodiments of the present disclosure. Figure 5A The simulation of simulation region 50 of a semiconductor device using conventional methods is shown. It can be... Figure 5A What we observe is an anomaly in particle doping concentration within the critical region 51, from X=0.06 to X=0.11. For example, in Figure 5A The uniformity of the concentration of doped particles within the aforementioned range is poor. In contrast, Figure 5B Simulation results using the method 200 of this disclosure are shown. Figure 5A Comparison of results in simulation region 50 Figure 5B The particle doping uniformity in the critical region 53 from X=0.06 to X=0.11 in the simulation region 52 is relative to Figure 5A Significant improvements were achieved in the critical region 51.

[0072] This disclosure provides a method for resolving lattice discontinuities in simulation boundary regions and improving the accuracy of simulation physics. It features high-speed update capability and memory-efficient robustness.

[0073] Figure 6A schematic block diagram of an example device 600 that can be used to implement embodiments of the present disclosure is shown. Device 600 can be used to implement... Figure 1 The computing device 10. As shown, the device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 602 or loaded from storage unit 608 into random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0074] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0075] Processing unit 601 executes the various methods and processes described above. For example, according to Figure 2 The simulation method 200 shown and according to Figure 4 The simulation method 400 shown can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU 601, one or more steps of extending the boundary of the simulation region and / or simulating the collision of particles with the boundary as described above can be performed. Alternatively, in other embodiments, CPU 601 can be configured by any other suitable means (e.g., by means of firmware) to perform any of the methods 400 and 200 according to this disclosure.

[0076] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0077] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0079] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0080] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A simulation method for a semiconductor device, comprising: The current position of the particle is determined within a simulation region of the semiconductor device, wherein the simulation region includes a locally extended region containing at least one complete virtual lattice; as well as In response to determining that the distance between the particle's current position and the boundary of the simulation region to be reached is less than a predetermined threshold, the motion of the particle is simulated using the local extended region.

2. The method according to claim 1, further comprising: Based on the current position, the local extended region is constructed at the boundary of the simulation region, wherein constructing the local extended region includes: In response to determining that the distance between the current position of the particle and the boundary of the simulation region to be reached is less than the predetermined threshold, a local expansion region is dynamically constructed at the boundary; The motion simulation of the particles continues within the local extended region.

3. The method of claim 2, wherein constructing the local extended region at the boundary of the simulation region based on the current position comprises: The local expansion region is dynamically generated at the boundary near the particle's current position or the expected trajectory of the particle determined based on the current position.

4. The method according to claim 3, wherein dynamically generating the local extended region comprises: Based on the current position of the particle, virtual atoms for the virtual lattice are added to the outside of the boundary to expand the incomplete lattice at the boundary of the simulation region into at least one complete lattice with no cuts.

5. The method of claim 1, wherein after simulating the motion of the particle using the locally extended region, it further comprises: The final state of the particle is determined based on preset boundary conditions.

6. The method according to claim 5, wherein the preset boundary condition includes at least one of the following: Reflection boundary conditions; Periodic boundary conditions; Extend boundary conditions; Transparent boundary conditions.

7. The method according to claim 6, wherein determining the final state of the particle based on the preset boundary conditions comprises: Under the reflection boundary conditions, in response to the particle crossing the boundary, the position and momentum of the particle are reset back to the simulation region according to the law of reflection; Under the periodic boundary conditions, in response to the particle crossing the boundary, the particle is reset back to the corresponding periodic position within the simulation area by translation; Under the extended boundary conditions, the position of the particle in the local extended region is determined as the new position of the particle; Under the transparent boundary condition, in response to the particle crossing the boundary, the particle is marked as permanently leaving the simulation area.

8. The method of claim 7, wherein resetting the particle back to the corresponding periodic position within the simulation region by translation comprises: Change the position of the particle while keeping its direction of motion unchanged.

9. The method according to claim 7, further comprising: In response to determining that the distance between the current position of the particle and the boundary of the simulation area to be reached is not less than a predetermined threshold, the particle is simulated for flight and collision within the simulation area according to the Monte Carlo method.

10. The method according to any one of claims 2 to 4 and 9, wherein the predetermined threshold is determined based on the lattice constant of the material.

11. An electronic device, comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, the instructions which, when executed by the processor, cause the electronic device to perform actions, the actions including: The current position of a particle is determined within a simulation region of a semiconductor device, wherein the simulation region includes a locally extended region containing at least one complete virtual lattice. In response to determining that the distance between the particle's current position and the boundary of the simulation region to be reached is less than a predetermined threshold, the motion of the particle is simulated using the local extended region.

12. A computer-readable storage medium having a computer program stored thereon, the program, when executed by a processor, implementing the method according to any one of claims 1 to 10.

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