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

Abstract

This disclosure relates to a method, electronic device, and computer-readable storage medium for semiconductor device simulation. The method includes: acquiring a simulation region of the semiconductor device, the simulation region comprising a plurality of grid cells; for each of the plurality of grid cells, acquiring one or more physical quantities characterizing the simulation state of the grid cell; and based on a comparison of the one or more physical quantities with corresponding thresholds, identifying a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located within the defect-sensitive region; and for the defect-sensitive grid cell, determining a defect energy level based on the distribution state of defects. Embodiments of this disclosure can advantageously improve the physical accuracy of ion / particle implantation simulations.

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] In semiconductor device simulation, the carrier trapping and release behavior of defects (such as interface states and bulk defects) is one of the key factors affecting device performance. Defect-induced carrier trapping and release can directly impact device performance and reliability. In complex structures (such as 3D devices) and advanced processes (below 3nm), simulation methods based on defect models can reflect electrical characteristics (such as threshold voltage and mobility) and predict failure mechanisms (such as hot carrier instability (HCI) and bias temperature instability (BTI)), thereby reducing experimental costs, accelerating device development, and providing crucial support for industrial-grade design.

[0003] Current simulation methods for defects mainly rely on deterministic methods, which involve directly calculating the macroscopic charge distribution by integrating the defect distribution function, and then further calculating its impact on device performance. In many cases, deterministic methods cannot simulate the microscopic behavior of defects trapping or releasing charge carriers, making it difficult to accurately quantify the impact of defects on device performance. 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. In a first aspect of this disclosure, a method for simulating a semiconductor device is provided, comprising: acquiring a simulation region of the semiconductor device, the simulation region including a plurality of grid cells; for each of the plurality of grid cells, acquiring one or more physical quantities characterizing the simulation state of the grid cell; and based on a comparison of the one or more physical quantities with corresponding thresholds, determining a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region; and for the defect-sensitive grid cell, determining a defect energy level based on the distribution state of defects. 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: acquiring a simulation region of the semiconductor device, the simulation region including a plurality of grid cells; for each of the plurality of grid cells, acquiring one or more physical quantities characterizing the simulation state of the grid cell; and based on a comparison of the one or more physical quantities with corresponding thresholds, identifying a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region; and for the defect-sensitive grid cell, determining a defect energy level based on the distribution state of defects. In some embodiments, determining a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region based on a comparison of the one or more physical quantities with a corresponding threshold includes: normalizing the plurality of different types of physical quantities to generate a plurality of normalized physical quantities of the grid cell; assigning corresponding weights to each of the plurality of normalized physical quantities to generate a plurality of weighted physical quantities of the grid cell; and determining that the grid cell is a defect-sensitive grid cell in the defect-sensitive region in response to the sum of the plurality of weighted physical quantities of the grid cell being greater than the threshold. In some embodiments, generating random energy levels for defects in the grid cells of the defect-sensitive region includes: integrating the defect distribution function to obtain the cumulative distribution function of the defects in the grid cells; generating random numbers; and determining Monte Carlo sampling defect energy levels based on the random numbers and the cumulative distribution function. In some embodiments, determining the defect energy level based on the distribution state of the defects includes: determining the event probability of a defect in the grid cell capturing a carrier and the event probability of a defect releasing a carrier; determining a total event time interval for the event probabilities of capturing and releasing carriers; and updating the concentration of the carriers and the charge of the defects in the grid cell based on the total event time interval. In some embodiments, the action further includes: in response to the physical quantity being greater than the threshold, determining the corresponding mesh cell among the plurality of mesh cells as the defect-sensitive mesh cell located in the defect-sensitive region; in response to the physical quantity not being greater than the threshold, determining the corresponding mesh cell among the plurality of mesh cells as another region located outside the defect-sensitive region; and performing a deterministic method on other regions in the simulation region that are determined not to belong to the defect-sensitive region. In some embodiments, the action further includes performing at least one of the following processes on the boundary defining the defect-sensitive region and the other regions to ensure that the physical quantities at the boundary remain consistent: performing an interpolation operation on the physical quantities of the grid cells adjacent to the boundary; performing an averaging operation on the physical quantities of the grid cells adjacent to the boundary. In some embodiments, performing interpolation on the physical quantity of the grid cell adjacent to the boundary includes: determining the value of the physical quantity by performing linear interpolation calculation based on the distances between a first grid in the defect-sensitive region at the boundary and a second grid adjacent to the first grid in another region at the boundary and a point on the boundary. In some embodiments, averaging the physical quantities of the grid cells adjacent to the boundary includes: determining the value of the physical quantity by performing a weighted average calculation based on the area or volume of a first grid in the defect-sensitive region at the boundary and a second grid adjacent to the first grid in other regions at the boundary.

[0005] In some embodiments, the physical quantities include one or more of the following: charge density, electric potential, carrier concentration, mobility, temperature, current density, band gap, conduction band bottom, valence band top, defect density, electric field strength, and carrier concentration gradient. In some embodiments, the action further includes correcting the carrier mobility of the defects in the simulation region based on the following formula. μ : in μ 0 represents the carrier mobility when there are no defects. Q trap This represents the amount of charge in the defect. α This represents the correction factor, the correction factor α The value is between 0.5 and 1.0. N eff Indicates the effective doping concentration.

[0006] In some embodiments, the effective doping concentration Neff is calculated based on the following formula: Where N represents the actual doping concentration. E g Indicates the width of the material's no-go zone. T It represents absolute temperature, and k B This represents the Oltzmann constant.

[0007] In some embodiments, the action further includes dynamically adjusting the weights and the thresholds based on the type and process conditions of the semiconductor device.

[0008] 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.

[0009] The schemes using embodiments of this disclosure can automatically identify defect-sensitive regions of a device for high-precision simulation, simulating the process of defect trapping and releasing charge carriers, and offer the dual flexibility of independent use and use in conjunction with deterministic methods. Furthermore, the method according to this disclosure advantageously employs boundary synchronization techniques to address the issue of physical quantity consistency at the boundary between sensitive and non-sensitive regions.

[0010] 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

[0011] 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.

[0012] Figure 1 A schematic diagram of an example environment in which several embodiments of the present disclosure can be implemented is shown.

[0013] Figure 2 A flowchart illustrating a simulation method for a semiconductor device according to some embodiments of the present disclosure is shown.

[0014] Figure 3 A flowchart illustrating a simulation method for a semiconductor device according to some embodiments of the present disclosure is shown.

[0015] Figure 4 A flowchart illustrating a simulation method for a semiconductor device according to some embodiments of the present disclosure is shown.

[0016] Figure 5 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] Current device simulation methods for defects mainly rely on deterministic approaches, which directly calculate the macroscopic charge distribution by integrating the defect distribution function, and then further calculate its impact on device performance. For example, current conventional methods typically use the following formula to calculate the macroscopic charge distribution:

[0020] in D it (E) Represents the defect distribution function. f(E) This represents the Fermi-Dirac distribution.

[0021] However, as mentioned above, in particle injection simulations of complex crystal structures (e.g., compound semiconductors), deterministic methods cannot simulate the microscopic behavior of defect trapping or releasing charge carriers, making it difficult to accurately quantify the impact of defects on device performance and reducing the predictive reliability of the entire simulation tool.

[0022] Therefore, an improved scheme is needed to enhance the performance of particle injection simulation for semiconductor devices.

[0023] According to embodiments of this disclosure, a simulation method for a semiconductor device is provided. The method includes: acquiring a simulation region of the semiconductor device, the simulation region including a plurality of grid cells; for each of the plurality of grid cells, acquiring one or more physical quantities characterizing the simulation state of the grid cell; and based on a comparison of the one or more physical quantities with corresponding thresholds, determining a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region; and for the defect-sensitive grid cell, determining a defect energy level based on the distribution state of defects. Embodiments of this disclosure can advantageously improve the physical accuracy of ion / particle implantation simulations.

[0024] The method of this disclosure is based on the normalization processing of physical quantities (such as defect density, electric field strength, and carrier gradient) of the grid cells used to characterize the simulation region during semiconductor device simulation, and identifies sensitive regions through multi-dimensional sensitivity calculation. The weighting coefficients and sensitivity thresholds can be dynamically adjusted according to device type and process conditions. For defects with continuous distribution functions, this disclosure employs the Monte Carlo method to generate defect energy levels through inverse transformation sampling. Furthermore, this disclosure calculates defect event probabilities based on a Poisson process, generates time intervals through an exponential distribution, and generates random numbers using the Monte Carlo method to determine the event type (capture or release), achieving precise quantification of the microscopic dynamics of defects. Further, to address the discontinuity at the boundary between sensitive and non-sensitive regions, this disclosure uses interpolation and averaging methods to synchronize boundary physical quantities. In some embodiments, this disclosure proposes a correction formula for defect charge on mobility, improving the accuracy of mobility prediction. Furthermore, this disclosure also supports co-simulation using the Monte Carlo method and deterministic methods. For example, the Monte Carlo method is used in defect-sensitive areas, while the deterministic method is used in non-defect-sensitive areas, and partition collaboration is triggered by a sensitivity threshold.

[0025] Compared with the prior art, the solutions of the present disclosure embodiments have at least the following significant advantages:

[0026] By simulating the carrier capture and release behavior of defects using the Monte Carlo method, this approach overcomes the limitations of traditional deterministic methods for calculating macroscopic charge distributions. Through random energy level generation and event probability simulation, it achieves precise quantification of the microscopic dynamics of defects, making it particularly suitable for complex energy level distributions (such as continuous distribution function defects).

[0027] Based on the normalization of multiple physical quantities such as defect density, electric field strength, and carrier gradient, defect-sensitive regions in devices are automatically identified. Through dynamic weight allocation and sensitivity threshold adjustment, it can be adapted to different device structures (such as MOSFETs and FinFETs) and process conditions (such as temperature and doping concentration).

[0028] 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.

[0029] A modified model for the effect of defect charge on carrier mobility can be proposed based on material properties (such as bandgap). E g The calibration of process parameters significantly improves the accuracy of migration rate prediction, especially for defect-sensitive areas.

[0030] It supports incorporating multiple physical quantities (such as temperature, current density, and bandgap parameters) into defect sensitivity calculations, adapting to the device simulation needs under complex process conditions. It also provides a user-defined sensitive area marking function to meet the rapid modeling needs of specific design scenarios, such as directly designating a region as a sensitive area to accelerate defect analysis in critical regions.

[0031] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0032] Figure 1 A 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.

[0033] In this example environment 100, computing device 10 may include or be deployed with acquisition module 1 and processing module 2. The actions described below with respect to computing device 10 may be performed by at least one of acquisition module 1 and processing module 2.

[0034] 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 an external device (not shown) coupled to the computing device 10. 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. Simulation information of the particle 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 pre-stored region information for the simulation of the semiconductor device.

[0035] The processing 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 processing 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.

[0036] Processing module 2 can execute both the defect-sensitive method described in this disclosure and the non-defect-sensitive method, for example, integrating the defect distribution function to directly calculate the macroscopic charge distribution.

[0037] 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.

[0038] 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, defect-sensitive mesh cells in a simulation region can be dynamically determined, and defect energy levels can be determined based on the distribution state of the defects. For example, method 200 can be performed using a computing device such as computing device 10 or other computing devices.

[0039] In some embodiments, the simulation region 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, cell topology 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 processing module 2, and the processing module 2 performs the determination of defect-sensitive mesh cells and the tracking of particle motion. 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. According to some embodiments, the defect-sensitive region can be manually specified by the user in a designated manner. For example, if the user believes that a defect in a certain region may have a significant impact on the device, the user can directly mark the mesh cells in that region as defect-sensitive mesh cells.

[0040] To determine the defect-sensitive grid cells located in the defect-sensitive region among multiple grid cells of the simulation region of the semiconductor device, method 200 may include step 21, acquiring the simulation region of the semiconductor device, the simulation region comprising multiple grid cells. For example, the simulation region of the semiconductor device may be obtained by the acquisition module 1 of the computing device 10 from a device coupled to the computing device 10, or pre-stored in the acquisition module 1. The simulation region may be pre-divided into multiple grid cells, or obtained by the processing module 2 dividing the acquired simulation region after acquisition.

[0041] Method 200 further includes step 22, acquiring one or more physical quantities characterizing the simulation state of each of the plurality of mesh cells. Similar to the simulation region, the one or more physical quantities can also be acquired by the acquisition module. For example, the one or more physical quantities may include at least one of the following: defect density Dit, electric field strength E, and carrier concentration gradient. .

[0042] Method 200 further includes step 23, which compares a physical quantity with a threshold, and, based on the comparison of the one or more physical quantities with the corresponding threshold, determines a corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region. For example, in some embodiments, the defect density Dit, electric field intensity E, and carrier concentration gradient of the grid cells in the simulation region can be used. Three physical quantities are used in conjunction with the corresponding defect density threshold Dth (e.g., 1×10⁻⁶). 11 cm -2· eV -1 ), electric field strength threshold Eth (e.g., 1×10), 5 V / cm), carrier concentration gradient threshold Gth (e.g., 1×10⁻⁶) 18 cm -3 Compare ( / μm).

[0043] To identify a corresponding mesh cell as a defect-sensitive mesh cell located within a defect-sensitive region, for example, in response to any of the aforementioned physical quantities exceeding a threshold, the corresponding mesh cell can first be identified as located within a potential defect-sensitive region. Then, based on further subsequent processing, it is confirmed that the corresponding mesh cell located within the potential defect-sensitive region is indeed a defect-sensitive mesh cell located within the defect-sensitive region.

[0044] For example, refer to Figure 3 The method 400 shown can determine whether the grid cell located in the potential defect-sensitive region identified in method 200 is a defect-sensitive grid cell located in the defect-sensitive region. In step 41, multiple different types of physical quantities can be normalized and mapped to the range [0,1] to generate multiple normalized physical quantities for each corresponding grid cell i. In this way, the weight bias caused by the difference in dimensions between different physical quantities can be eliminated. Continuing with defect density Dit, electric field intensity E, carrier concentration gradient... Three physical quantities are used as examples. In some embodiments, the normalized defect density can be determined using the following equation. : Where D min and Dmax Let represent the minimum and maximum values ​​of the global defect density, respectively. This represents the defect density of the corresponding mesh element.

[0045] In some embodiments, the normalized electric field strength can be determined using the following equation. : Where E min and E max Let E represent the minimum and maximum values ​​of the global electric field intensity, respectively, and E i This represents the electric field strength of the corresponding grid cell. In some embodiments, the normalized defect density can be determined using the following equation. : in( ) min and( ) max Let represent the minimum and maximum values ​​of the global defect density, respectively, and ( )i represents the carrier concentration gradient of the corresponding grid cell.

[0046] Subsequently, in step 42, corresponding weights can be assigned to each of the plurality of normalized physical quantities to generate a plurality of weighted physical quantities for the mesh element. For example, weights w1 (e.g., 0.5), w2 (e.g., 0.2), and w3 (e.g., 0.2) can be assigned to the normalized defect density according to the contribution of each parameter to the defect sensitivity. Normalized electric field strength and normalized defect density To calculate the sensitivity index Si of this grid cell:

[0047] The values ​​of weights w1, w2, and w3 above are merely illustrative examples, and the scope of this disclosure is not limited thereto. For example, the values ​​of weights w1, w2, and w3 can be other values, and w1, w2, and w3 can be the same as or different from each other.

[0048] In some embodiments, the values ​​of weights w1, w2, and w3 may be pre-stored in the computing device 10 or input into the computing device 10 by the user.

[0049] The following is a reference. Figure 3, in step 43, it is possible to determine that the grid cell is a defect-sensitive grid cell in the defect-sensitive area in response to the sum of the multiple weighted physical quantities of the grid cell being greater than the threshold. For example, the cell sensitivity index Si can be compared with a preset sensitivity threshold Sth (such as 0.7). If Si > Sth, then the grid cell i is marked as a defect-sensitive grid cell. In contrast, if Si < Sth, then the grid cell i is marked as a non-defect-sensitive grid cell. The weights w1, w2, w3, and the sensitivity threshold Sth can be dynamically adjusted according to the device type (such as MOSFET, FinFET) or process conditions (such as temperature, doping concentration), thereby improving the generality of the method.

[0050] Although the above has been described for three physical quantities: defect density, electric field strength, and carrier concentration gradient, the scope of the present disclosure is not limited thereto. For example, the physical quantity can at least also be one or more of the following: charge density, electric potential, carrier concentration, mobility, temperature, current density, bandgap width, bottom of the conduction band, top of the valence band. In addition, according to the embodiments of the present disclosure, fewer than three or more than three physical quantities can also be used to determine the defect-sensitive grid cell. Return to Figure 2 , in step 24, for the determined defect-sensitive grid cell, the defect energy level can be determined based on the distribution state of the defects. The defect distribution function can be integrated to obtain the cumulative distribution function of the defects in the grid cell. For example, an energy level Et can be randomly sampled from the defect distribution function Dit(E), and its probability density function P(E) is: where Ec and Ev respectively represent the energy level positions of the bottom of the conduction band and the top of the valence band. Then, the defect distribution function Dit(E) can be integrated to calculate the cumulative distribution function CDF(E) of the defects:

[0051] Subsequently, a uniform random number r ∈ [0, 1] can be generated by the Monte Carlo method, and further inverse transform sampling is performed to obtain the corresponding energy level (that is, by a numerical method (such as the bisection method) to solve for Et such that CDF(Et) = r). The determined energy level Et is the defect energy level sampled by the Monte Carlo method. For example, for defects with a distribution function existing within a certain energy level range, such as: Et can be generated based on numerical integration and inverse transform sampling through the above method.

[0052] In addition, in some embodiments, for a single energy level Et defect, its distribution function is: Dit(E)=D0·δ(E-Et) The probability density function P(E) is: P(E)=δ(E-Et) That is, all sampling occurs at a fixed energy level Et, so there is no need to generate random energy levels.

[0053] Next, after determining the energy level Et, you can refer to... Figure 4 Method 500 details the calculation of carrier capture and release times for the defect-sensitive mesh cell. For example, step 51 is first performed to determine the event probability of a defect capturing a carrier and the event probability of releasing the carrier. For example, the event probability of a defect capturing a carrier can be expressed as: Furthermore, the probability of a defect releasing charge carriers can be expressed as: Therefore, the total event probability of defect trapping and releasing carriers can be expressed as: Where τ cap τ rel These represent the capture and release time constants (in seconds), respectively. And E t For defect energy levels, E c It is the bottom energy level of the conduction band, and k B Represents Boltzmann's constant. T This indicates absolute temperature.

[0054] In step 52, the total event time interval is determined based on the event probabilities of capturing and releasing charge carriers. According to some embodiments, the total event time interval can be assumed to follow an exponential distribution f(Δt) = λexp(-λΔt) based on a Poisson process. Therefore, the total event interval Δt is: Where r represents a uniform random number generated by the Monte Carlo method, and its value ranges from 0 to 1.

[0055] Therefore, in step 52, it can be determined that every time time interval Δt has elapsed, it is considered that a defect capture or release of carriers has occurred, thereby updating the carrier concentration and defect charge, and determining the event type as carrier capture or release based on the event probability.

[0056] In step 53, the carrier concentration and the defect charge of the grid cell are updated based on the total event time interval. For example, the local carrier concentration *ni* and defect charge *Q* of the grid cell can be updated according to the event probability (carrier capture or release).trap,i .

[0057] The following explanation uses electrons as the charge carrier. If r < λ cap / λ, meaning that the carriers are considered to be trapped by defects, then the local carrier concentration can be calculated as ni = ni-1, and the defect charge can be calculated as Q. trap,i =Q trap,i +e, where e is the unit charge. If r ≥ λ cap / λ, meaning that carriers are assumed to be released by defects, then the local carrier concentration can be calculated as ni = ni + 1, and the defect charge can be calculated as Q. trap,i =Q trap,i –e, where e is the unit charge.

[0058] The following explanation uses holes as the charge carriers. If r < λ cap / λ, meaning that the carriers are considered to be trapped by defects, the local carrier concentration can be calculated as ni = ni-1, Q trap,i =Q trap,i -e, where e is the unit charge. If r ≥ λ cap / λ, meaning that carriers are assumed to be released by defects, then the local carrier concentration can be calculated as ni = ni + 1, and the defect charge can be calculated as Q. trap,i =Q trap,i +e, where e is the unit charge.

[0059] According to embodiments of this disclosure, deterministic methods can also be performed in conjunction with methods 200, 400, and 500 on other regions (such as non-defect-sensitive regions) in the simulation region that are determined not to belong to the defect-sensitive region. In other words, methods 200 to 500 can be used independently or in conjunction with deterministic methods. To be used in conjunction with deterministic methods, embodiments of this disclosure can improve the consistency of physical quantities at boundary grid point i by optimizing the physical quantities.

[0060] For example, after determining the defect-sensitive mesh cells located within the defect-sensitive region and the non-defect-sensitive mesh cells located outside the defect-sensitive region, the physical quantities at the boundary mesh point i that defines the defect-sensitive and non-defect-sensitive regions can be adjusted (i.e., synchronized). The following example illustrates this using linear interpolation of charge density. For instance, the charge density Q of adjacent meshes can be used... H (Sensitive area) and Q L Linear interpolation calculations are performed in the non-sensitive region to determine the adjusted charge density Q. sync : Q H and Q L d represents the charge density in the defect-sensitive region and the non-defect-sensitive region, respectively. H and d L represents the distance from the boundary grid point i to the grid in the defect-sensitive region and the non-defect-sensitive region, respectively.

[0061] Besides linear interpolation, averaging can also be performed on the physical quantities at node i of the boundary grid. The averaging of electric potential is illustrated below. For example, the averaging can be performed using the electric potential φ of adjacent grids. H (Electric potential in the defect-sensitive region) and φ L The potential φ at the boundary grid point i is determined by weighted averaging of the potentials in the non-defect-sensitive regions. Where φ H and φ L Let A represent the potentials of the defect-sensitive region and the non-defect-sensitive region, respectively, and A... H and A L These represent the area (two-dimensional) or volume (three-dimensional) of the grid cell adjacent to the boundary grid point i in the defect-sensitive region and the non-defect-sensitive region, respectively.

[0062] When using multiple physical quantities, such as carrier concentration, mobility, and charge density, each of these physical quantities can be optimized separately using interpolation methods (such as linear interpolation) or averaging methods (such as weighted averaging). This can improve the consistency of boundary physical quantities between defect-sensitive and non-defect-sensitive regions.

[0063] Since the macroscopic charge carried by defects affects carrier mobility, a carrier mobility correction method is also proposed according to the technical solution of this disclosure, applicable to methods 200 to 500 above and traditional determination methods. The corrected carrier mobility μ can be calculated using the following formula: Where μ0 represents the carrier mobility in defect-free conditions (unit: cm). 2 / (V·s)), Q trap Let N be the defect charge, and α represent an empirical correction factor (ranging from 0.5 to 1.0). Furthermore, N... eff Indicates effective doping concentration (unit: cm⁻¹) -3 It can be calculated based on the actual doping concentration N using the following formula: in E g Indicates the material's band gap width, andk B denoted by Boltzmann constant, and T represents absolute temperature.

[0064] It should be understood that the embodiments shown in the accompanying drawings are merely illustrative of some embodiments of this disclosure and are not intended to limit this disclosure. Embodiments of this disclosure may also have various other forms.

[0065] An electronic device is also disclosed in embodiments of this disclosure. The electronic device includes: a processor; and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: determining a defect-sensitive grid cell located in a defect-sensitive region among a plurality of grid cells of a simulation region of the semiconductor device; and determining a defect energy level for the defect-sensitive grid cell based on the distribution state of defects.

[0066] The embodiments of this disclosure also disclose a computer-readable storage medium having machine-executable instructions stored thereon, which, when executed by a processor, implement the method according to the embodiments of this disclosure.

[0067] The embodiments of this disclosure have at least the following advantages:

[0068] High-precision defect-state behavior modeling. Some embodiments of this disclosure simulate the trapping and releasing behavior of charge carriers by defects using the Monte Carlo method, overcoming the limitations of traditional deterministic methods for calculating macroscopic charge distribution. Through random energy level generation and event probability simulation, accurate quantification of the microscopic dynamics of defects is achieved, which is particularly suitable for complex energy level distributions (such as continuous distribution function defects).

[0069] Dynamic Sensitive Region Segmentation and Multi-Physical Quantity Collaborative Analysis. Embodiments of this disclosure advantageously enable automatic identification of defect-sensitive regions in devices based on the normalization of multiple physical quantities such as defect density, electric field strength, and carrier gradient. Through dynamic weight allocation and sensitivity threshold adjustment, the method of this disclosure can be adapted to different device structures (such as MOSFETs and FinFETs) and process conditions (such as temperature and doping concentration).

[0070] Dynamic correction of mobility by defect charge. The defect charge correction model for carrier mobility proposed in this disclosure can be based on material properties (such as bandgap). E g The calibration of process parameters significantly improves the accuracy of migration rate prediction, especially for defect-sensitive areas. Flexible scalability and user-defined functionality. The method disclosed herein supports the incorporation of multiple physical quantities (such as temperature, current density, and bandgap parameters) into defect sensitivity calculations, adapting to the device simulation needs under complex process conditions. Furthermore, the method disclosed herein also provides a user-defined sensitive region marking function to meet the rapid modeling needs of specific design scenarios, such as directly designating a region as a sensitive area to accelerate defect analysis in critical regions.

[0071] Figure 5 A schematic block diagram of a 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.

[0072] 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.

[0073] CPU 601 executes the various methods and processes described above. For example, according to Figures 2 to 4 The simulation methods 200 to 500 shown respectively can be implemented as computer software programs 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 described above for determining defect-sensitive regions and / or calculating and updating physical quantities can be performed. Alternatively, in other embodiments, CPU 601 can be configured by any other suitable means (e.g., by means of firmware) to execute any of the methods 200 to 500 according to this disclosure.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] Furthermore, although the operations are depicted 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.

[0078] 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 method for simulating semiconductor devices, comprising: Obtain the simulation region of the semiconductor device, the simulation region comprising multiple grid cells; For each of the plurality of mesh cells, obtain one or more physical quantities characterizing the simulation state of the mesh cell; as well as Based on the comparison of the one or more physical quantities with the corresponding thresholds, the corresponding grid cells among the plurality of grid cells are determined as defect-sensitive grid cells located in the defect-sensitive region; For the defect-sensitive mesh element, the defect energy level is determined based on the defect distribution state; as well as The probability of a defect capturing a carrier and the probability of a carrier releasing a carrier in the grid cell are determined based on the defect energy level.

2. The method according to claim 1, wherein determining the corresponding grid cell among the plurality of grid cells as a defect-sensitive grid cell located in the defect-sensitive region based on a comparison of the one or more physical quantities with a corresponding threshold comprises: The physical quantities of different types are normalized respectively to generate multiple normalized physical quantities of the grid cell; The corresponding weights are assigned to each of the plurality of normalized physical quantities to generate a plurality of weighted physical quantities for the mesh cell; as well as In response to the sum of the plurality of weighted physical quantities of the mesh cell being greater than the threshold, the mesh cell is determined to be a defect-sensitive mesh cell in the defect-sensitive region.

3. The method according to claim 2, further comprising generating random energy levels for defects in the mesh cells of the defect-sensitive region, including: Integrating the defect distribution function yields the cumulative defect distribution function of the mesh element; Generate random numbers; as well as The Monte Carlo sampling defect energy level is determined based on the random number and the cumulative distribution function.

4. The method according to claim 1, wherein the method further comprises: The total event time interval for determining the event probability of capturing carriers and the event probability of releasing carriers; as well as The concentration of charge carriers and the charge amount of defects in the grid cells are updated based on the total event time interval.

5. The method according to claim 1, further comprising: In response to the physical quantity being greater than the threshold, the corresponding grid cell among the plurality of grid cells is determined as the defect-sensitive grid cell located in the defect-sensitive region; In response to the physical quantity not being greater than the threshold, the corresponding grid cell among the plurality of grid cells is determined to be located in other regions outside the defect-sensitive region; A deterministic method is performed on the other regions in the simulation region that are determined to be located outside the defect-sensitive region.

6. The method according to claim 5, further comprising: Perform at least one of the following processes on the boundary defining the defect-sensitive region and the other regions to ensure that the physical quantities at the boundary remain consistent: Perform interpolation operations on the physical quantities of the mesh cells adjacent to the boundary; The physical quantities of the grid cells adjacent to the boundary are averaged.

7. The method of claim 6, wherein performing interpolation on the physical quantities of the mesh cells adjacent to the boundary comprises: The value of the physical quantity is determined by linear interpolation based on the distances between the first grid in the defect-sensitive area at the boundary and the second grid adjacent to the first grid in the other areas at the boundary and the points on the boundary.

8. The method of claim 6, wherein averaging the physical quantities of the grid cells adjacent to the boundary comprises: The value of the physical quantity is determined by a weighted average calculation based on the area or volume of the first grid in the defect-sensitive area at the boundary and the second grid adjacent to the first grid in other areas at the boundary.

9. The method according to claim 6, wherein the physical quantity includes one or more of the following: charge density, electric potential, carrier concentration, mobility, temperature, current density, bandgap width, conduction band bottom, valence band top, defect density, electric field strength, and carrier concentration gradient.

10. The method of claim 5, further comprising: The carrier mobility of the defect in the simulation region is corrected based on the following formula. μ : in μ 0 represents the carrier mobility when there are no defects. Q trap This represents the amount of charge in the defect. α This represents the correction factor, the correction factor α The value is between 0.5 and 1.

0. N eff Indicates the effective doping concentration.

11. The method according to claim 10, wherein, The effective doping concentration is calculated based on the following formula. N eff : Where N represents the actual doping concentration. E g Indicates the material's no-band width. T It represents absolute temperature, and k B This represents the Oltzmann constant.

12. The method according to claim 2, further comprising: The weights and thresholds are dynamically adjusted based on the type of semiconductor device and process conditions.

13. 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: Obtain the simulation region of the semiconductor device, the simulation region comprising multiple grid cells; For each of the plurality of mesh cells, obtain one or more physical quantities characterizing the simulation state of the mesh cell; Based on the comparison of the one or more physical quantities with the corresponding thresholds, the corresponding grid cells among the plurality of grid cells are determined as defect-sensitive grid cells located in the defect-sensitive region; For the defect-sensitive mesh element, the defect energy level is determined based on the defect distribution state; and The probability of a defect capturing a carrier and the probability of a carrier releasing a carrier in the grid cell are determined based on the defect energy level.

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

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