Reliability simulation method, reliability simulation device, simulation equipment and storage medium

Abstract

The present application discloses a reliability simulation method, a reliability simulation device, a simulation equipment and a non-volatile computer-readable storage medium. The method includes obtaining a periodic interference signal of an electromagnetic interference signal; performing a Gaussian mixture simulation on the obtained periodic interference signal of the electromagnetic interference signal to obtain a Gaussian mixture interference signal time domain function; converting the periodic interference signal into a rectangular pulse signal according to the Gaussian mixture interference signal time domain function and a preset acceleration factor model, wherein the preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under preset accelerated stress conditions, and the preset acceleration factor model satisfies the degradation consistency condition; and using the rectangular pulse signal as an input boundary condition, inputting it into a preset simulation software to simulate a preset number of times, so as to output the degradation rate of the transistor to be simulated, and the degradation rate is configured to characterize the reliability of the transistor to be simulated. In this way, the simulation result can accurately reflect the reliability of the transistor to be simulated.

Description

Technical Field

[0001] The present application relates to the technical field of transistor simulation, and more specifically, to a reliability simulation method, a reliability simulation apparatus, a simulation device, and a non-volatile computer-readable storage medium. Background Art

[0002] At present, power transistors are widely used in the circuit structure of industrial chips as representative semiconductor devices. Before the actual tape-out, simulation software, such as semiconductor process simulation and device simulation software (Technology Computer Aided Design, TCAD), is required to simulate the reliability of transistors, and the life of transistors is evaluated based on the calculation results. In the reliability evaluation of transistors under electromagnetic fields, the existing technical solutions mainly rely on the signal parameters of simpler pulses as transient simulation inputs. For example, for laterally diffused metal oxide semiconductor transistors, the signal parameters of periodic single transmission line pulses (Transmission Line Pulse, TLP) are usually relied on as transient simulation inputs. However, this kind of simulation condition is relatively simple and it is difficult to cover the common complex electromagnetic pulse environment of industrial chips, which makes the expected value of the reliability life of transistors differ greatly from the actual experience value, making it difficult to accurately evaluate the reliability of transistors. Summary of the invention

[0003] The embodiments of the present application provide a reliability simulation method, a reliability simulation apparatus, a simulation device and a non-volatile computer-readable storage medium, which can realize the equivalent conversion of complex electromagnetic interference signals to obtain rectangular pulse signals, and then use the rectangular pulse signals to realize simulation to improve the reliability of simulation and reduce simulation time.

[0004] The reliability simulation method of the embodiment of the present application includes obtaining a periodic interference signal of an electromagnetic interference signal, wherein the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals; performing Gaussian mixture simulation on the periodic interference signal to obtain a time domain function of the Gaussian mixture interference signal; converting the periodic interference signal into a rectangular pulse signal according to the time domain function of the Gaussian mixture interference signal and a preset acceleration factor model, wherein the preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under preset accelerated stress conditions, and the preset acceleration factor model satisfies a degradation consistency condition, wherein the degradation consistency condition is that for dynamic stress within a preset time length, the degradation of the effective electric field static stress and dynamic stress obtained by integration is consistent; and the rectangular pulse signal is input as an input boundary condition to a preset simulation software for a preset number of simulations to output a degradation rate of the transistor to be simulated, wherein the degradation rate is configured to characterize the reliability of the transistor to be simulated.

[0005] In some embodiments, the periodic interference signal is converted into a rectangular pulse signal according to the Gaussian mixture interference signal time domain function and the preset acceleration factor model, including: determining the preset acceleration factor model according to the preset stress parameters of the transistor to be simulated; obtaining an equivalent pulse voltage according to the degradation consistency condition, the preset acceleration factor model, the Gaussian mixture interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal; and determining the rectangular pulse signal according to the equivalent pulse voltage, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal.

[0006] In some embodiments, the preset acceleration factor model is determined based on the preset stress parameters of the transistor to be simulated, including: performing an accelerating electric field experiment on multiple test transistors at a constant temperature, and obtaining the actual failure time of each test transistor by applying a constant electric field of different intensities to each test transistor, wherein the specifications and models of the multiple test transistors are the same; taking the constant electric field of each test transistor and the corresponding actual failure time as a set of data, and obtaining a failure time model based on multiple sets of data fitting, wherein the specifications and models of the test transistors are the same as the specifications and models of the transistor to be simulated; obtaining preset stress parameters based on the failure time model and any one of the multiple sets of data; and obtaining the preset acceleration factor model based on the preset stress parameters, the effective electric field static stress, and the real electric field stress.

[0007] In some embodiments, the degenerate consistency condition includes: Wherein, P is the preset duration, is the preset acceleration factor model, t is time, and the preset duration is determined according to the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

[0008] In some embodiments, the step of acquiring a periodic interference signal of an electromagnetic interference signal includes: acquiring a signal characteristic period of the electromagnetic interference signal; and acquiring a periodic interference signal from the electromagnetic interference signal according to the signal characteristic period.

[0009] In some embodiments, the Gaussian mixture simulation of the periodic interference signal to obtain the time domain function of the Gaussian mixture interference signal includes: obtaining a target Gaussian mixture model corresponding to the number of peaks of the periodic interference signal; and using an expected maximum algorithm to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain the time domain function of the Gaussian mixture interference signal.

[0010] In certain embodiments, the expected maximum algorithm is used to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain the time domain function of the Gaussian mixture interference signal, including: converting the voltage intensity in the time domain of the periodic interference signal into a probability density in the time domain, wherein the probability density is determined based on the ratio of the voltage intensity value to the total voltage intensity value; obtaining the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model according to preset distribution data, wherein the Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions, and the optimal parameters include the weight, distribution mean and standard deviation of each Gaussian distribution function; and generating the Gaussian mixture interference signal time domain function based on the Gaussian mixture model probability function and the optimal parameters.

[0011] In certain embodiments, obtaining the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model based on preset distribution data includes: obtaining different parameter combinations, the parameter combination including the weight, distribution mean and standard deviation of each Gaussian distribution function; calculating the distribution probability of the preset distribution data belonging to each Gaussian distribution function under each parameter combination, and updating the parameter combination according to the distribution probability; determining a target parameter combination, the target parameter combination being the parameter combination whose error value of the parameter combination before and after the update is less than the preset error value.

[0012] In some embodiments, the rectangular pulse signal is used as an input boundary condition and input into a preset simulation software to simulate a preset number of times to output the degradation rate of the transistor to be simulated, including: determining multiple simulation points in the rectangular pulse signal; inputting the voltages corresponding to the multiple simulation points in sequence as the gate voltages of the transistor to be simulated, and performing a preset number of simulations, where one simulation is considered as the case where all the simulation points of the rectangular pulse signal are input; and detecting the current drain current of the transistor to be simulated after the preset number of simulations, and determining the degradation rate based on the current drain current and the initial drain current.

[0013] In some embodiments, the degradation rate is characterized by a first ratio of the current drain current to the initial drain current, or the degradation rate is characterized by a second ratio of a difference between the initial drain current and the current drain current to the initial drain current.

[0014] In some embodiments, the number of simulation points when simulating with the rectangular pulse signal is less than the number of simulation points when simulating with the periodic interference signal.

[0015] In some implementations, the simulation software performs simulation according to a preset reliability simulation model, and the reliability simulation model is determined according to the type of the transistor to be simulated.

[0016] In some embodiments, the transistor to be simulated includes a laterally diffused metal oxide semiconductor transistor, and the reliability simulation model includes a reaction diffusion model.

[0017] In some embodiments, the plurality of electromagnetic pulse signals are respectively generated by a plurality of gas insulated switchgears operating in parallel at the same frequency.

[0018] In some embodiments, the transistor to be simulated includes a diode or a triode.

[0019] The reliability simulation device of the embodiment of the present application includes an acquisition module, a simulation module, a conversion module and a simulation module. The acquisition module is used to acquire a periodic interference signal of an electromagnetic interference signal, and the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals. The simulation module is used to perform Gaussian mixture simulation on the periodic interference signal to obtain the time domain function of the Gaussian mixture interference signal; the conversion module is used to convert the periodic interference signal into a rectangular pulse signal according to the time domain function of the Gaussian mixture interference signal and a preset acceleration factor model, the preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under preset accelerated stress conditions, and the preset acceleration factor model satisfies the degradation consistency condition, and the degradation consistency condition is that the degradation of the effective electric field static stress and dynamic stress generated by the integral acquisition is consistent for the dynamic stress within the preset time length; the simulation module is used to input the rectangular pulse signal as an input boundary condition to the preset simulation software for a preset number of simulations to output the degradation rate of the transistor to be simulated, and the degradation rate is configured to characterize the reliability of the transistor to be simulated.

[0020] In some embodiments, the conversion module is also used to: determine the preset acceleration factor model based on the preset stress parameters of the transistor to be simulated; obtain an equivalent pulse voltage based on the degradation consistency condition, the preset acceleration factor model, the Gaussian mixed interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal; and determine the rectangular pulse signal based on the equivalent pulse voltage, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal.

[0021] In some embodiments, the conversion module is also used to: perform an accelerating electric field experiment on multiple test transistors at a constant temperature, obtain the actual failure time of each test transistor by applying a constant electric field of different intensities to each test transistor, wherein the specifications and models of the multiple test transistors are the same; use the constant electric field and the corresponding actual failure time of each test transistor as a set of data, and obtain a failure time model based on multiple sets of data fitting, wherein the specifications and models of the test transistors are the same as the specifications and models of the transistor to be simulated; obtain preset stress parameters based on the failure time model and any one of the multiple sets of data; and obtain the preset acceleration factor model based on the preset stress parameters, the effective electric field static stress and the real electric field stress.

[0022] In some embodiments, the degenerate consistency condition includes: Wherein, P is the preset duration, is the preset acceleration factor model, t is time, and the preset duration is determined according to the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

[0023] In some embodiments, the acquisition module is further used to acquire a signal characteristic period of the electromagnetic interference signal; and acquire a periodic interference signal from the electromagnetic interference signal according to the signal characteristic period.

[0024] In some embodiments, the simulation module is also used to obtain a target Gaussian mixture model corresponding to the number of peaks of the periodic interference signal based on the number of peaks of the periodic interference signal; and to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model using an expected maximum algorithm to obtain a time domain function of the Gaussian mixture interference signal.

[0025] In certain embodiments, the simulation module is also used to convert the voltage intensity in the time domain of the periodic interference signal into a probability density in the time domain, wherein the probability density is determined based on the ratio of the voltage intensity value to the total voltage intensity value; the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model are obtained based on preset distribution data, the Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions, and the optimal parameters include the weight, distribution mean and standard deviation of each Gaussian distribution function; and the Gaussian mixture interference signal time domain function is generated based on the Gaussian mixture model probability function and the optimal parameters.

[0026] In some embodiments, the simulation module is also used to obtain different parameter combinations, the parameter combination including the weight, distribution mean and standard deviation of each Gaussian distribution function; calculate the distribution probability of the preset distribution data belonging to each Gaussian distribution function under each parameter combination, and update the parameter combination according to the distribution probability; determine the target parameter combination, the target parameter combination being the parameter combination whose error value of the parameter combination before and after the update is less than the preset error value.

[0027] In some embodiments, the simulation module is also used to determine multiple simulation points in the rectangular pulse signal; input the voltages corresponding to the multiple simulation points in sequence as the gate voltages of the transistor to be simulated, and perform a preset number of simulations, where one simulation is performed when all the multiple simulation points of the rectangular pulse signal are input; and detect the current drain current of the transistor to be simulated after the preset number of simulations, and determine the degradation rate based on the current drain current and the initial drain current.

[0028] In some embodiments, the degradation rate is characterized by a first ratio of the current drain current to the initial drain current, or the degradation rate is characterized by a second ratio of a difference between the initial drain current and the current drain current to the initial drain current.

[0029] In some embodiments, the number of simulation points when simulating with the rectangular pulse signal is less than the number of simulation points when simulating with the periodic interference signal.

[0030] In some implementations, the simulation software performs simulation according to a preset reliability simulation model, and the reliability simulation model is determined according to the type of the transistor to be simulated.

[0031] In some embodiments, the transistor to be simulated includes a laterally diffused metal oxide semiconductor transistor, and the reliability simulation model includes a reaction diffusion model.

[0032] In some embodiments, the plurality of electromagnetic pulse signals are respectively generated by a plurality of gas insulated switchgears operating in parallel at the same frequency.

[0033] In some embodiments, the transistor to be simulated includes a diode or a triode.

[0034] The simulation device of the embodiment of the present application includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the reliability simulation method described in any of the above embodiments.

[0035] The non-volatile computer-readable storage medium of the embodiment of the present application includes a computer program. When the computer program is executed by a processor, the processor executes the reliability simulation method described in any of the above embodiments.

[0036] The reliability simulation method, reliability simulation device, simulation equipment and non-volatile computer-readable storage medium of the embodiment of the present application first obtain an electromagnetic interference signal formed by the superposition of multiple electromagnetic pulse signals, and then pre-process the electromagnetic interference signal to obtain a periodic interference signal of the electromagnetic interference signal. Then, a Gaussian mixture simulation is performed on the periodic interference signal. After the simulation, the Gaussian mixture interference signal time domain function corresponding to the periodic interference signal can be obtained to characterize the complex electromagnetic interference signal, so that the reliability simulation can be performed based on the Gaussian mixture interference signal time domain function in the future, thereby ensuring that the simulation effect is closer to the actual degradation effect. Then, an acceleration factor model that meets the degradation consistency condition can be preset, and the periodic interference signal can be converted according to the preset acceleration factor model to obtain a rectangular pulse signal. At this time, the stress generated by the periodic interference signal can be considered as dynamic stress, and the stress generated by the converted rectangular pulse signal can be considered as effective electric field static stress. The rectangular pulse signal retains the rising and falling characteristics of the signal, and the degradation caused by the effective electric field static stress and dynamic stress is consistent. Combined with the periodic interference signal, it can be understood that when the rectangular pulse signal and the electromagnetic interference signal are input to the simulation software, the degradation of the transistor is consistent, so the rectangular pulse signal can be used to simulate the transistor degradation in the future. Then, the rectangular pulse signal can be used as an input boundary condition, input into the preset simulation software, and simulate a preset number of times to obtain the degradation rate of the transistor to be simulated. It can be understood that the degradation effect of the rectangular pulse signal is consistent with the degradation effect of the electromagnetic interference signal, so the degradation rate obtained after simulation according to the rectangular pulse signal can be closer to the actual degradation rate, so that the reliability obtained by simulation can more accurately reflect the reliability of the transistor to be simulated. And compared with the large number of simulation points required for the simulation of electromagnetic interference signals, the rectangular pulse signal can use fewer simulation points to achieve simulation, which can reduce the simulation time and accurately and efficiently evaluate the reliability of the transistor to be simulated. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0038] Figure 1 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0039] Figure 2 is a schematic diagram of the structure of a simulation device of certain embodiments of the present application;

[0040] Figure 3 It is a schematic diagram of a use scenario of a reliability simulation method of certain embodiments of the present application;

[0041] Figure 4 It is a pulse signal conversion schematic diagram of a reliability simulation method in certain embodiments of the present application;

[0042] Figure 5 is a pulse signal simulation schematic diagram of a reliability simulation method in certain embodiments of the present application;

[0043] Figure 6 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0044] Figure 7 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0045] Figure 8 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0046] Fig. 9 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0047] Fig.10 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0048] Fig.11 It is a pulse data schematic diagram and a probability density data schematic diagram of a reliability simulation method in certain embodiments of the present application;

[0049] Fig.12 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0050] Fig.13 is a flow chart of a reliability simulation method of certain embodiments of the present application;

[0051] Fig.14 1 is a schematic diagram of drain current comparison and a schematic diagram of degradation rate comparison of a reliability simulation method in certain embodiments of the present application;

[0052] Fig.15 is a schematic diagram of a module of a reliability simulation device according to certain embodiments of the present application;

[0053] Fig.16 It is a schematic diagram of the connection status of a non-volatile computer-readable storage medium and a processor in certain embodiments of the present application. DETAILED DESCRIPTION

[0054] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and cannot be understood as limiting the embodiments of the present application.

[0055] Currently, power transistors can be widely used as a semiconductor device in the circuits of industrial chips. When applying transistors in circuits, staff also need to systematically optimize the transistor structure for different chip application environments to enhance the robustness of transistors in specific scenarios. Therefore, before actual tape-out (i.e., manufacturing chips through a series of process steps like an assembly line), staff need to use simulation software, such as semiconductor process simulation and device simulation software (Technology Computer Aided Design, TCAD) to simulate the reliability of transistors in order to evaluate the life of the transistor based on the calculation results.

[0056] The electromagnetic interference signal components of common industrial chip application environments are complex. Such electromagnetic interference signals may be difficult to characterize as simulation conditions and input into simulation software, or may cause reliability simulation to be time-consuming, resulting in severe challenges to the simulation of real transistor degradation. In the reliability assessment of transistors in electromagnetic fields, existing technical solutions mainly rely on the signal parameters of simpler pulses as transient simulation inputs. For example, for laterally diffused metal oxide semiconductor (LDMOS) transistors, the signal parameters of periodic single transmission line pulses (TLP) are currently usually relied on as transient simulation inputs. However, due to the simple simulation input conditions of this simulation method, the periodic single transmission line pulse cannot well reflect the real electromagnetic interference signal, which makes it difficult for the current simulation method to cover the common complex electromagnetic pulse environment of industrial chips, resulting in a huge difference between the expected value of the reliability life of the transistor and the actual experience value, which in turn makes it difficult to accurately assess the stability of the chip.

[0057] In order to solve the above technical problems, an embodiment of the present application provides a reliability simulation method.

[0058] The reliability simulation method of this application will be described in detail below:

[0059] See also Figure 1 and Figure 2 The embodiment of the present application provides a reliability simulation method, which includes:

[0060] Step 01: Obtain a periodic interference signal of an electromagnetic interference signal, where the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals;

[0061] Specifically, the reliability simulation method of the present application can be applied to a simulation device 100, which includes a processor 30. The processor 30 can be provided with simulation software 20, such as commercial TCAD software. The simulation software 20 can be used to perform reliability simulation on the transistor to be simulated, so as to evaluate the life of the transistor and the chip according to the simulation results. Among them, the transistor to be simulated can be a diode or a triode, for example, the transistor to be simulated is an LDMOS transistor, which has the advantages of low distortion, high withstand voltage, and extremely high output, and is widely used in the circuit structure of industrial chips as a representative device of power semiconductors.

[0062] There are many devices in the industrial chip application environment, so there may be multiple pulse signals acting on the chip and the transistors therein. Taking the common electromagnetic interference source in the electric power industry as an example, when multiple gas insulated switchgear (GAS insulated SWITCHGEAR, GIS) are running in parallel at the same frequency, each gas insulated switchgear may generate an electromagnetic pulse signal, so that multiple electromagnetic pulse signals are superimposed to generate an electromagnetic interference signal with complex components. In order to ensure the simulation effect, the processor 30 needs to determine the simulation input conditions based on the electromagnetic interference signal actually acting on the chip to ensure that the simulation results can be closer to the actual degradation effect. Therefore, the processor 30 needs to first obtain the electromagnetic interference signal. Taking the common electromagnetic interference source in the electric power industry as an example, the processor 30 can obtain the electromagnetic interference signal at the pin of the industrial chip. At this time, multiple electromagnetic pulse signals are generated by multiple gas insulated switchgear running in parallel at the same frequency. Figure 3 (a) is the time domain spectrum of the electromagnetic pulse signal caused by the high-frequency switch of the gas-insulated switch. From the figure, we can see the voltage interference on the chip pins caused by multiple devices running in parallel at the same frequency.

[0063] A single electromagnetic pulse signal can form an extremely short rising / falling edge and has a relatively high amplitude, so a single electromagnetic pulse signal conforms to the Gaussian distribution law in the time domain. Therefore, according to the voltage superposition theorem, the processor 30 can perform Gaussian mixture simulation on the electromagnetic interference signal to quantitatively analyze the electromagnetic interference signal. At the same time, the electromagnetic interference signal is periodic, and the signal strength in different periods remains relatively consistent as a whole. It can be understood that the Gaussian mixture interference signal time domain function corresponding to the periodic interference signal in different periods is roughly the same. Therefore, at this time, the processor 30 can obtain the periodic interference signal of the electromagnetic interference signal according to the signal characteristic period of the electromagnetic interference signal, for example Figure 3 (b) so that it is convenient to perform Gaussian mixture simulation based on the periodic interference signal later without performing Gaussian mixture simulation on the entire electromagnetic interference signal.

[0064] Step 02: Perform Gaussian mixture simulation on the periodic interference signal to obtain the time domain function of the Gaussian mixture interference signal;

[0065] Specifically, after obtaining the periodic interference signal, the processor 30 may perform Gaussian mixture simulation on the periodic interference signal to obtain a Gaussian mixture interference signal time domain function, and obtain a distribution curve of the periodic interference signal, such as Figure 3 (c) is a schematic diagram of the fitting process of the periodic interference signal. The light-colored curve is the fitting line segment generated during the fitting process. Figure 3 (d) is the distribution curve of the fitted periodic interference signal. In this way, the processor 30 completes the characterization of the periodic interference signal, thereby completing the characterization of the electromagnetic interference signal, so that reliability simulation can be performed based on the Gaussian mixture interference signal time domain function in the future, thereby ensuring that the simulation effect is closer to the actual degradation effect.

[0066] Step 03: According to the Gaussian mixed interference signal time domain function and the preset acceleration factor model, the periodic interference signal is converted into a rectangular pulse signal. The preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under the preset accelerated stress conditions. The preset acceleration factor model satisfies the degradation consistency condition. The degradation consistency condition is that for the dynamic stress within the preset time length, the degradation of the effective electric field static stress and dynamic stress obtained by integration is consistent;

[0067] Specifically, the reliability dynamic-static stress conversion theory can be used to convert complex periodic interference signals into single pulse signals (such as rectangular pulse signals) to reduce the complexity of simulation. Among them, the voltage of the periodic interference signal is not stable, so the stress generated by the periodic interference signal can be considered as dynamic stress; the voltage of the rectangular pulse signal is relatively stable, so the stress generated by the rectangular pulse signal can be considered as effective electric field static stress.

[0068] The conversion principle of reliability dynamic and static stress conversion theory is as follows:

[0069] The prerequisite for reliability dynamic-static stress conversion is that the stress change amplitude is relatively small, so that the dynamic stress only accelerates the original failure mechanism but does not change the original failure mechanism. Of course, the stress change amplitude is generally small during the simulation process. Therefore, reliability dynamic-static stress conversion can be directly performed in common simulation processes.

[0070] Effective electric field static stress ε eff The time increment d t 'The time increment d under the real electric field stress ε(t) (acceleration or deceleration conditions) t The relationship between can be written as in is the preset acceleration factor model, which is defined as the ratio between the expected failure time under normal working conditions and the actual failure time under preset accelerated stress conditions. At the same time, the preset acceleration factor model satisfies the degradation consistency condition. The degradation consistency condition is that for the dynamic stress within the preset time, the effective electric field static stress ε obtained by integration eff The degradation generated is consistent with the degradation generated by dynamic stress. The preset duration may be equal to the signal characteristic period of the electromagnetic interference signal, or the preset duration may be equal to the duration corresponding to the portion of the voltage intensity not being 0 in a single signal characteristic period of the electromagnetic interference signal.

[0071] It can be understood that after the reliability dynamic and static stress conversion is performed according to the Gaussian mixed interference signal time domain function and the preset acceleration factor model, the degradation effect of the rectangular pulse signal on the transistor to be simulated is consistent with the degradation effect of the periodic interference signal on the transistor to be simulated, so that a simpler rectangular pulse signal can be used to perform transistor degradation simulation later. Figure 4 The solid line in the figure is a periodic interference signal, and the dotted line is a rectangular pulse signal. The amplitude of the rectangular pulse signal obtained after conversion is 11.64V, and the pulse width is 250ns.

[0072] At the same time, please combine Figure 5 , Figure 5 The solid line in the figure is the pulse signal, and the dots are the simulation points. It can be understood that the more simulation points there are, the longer the simulation time will be. Figure 5 (a) is an excerpt of simulation points for reliability simulation based on a periodic interference signal in the electromagnetic interference signal. Figure 5 (b) is a selection of simulation points for reliability simulation based on rectangular pulse signals. Figure 5 As can be seen in (a), for a periodic interference signal, the voltage of the periodic interference signal is constantly changing, so that more simulation points need to be set when simulating the periodic interference signal to ensure the simulation effect, but this will result in a longer simulation time. Figure 5 As can be seen in (b), since the rectangular pulse signal is relatively stable, when the simulation software 20 simulates the rectangular pulse signal, the number of simulation points can be set to be less for the period of stable voltage, and more simulation points can be set when the voltage suddenly changes, so as to extract more details of the rectangular pulse signal under the voltage. This can ensure that the simulation software 20 can extract more complete details of the rectangular pulse signal, so that the simulation software 20 can simulate the rectangular pulse signal well, and can also reduce the simulation time. Therefore, for the rectangular pulse signal and the periodic interference signal of the same period, the number of simulation points when simulating with the rectangular pulse signal is less than the number of simulation points when simulating with the periodic interference signal, so that the simulation time when simulating with the rectangular pulse signal is shorter than the simulation time when simulating with the electromagnetic interference signal.

[0073] For example, the reliability simulation of LDMOS transistors is carried out in a transient solution mode, with an initial step size of 0.0001ns, a growth rate of 1.2, and a maximum step size of 100ns. For a periodic interference signal with a period of 300ns, the number of simulation points of a signal sample with a resolution of 1ns is 302 during the simulation process, and the simulation time is 26min. For a rectangular pulse signal, the number of simulation points within a period of 300ns is 103, and the simulation time is 8min16s. By comparison, it can be seen that the device reliability simulation time is shortened by 2 / 3 after equivalent replacement.

[0074] In summary, the reliability simulation based on the periodic interference signal and the reliability simulation based on the rectangular pulse signal have the same simulation effect, but the simulation time of the rectangular pulse signal is shorter than that of the periodic interference signal. Therefore, after obtaining the Gaussian mixed interference signal time domain function, the processor 30 will transform the electromagnetic interference signal collected in the experiment into dynamic and static stresses of reliability according to the Gaussian mixed interference signal time domain function and the preset acceleration factor model to obtain a simpler rectangular pulse signal. This can simplify the signal input parameters and reduce the complexity of the simulation on the one hand, and on the other hand, it can greatly shorten the simulation time while maintaining relatively consistent simulation accuracy, and achieve efficient and accurate simulation of reliability.

[0075] Step 04: The rectangular pulse signal is used as an input boundary condition and input into the preset simulation software 20 for simulation for a preset number of times to output the degradation rate of the transistor to be simulated, wherein the degradation rate is configured to characterize the reliability of the transistor to be simulated.

[0076] Specifically, after determining the rectangular pulse signal, the processor 30 may input the rectangular pulse signal as an input boundary condition into the preset simulation software 20, and the simulation software 20 may simulate the rectangular pulse signal to perform a preset number of simulations using the simulated rectangular pulse signal, for example, 2000 simulations. Then the processor 30 may determine the degradation rate of the transistor to be simulated after the preset number of simulations according to certain parameters, for example, the transistor to be simulated includes four electrodes, namely, a gate, a source, a drain, and a substrate, and the processor 30 may determine the degradation rate of the transistor to be simulated according to the gate voltage or the drain current of the transistor to be simulated.

[0077] The degradation rate is configured to characterize the reliability of the transistor to be simulated. For example, the processor 30 may obtain a drain current (i.e., an initial drain current) before the simulation software 20 performs reliability simulation, and obtain a drain current (i.e., a current drain current) again after the simulation software 20 completes a preset number of reliability simulations, and then the processor 30 determines the degradation rate of the transistor to be simulated based on the initial drain current and the current drain current. In some embodiments, the degradation rate is characterized by a first ratio of the current drain current to the initial drain current. The larger the current drain current, the larger the first ratio, which means that the transistor is less degraded, the smaller the degradation rate, and the higher the reliability. In other embodiments, the degradation rate is characterized by a second ratio of the difference between the initial drain current and the current drain current to the initial drain current. The larger the current drain current, the smaller the difference between the initial drain current and the current drain current, and the smaller the second ratio, which means that the transistor is less degraded, the smaller the degradation rate, and the higher the reliability.

[0078] The reliability simulation method of the embodiment of the present application first obtains an electromagnetic interference signal formed by the superposition of multiple electromagnetic pulse signals, and then pre-processes the electromagnetic interference signal to obtain a periodic interference signal of the electromagnetic interference signal. Then, a Gaussian mixture simulation is performed on the periodic interference signal. After the simulation, the Gaussian mixture interference signal time domain function corresponding to the periodic interference signal can be obtained to characterize the complex electromagnetic interference signal, so that the reliability simulation can be performed based on the Gaussian mixture interference signal time domain function in the future, thereby ensuring that the simulation effect is closer to the actual degradation effect. Then, an acceleration factor model that meets the degradation consistency condition can be preset, and the periodic interference signal can be converted according to the preset acceleration factor model to obtain a rectangular pulse signal. At this time, the stress generated by the periodic interference signal can be considered as dynamic stress, and the stress generated by the converted rectangular pulse signal can be considered as effective electric field static stress. The rectangular pulse signal retains the rising and falling characteristics of the signal, and the degradation caused by the effective electric field static stress and dynamic stress is consistent. Combined with the periodic interference signal, it can be understood that when the rectangular pulse signal and the electromagnetic interference signal are input to the simulation software 20, the degradation of the transistor is consistent, so the rectangular pulse signal can be used to perform transistor degradation simulation in the future. Then, the rectangular pulse signal can be used as an input boundary condition, input into the preset simulation software 20, and simulated for a preset number of times to obtain the degradation rate of the transistor to be simulated. It can be understood that the degradation effect of the rectangular pulse signal is consistent with the degradation effect of the electromagnetic interference signal, so the degradation rate obtained after simulation according to the rectangular pulse signal can be closer to the real degradation rate, so that the reliability obtained by simulation can more accurately reflect the reliability of the transistor to be simulated. And compared with the large number of simulation points required for the simulation of the electromagnetic interference signal, the rectangular pulse signal can use fewer simulation points to achieve simulation, which can reduce the simulation time and accurately and efficiently evaluate the reliability of the transistor to be simulated.

[0079] See also Figure 2 and Figure 6 In some embodiments, step 03: converting the periodic interference signal into a rectangular pulse signal according to the Gaussian mixture interference signal time domain function and the preset acceleration factor model, comprises:

[0080] Step 031: Determine a preset acceleration factor model according to preset stress parameters of the transistor to be simulated;

[0081] Step 032: Obtaining an equivalent pulse voltage according to the degradation consistency condition, the preset acceleration factor model, the Gaussian mixture interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal;

[0082] Step 033: Determine the rectangular pulse signal according to the equivalent pulse voltage, the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

[0083] Specifically, the preset stress parameter is a parameter related to the electric field in the preset failure time model. The preset stress parameter reflects the influence of different electric field stresses on the degradation of the simulated transistor. It can be understood that this preset stress parameter is indispensable in the dynamic-static stress conversion of reliability.

[0084] After obtaining the preset stress parameters, the processor 30 may determine a preset acceleration factor model according to the preset stress parameters and failure mechanism of the transistor to be simulated, and then the processor 30 may convert the signal according to the preset acceleration factor model and the degradation consistency condition.

[0085] For example, Figure 4 is the periodic interference signal and rectangular pulse signal corresponding to the LDMOS transistor. Transistor reliability simulation is based on defect theory. For LDMOS transistors, the interface state defects caused by the breakage of Si-H bonds at the silicon-oxygen interface inside the transistor have a severe impact on transistor degradation. At this time, the failure time model usually uses an exponential form to describe the field-induced impact. The acceleration factor model corresponding to the LDMOS transistor is

[0086]

[0087] Where ε(t) is the real electric field stress, ε eff is the effective electric field static stress, the effective electric field static stress ε eff In this case, ε(t) and the true electric field stress ε(t) represent the effective electric field intensity and the true electric field intensity, respectively.

[0088] Degenerate consistency conditions may include:

[0089]

[0090] Wherein, P is the effective duration of the signal, that is, the time range corresponding to the part where the voltage intensity of the signal is not 0, and t is the time.

[0091] By combining formula (1) and formula (2), and converting the electric field strength into voltage (for example, multiplying the electric field strength by the gate oxide thickness of the transistor to be simulated to obtain the corresponding voltage), the formula can be obtained:

[0092]

[0093] Among them, V(t) is the time domain function of the Gaussian mixed interference signal, γ is the preset stress parameter, is the equivalent pulse voltage, t a is the effective starting time of the periodic interference signal, t b is the effective termination time of the periodic interference signal, T ox is the gate oxide thickness of the transistor to be simulated, and the gate oxide thickness of the LDMOS transistor is 38nm. Figure 4 It can be found in a 30ns, t b When the processor 30 simulates the LDMOS transistor, the equivalent pulse voltage can be obtained by inputting all the data. is 11.64V, and then the processor 30 can obtain Figure 4 Rectangular pulse signal in .

[0094] Therefore, after obtaining the preset acceleration factor model, the processor 30 can link the degradation consistency condition with the preset acceleration factor model, and then obtain the equivalent pulse voltage according to the Gaussian mixed interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal and the effective end time of the periodic interference signal. Among them, the effective start time of the periodic interference signal is the moment when the voltage of the periodic interference signal starts to rise from 0, or a value very close to 0, and the effective end time of the periodic interference signal is the moment when the voltage of the periodic interference signal drops to 0 or a value very close to 0. The effective duration of the periodic interference signal is the time range corresponding to the part of the voltage intensity of the periodic interference signal that is not 0, that is, the time range between the effective start time and the effective end time of the periodic interference signal.

[0095] The effective duration before and after the signal conversion will not change. The effective start time of the periodic interference signal is the same as the effective start time of the rectangular pulse signal, and the effective end time of the periodic interference signal is the same as the effective end time of the rectangular pulse signal. The processor 30 can determine the effective start time of the rectangular pulse signal according to the effective start time of the periodic interference signal, and determine the effective end time of the rectangular pulse signal according to the effective end time of the periodic interference signal, thereby determining the waveform and pulse width of the rectangular pulse signal. For example, Figure 4 The effective start time is 30ns, the effective end time is 280ns, and the pulse width is 250ns.

[0096] In this way, the processor 30 combines the preset acceleration factor model and the degradation consistency condition, and accurately obtains the equivalent pulse voltage according to the Gaussian mixed interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal required in the combined formula. In combination with the equivalent pulse voltage, the effective start time, and the effective end time, the processor 30 can generate a converted rectangular pulse signal whose degradation effect is consistent with the degradation effect of the periodic interference signal.

[0097] See also Figure 2 and Figure 7 In some embodiments, step 031: determining a preset acceleration factor model according to preset stress parameters of the transistor to be simulated, including:

[0098] Step 0311: performing an accelerating electric field experiment on a plurality of test transistors at a constant temperature, and obtaining an actual failure time of each test transistor by applying a constant electric field of different strengths to each test transistor, wherein the specifications and models of the plurality of test transistors are the same;

[0099] Step 0312: taking the constant electric field of each test transistor and the corresponding actual failure time as a set of data, and obtaining a failure time model by fitting the multiple sets of data, wherein the specification and model of the test transistor are the same as the specification and model of the transistor to be simulated;

[0100] Step 0313: Obtaining a preset stress parameter according to the failure time model and any one of the multiple sets of data; and

[0101] Step 0314: Obtain a preset acceleration factor model according to preset stress parameters, effective electric field static stress, and true electric field stress.

[0102] Specifically, without changing the failure mechanism of the transistor, the processor 30 can accelerate the failure of the transistor to be simulated by strengthening the test conditions, so as to obtain necessary information in a shorter time, such as preset stress parameters, to evaluate the life indicators of the device under normal conditions.

[0103] First, the processor 30 performs an accelerated electric field experiment on multiple test transistors of the same specifications and models at a constant temperature. At this time, the processor 30 obtains the actual failure time of each test transistor by applying a constant electric field of different strengths. Then, the constant electric field of each test transistor and the corresponding actual failure time are used as a set of data, and a failure time model is obtained by fitting based on multiple sets of data, wherein the test transistor and the transistor to be simulated are of the same type to ensure the accuracy of the preset stress parameters obtained, thereby ensuring the accuracy of the rectangular pulse signal after subsequent conversion. After fitting the failure time model, the processor 30 can calculate the preset stress parameters based on the failure time model and a set of data.

[0104] For example, the test transistor is an LDMOS transistor, and the experimental conditions of the LDMOS transistor acceleration electric field experiment are simple and time-consuming. By applying a constant electric field of different strengths at the device end, the exponential failure time model TF=A can be obtained. 0 exp(-γε)exp(Q / k B T), where TF is the device failure time, A 0 is the process-related model factor coefficient, γ and Q are stress parameters in the exponential relationship, where γ is the coefficient related to the electric field (i.e., the preset stress parameter), Q is the coefficient related to temperature, ε is the applied stress field, and k B is the Boltzmann constant, and T is the temperature. After calculation, the stress parameter γ=2.663cm / MV is obtained from the accelerated electric field experiment of the LDMOS transistor.

[0105] Combined with the above formula (1), the processor 30 can obtain a preset acceleration factor model according to the preset stress parameters, effective electric field static stress and real electric field stress obtained through a simple electric field acceleration experiment.

[0106] In this way, the processor 30 can combine the fitted Gaussian mixed interference signal time domain function with the preset stress parameters obtained by the short constant electric field experiment to determine the preset acceleration factor model, so as to convert the complex electromagnetic interference signal into a simple rectangular pulse signal, thereby greatly shortening the simulation time and reducing the complexity of the simulation. At this time, the processor 30 can also use the experimental results (i.e., the preset stress parameters obtained by the experiment) to calibrate the acquired data, thereby improving the reliability of the simulation. At the same time, the device acceleration experiment under the constant electric field is short in time, so the reliability simulation method of the present application can greatly shorten the reliability simulation time.

[0107] See also Figure 2 and Figure 8 In some implementations, step 01: obtaining a periodic interference signal of an electromagnetic interference signal includes:

[0108] Step 011: Obtaining a signal characteristic period of an electromagnetic interference signal;

[0109] Step 012: Obtain a periodic interference signal from the electromagnetic interference signal according to the signal characteristic period.

[0110] Specifically, the pulse peak value of each pulse signal of the electromagnetic interference signal may fluctuate slightly due to environmental factors, that is, the pulse peak value of each pulse signal may be different, but the difference is not large, and the overall relative uniformity is maintained.

[0111] After acquiring the electromagnetic interference signal, the processor 30 may pre-process the electromagnetic interference signal, that is, sample and extract the electromagnetic interference signal to obtain the signal characteristic period of the electromagnetic interference signal, and at this time, the peak voltage of the electromagnetic interference signal may also be obtained. Then, the processor 30 obtains the periodic interference signal from the electromagnetic interference signal according to the signal characteristic period. For example, Figure 3 The periodic interference signal shown in (b) is from Figure 3 (a) is obtained from the electromagnetic interference signal shown, and the characteristic period of the electromagnetic interference signal is 300ns, the peak value is 16.1V, and the side peak-to-peak values ​​are 10.2V and 12.1V respectively. In this way, since the periodic interference signal is roughly the same as other interference signals in the electromagnetic interference signal, the Gaussian mixed interference signal time domain function subsequently determined according to the periodic interference signal can represent the Gaussian mixed interference signal time domain function of the electromagnetic interference signal, so that the processor 30 can complete the characterization of the electromagnetic interference signal for the periodic interference signal, thereby simplifying the Gaussian simulation process and speeding up the Gaussian simulation.

[0112] See also Figure 2 and Fig. 9 In some implementations, step 02: performing Gaussian mixture simulation on the periodic interference signal to obtain a Gaussian mixture interference signal time domain function, including:

[0113] Step 021: according to the number of peaks of the periodic interference signal, obtaining a target Gaussian mixture model corresponding to the number of peaks;

[0114] Step 022: Using the expected maximum algorithm, perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain the time domain function of the Gaussian mixture interference signal.

[0115] Specifically, the probability function of the Gaussian mixture model is represents a linear combination of K Gaussian distributions, with an overall parameter of Θ = {α 1 ,K,α K ,μ 1 ,K,μ K ,σ 1 ,K,σ K}. Among them, αi is the probability weighted value of the i-th Gaussian distribution, and represents the probability density of x under the i-th Gaussian distribution, represents the i-th Gaussian distribution, μ i is the mean of the distribution, σ i According to the voltage superposition theorem, the processor 30 obtains each Gaussian distribution function, and after weighted summing the obtained Gaussian distribution functions, the Gaussian mixture interference signal time domain function can be obtained, wherein since the time domain function is obtained, x in the above formula refers to time in this application.

[0116] The processor 30 may determine the number of K in the target Gaussian mixture model according to the number of peaks of the periodic interference signal, for example, Figure 3 (b) has three peaks. At this time, the processor 30 can determine that the target Gaussian mixture model is composed of three Gaussian distribution functions. Therefore, it can be determined that the target Gaussian mixture model is a 3-state Gaussian mixture model, that is, the K value in the target Gaussian mixture model is 3.

[0117] The Expectation Maximization (EM) algorithm is an algorithm for finding the maximum likelihood estimate or maximum a posteriori estimate of parameters in a probability model. The formula for the maximum likelihood function is: The processor 30 obtains the distribution data X={x 1 ,x 2 ,K,x N}, the distribution data is simulated by the Gaussian mixture model parameter θ. At this time, the processor 30 constructs the maximum likelihood function, and then calculates the likelihood function value based on the initial value of the parameter, and then adjusts the parameter according to the likelihood function value, and then calculates the likelihood function value according to the adjusted parameter value. After repeated iterative calculations, the parameter with the maximum likelihood function value can be considered as the optimal parameter The posterior probability indicates the probability that the event is caused by a certain factor under the condition that the event has already occurred. For example, the formula for the posterior probability is Where k = 1, 2, ..., N, i = 1, 2, ..., K. At this time, the processor 30 calculates the posterior probability based on the initial value of the parameter, adjusts the parameter according to the posterior probability, and then calculates the likelihood function value according to the adjusted parameter value. After repeated iterative calculations, the parameter with the maximum posterior probability can be considered as the optimal parameter. Alternatively, in the process of maximum likelihood estimation and maximum a posteriori estimation, when the parameter difference between two iterations is less than the error value, the parameter at this time can also be considered as the optimal parameter.

[0118] The processor 30 uses the expected maximum algorithm to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model, and the optimal parameters obtained are the parameters in the time domain function of the Gaussian mixture interference signal. After combining the optimal parameters obtained by the expected maximum algorithm and the target Gaussian mixture model, the processor 30 can obtain the Gaussian mixture interference signal time domain function corresponding to the periodic interference signal. It can be understood that the obtained Gaussian mixture interference signal time domain function also corresponds to the entire electromagnetic interference signal.

[0119] In this way, the processor 30 can use the number of peaks of the periodic interference signal to determine the target Gaussian mixture model that is suitable for the periodic interference signal, and use the expected maximum algorithm to obtain the optimal parameters of the target Gaussian mixture model, thereby determining the time domain function of the Gaussian mixture interference signal, so that the time domain function of the Gaussian mixture interference signal can be more consistent with the periodic interference signal, thereby improving the accuracy of subsequent reliability simulation.

[0120] See also Figure 2 and Fig.10 In some implementations, step 022: using an expected maximum algorithm to perform Gaussian mixture simulation on the periodic interference signal according to a target Gaussian mixture model to obtain a Gaussian mixture interference signal time domain function, including:

[0121] Step 0221: converting the voltage intensity of the periodic interference signal in the time domain into a probability density in the time domain, wherein the probability density is determined according to the ratio of the voltage intensity value to the total voltage intensity value;

[0122] Step 0222: deriving optimal parameters of a Gaussian mixture model probability function corresponding to the target Gaussian mixture model according to preset distribution data. The Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions. The optimal parameters include the weight, distribution mean, and standard deviation of each Gaussian distribution function.

[0123] Step 0223: Generate a Gaussian mixture interference signal time domain function based on the Gaussian mixture model probability function and optimal parameters.

[0124] Specifically, when performing Gaussian mixture simulation on the periodic interference signal based on the expected maximum algorithm, the processor 30 first needs to convert the voltage intensity of the periodic interference signal in the time domain into the probability density in the time domain. The processor 30 can perform nanosecond sampling and extraction on the pulse data of the periodic interference signal, and accumulate and obtain the total value of the voltage intensity. At this time, the probability density in the time domain can be calculated by the formula probability density = time domain voltage value / total value of voltage intensity, for example Fig.11 (a) shows the pulse data of a periodic interference signal. Fig.11 (b) shows the probability density in the time domain.

[0125] Then the processor 30 determines the number of Gaussian distribution functions in the Gaussian mixture model probability function according to the target Gaussian mixture model. For example, if the target Gaussian mixture model is a 3-state Gaussian mixture model, then it can be considered that the number of Gaussian distribution functions in the Gaussian mixture model probability function corresponding to the target Gaussian mixture model is 3. Then the processor 30 can randomly obtain the parameter value of the Gaussian distribution function, that is, randomly obtain the weight, distribution mean and standard deviation of each Gaussian distribution function, so as to obtain the Gaussian mixture model probability function by weighted summation according to one or more Gaussian distribution functions.

[0126] The periodic interference signal represents the changing trend of the voltage over time, so the preset distribution data can be a set of simulation time. After obtaining the Gaussian mixture model probability function, the processor 30 can obtain the optimal parameters of the Gaussian mixture model probability function according to the preset distribution data, the probability density corresponding to the preset distribution data, and the expected maximum value algorithm, and then adjust the parameters of the Gaussian mixture model probability function according to the optimal parameters, thereby generating a Gaussian mixture interference signal time domain function. It can be understood that the Gaussian mixture interference signal time domain function generated at this time has a high accuracy in describing the periodic interference signal, thereby ensuring that the subsequent simulation based on the Gaussian mixture interference signal time domain function is high in simulation accuracy.

[0127] In this way, the processor 30 can decompose and analyze the periodic interference signal based on the expected maximum algorithm to obtain the time domain function of the Gaussian mixture interference signal, thereby achieving the purpose of accurately characterizing the complex periodic interference signal.

[0128] See also Figure 2 and Fig.12 In some embodiments, step 0222: obtaining the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model according to the preset distribution data, includes:

[0129] Step 02221: obtaining different parameter combinations, the parameter combinations including the weight, distribution mean and standard deviation of each Gaussian distribution function;

[0130] Step 02222: Calculate the distribution probability of each Gaussian distribution function of the preset distribution data under each parameter combination, and update the parameter combination according to the distribution probability;

[0131] Step 02223: Determine a target parameter combination, where the target parameter combination is a parameter combination whose error value between the parameter combination before and after the update is less than a preset error value.

[0132] Specifically, after determining the number of Gaussian distribution functions, the processor 30 randomly obtains a parameter combination of each Gaussian distribution function, where the parameter combination includes a weight, a distribution mean, and a standard deviation, and the sum of the weights of each Gaussian distribution function is 1.

[0133] The processor 30 calculates the distribution probability of each preset distribution data belonging to the Gaussian distribution function corresponding to the parameter combination under each parameter combination, and updates the parameter combination according to the distribution probability. For example, according to the formula Where k = 1, 2, ..., N, i = 1, 2, ..., K, the distribution probability of each preset distribution data coming from the Gaussian distribution function corresponding to a certain parameter combination is calculated. Then according to the formula and Update the parameters in the parameter combination. After updating the parameters, the processor 30 calculates again the distribution probability of each preset distribution data from the Gaussian distribution function corresponding to a certain parameter combination, and then updates the parameters again according to the calculated distribution probability, and repeats the steps of calculating the distribution probability and updating the parameters until the difference between the parameter combinations between the two iterations (i.e., the error value of the parameter combination before and after the update) is less than the preset error value, and the fitting is considered complete. The parameter combination at this time is the target parameter combination, and the processor 30 can adjust the corresponding Gaussian distribution function parameter combination according to the target parameter combination, generate the Gaussian mixture model probability function after parameter adjustment, and then generate the Gaussian mixture interference signal time domain function according to the Gaussian mixture model probability function after parameter adjustment.

[0134] After determining the target parameter combination, the processor 30 can adjust the parameters of the Gaussian mixture model probability function according to the target parameter combination, and then convert the parameter-adjusted Gaussian mixture model probability function into an electrical signal to complete the signal analysis of the periodic interference signal and obtain the pulse data of the periodic interference signal, so as to facilitate the subsequent reliability dynamic and static stress conversion according to the pulse data. For example, the processor 30 can obtain the pulse data of the periodic electromagnetic interference signal corresponding to each preset distribution data according to the formula: periodic electromagnetic signal = probability density of Gaussian mixture model probability function * total voltage intensity.

[0135] In addition, after obtaining each Gaussian distribution function, the processor 30 also multiplies each Gaussian distribution function by the total voltage intensity value, and then performs weighted summation on the product to obtain a Gaussian mixed interference signal time domain function. For example, the Gaussian mixed interference signal time domain function is: Where V(t) is the Gaussian mixture interference signal time domain function, t is time, and the value of more than 500 in each Gaussian distribution function is the product of the total voltage intensity and the weight of the corresponding Gaussian distribution function. In this way, the processor 30 can improve the correlation between the Gaussian mixture interference signal time domain function and the pulse data, and avoid the data obtained by the Gaussian mixture interference signal time domain function being too small, which leads to subsequent calculation difficulties.

[0136] In this way, through continuous iterative calculation, the processor 30 can obtain the optimal parameter combination of each Gaussian distribution function, so that the Gaussian mixed interference signal time domain function obtained according to the Gaussian distribution function can more accurately characterize the probability function of the electromagnetic interference signal.

[0137] See also Figure 2 and Fig.13 In some embodiments, step 04: taking the rectangular pulse signal as an input boundary condition, inputting it into the preset simulation software 20 to simulate a preset number of times to output the degradation rate of the transistor to be simulated, comprises:

[0138] Step 041: determining a plurality of simulation points in the rectangular pulse signal;

[0139] Step 042: sequentially inputting the voltages corresponding to the plurality of simulation points as the gate voltages of the transistor to be simulated, and performing a preset number of simulations. When the plurality of simulation points of the rectangular pulse signal are all inputted, it is considered one simulation;

[0140] Step 043: Detect the current drain current of the transistor to be simulated after a preset number of simulations, and determine the degradation rate according to the current drain current and the initial drain current.

[0141] Specifically, before performing degradation simulation, the processor 30 may first perform a quasi-static simulation on the transistor to be simulated that has not been subjected to degradation simulation to determine the initial drain current, wherein the quasi-static simulation is a detection method that can obtain the electrical characteristic curve of the transistor (for example, the characteristic curve of the drain voltage and the drain current can be obtained), and at the same time, it is considered to be static during the quasi-static simulation process and will not cause the degradation of the transistor, so the quasi-static simulation is generally used for electrical test simulation. When simulating a rectangular pulse signal, the pulse device will determine multiple simulation points based on the rectangular pulse signal, and then sequentially input the voltages corresponding to the multiple simulation points as the gate voltage of the transistor to be simulated, and perform a preset number of simulations, wherein only when the multiple simulation points of the rectangular pulse signal are input, can it be considered that a simulation is completed, so that the transistor to be simulated is degraded. After simulating the preset number of times, the processor 30 may perform a quasi-static simulation on the transistor to be simulated that has been degraded to detect the current drain current of the transistor to be simulated. Then, the processor 30 can determine the degradation rate based on the current drain current and the initial drain current to evaluate the reliability of the transistor to be simulated.

[0142] Please combine Fig.14(a), the horizontal axis is the drain voltage (Vd), the vertical axis is the drain current (Id), the data in the figure are the data obtained after quasi-static simulation when in the on state (gate voltage (Vg) = 5V), the curve with a five-pointed star represents the initial drain current, the curve with a triangle represents the electromagnetic interference signal as the boundary condition, and the current drain current obtained after 2000 simulation cycles, and the curve with a circle represents the current drain current obtained after 2000 simulation cycles with a rectangular pulse signal as the boundary condition. It can be seen that after 2000 degradation cycles, whether it is the simulation with the electromagnetic interference signal as the boundary condition or the simulation with the rectangular pulse signal as the boundary condition, the corresponding current drain current has decreased relative to the initial drain current, so it can be proved that after both simulations, the transistor to be simulated has degraded.

[0143] Fig.14 (b) The system organizes the degradation of the working state current simulation of the saturation region of the LDMOS transistor (Vg = 5V, Vd = 20V). The horizontal axis in the figure is the number of cycles (Cycle), and the vertical axis is the degradation rate (ΔId). In order to make an effective comparison, the traditional TLP simulation model is also introduced in the figure for reference. The traditional TLP simulation model usually uses the half-width method (Full-width at the half of the maximum, FWHM) to process complex electromagnetic signals. At this time, the pulse width is 135ns and the pulse amplitude is 16.1V. The curve with a five-pointed star in the figure represents the change of the degradation rate when the traditional TLP simulation model is simulated, the curve with a triangle label represents the reliability simulation model with electromagnetic interference signal as the boundary condition, and the change of the degradation rate when the simulation is performed. The curve with a circular label represents the reliability simulation model with rectangular pulse signal as the boundary condition, and the change of the degradation rate when the simulation is performed. The electromagnetic interference signal is directly obtained from the environment, so the degradation simulation based on the electromagnetic interference signal can largely characterize the degradation of the transistor in actual use. By comparing the three curves, it can be seen that the degradation rate of the reliability simulation model that introduces electromagnetic interference signals or their rectangular pulse signals as simulation boundary conditions is less than 1%, and the difference between the two is 0.06%. The degradation rate of the reliability simulation model current after 2000 stress cycles using the traditional TLP simulation model is 1.87%, which is a significant increase compared to the degradation rate of the reliability simulation model that introduces electromagnetic interference signals as simulation boundary conditions. This will lead to the simulation life of LDMOS transistors being underestimated when evaluating the simulation life of LDMOS transistors based on the simulation results of the traditional TLP simulation model.

[0144] Therefore, combined with Fig.14 (a) and Fig.14(b) It can be seen that the reliability simulation model that introduces a rectangular pulse signal as a boundary condition can indeed cause the degradation of the transistor to be simulated, and the simulation result of the reliability simulation model is highly accurate, so that the processor 30 can efficiently and accurately evaluate the life of the transistor to be simulated. It can be understood that the present application can actually provide a more efficient and accurate reliability simulation method. In addition, the present application can also be applied to the mainstream TCAD simulation software 20 as an electromagnetic multi-physics field targeted simulation module to perform reliability simulation on the transistor to be simulated, thereby improving the reliability simulation accuracy of industrial chip devices.

[0145] See also Figure 2 In some implementations, the simulation software 20 performs simulation according to a preset reliability simulation model, and the reliability simulation model is determined according to the type of transistor to be simulated.

[0146] Specifically, the simulation software 20 will perform simulation according to a preset reliability simulation model, and the failure mechanisms of different types of transistors to be simulated may be different, and the reliability simulation models corresponding to different failure mechanisms may also be different. Therefore, the simulation software 20 also needs to determine a suitable reliability simulation model according to the type of transistor to be simulated, so as to use a suitable reliability simulation model to more accurately describe the degradation physical process of the transistor to be simulated, thereby ensuring the simulation effect of the simulation software 20.

[0147] For example, for LDMOS transistors, the degradation of device electrical performance is reflected at the microscopic level as the accumulation of internal structural defect states over time. Among them, the growth of crystal bond defects caused by the breakage of Si-H bonds on the device heterojunction Si / SiO2 is crucial to the performance of the device. Therefore, the simulation software 20 can use a reaction diffusion model to describe the degradation mechanism of the device heterojunction surface, and based on this, perform a reliability degradation curve simulation of the LDMOS transistor.

[0148] See also Fig.15In order to facilitate the better implementation of the reliability simulation method of the embodiment of the present application, the embodiment of the present application also provides a reliability simulation device 10. The reliability simulation device 10 may include an acquisition module 11, a simulation module 12, a conversion module 13 and a simulation module 14. The acquisition module 11 is used to obtain a periodic interference signal of an electromagnetic interference signal, and the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals. The simulation module 12 is used to perform Gaussian mixture simulation on the periodic interference signal to obtain the time domain function of the Gaussian mixture interference signal. The conversion module 13 is used to convert the periodic interference signal into a rectangular pulse signal according to the time domain function of the Gaussian mixture interference signal and a preset acceleration factor model. The preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under preset accelerated stress conditions. The preset acceleration factor model satisfies the degradation consistency condition. The degradation consistency condition is that for the dynamic stress within the preset time length, the degradation of the effective electric field static stress and dynamic stress generated by the integral acquisition is consistent. The simulation module 14 is used to input the rectangular pulse signal as an input boundary condition to the preset simulation software 20 for simulation for a preset number of times to output the degradation rate of the transistor to be simulated, and the degradation rate is configured to characterize the reliability of the transistor to be simulated.

[0149] The conversion module 13 is specifically used to determine a preset acceleration factor model according to preset stress parameters of the transistor to be simulated; obtain an equivalent pulse voltage according to a degradation consistency condition, a preset acceleration factor model, a Gaussian mixed interference signal time domain function, a gate oxide thickness of the transistor to be simulated, an effective start time of the periodic interference signal, and an effective end time of the periodic interference signal; and determine a rectangular pulse signal according to the equivalent pulse voltage, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal.

[0150] The conversion module 13 is specifically used to perform an accelerating electric field experiment on multiple test transistors at a constant temperature, and obtain the actual failure time of each test transistor by applying a constant electric field of different intensities to each of the test transistors, wherein the specifications and models of the multiple test transistors are the same; the constant electric field of each test transistor and the corresponding actual failure time are used as a set of data, and a failure time model is obtained according to the multiple sets of data fitting, wherein the specifications and models of the test transistors are the same as the specifications and models of the transistors to be simulated; the preset stress parameters are obtained according to the failure time model and any one of the multiple sets of data; and the preset acceleration factor model is obtained according to the preset stress parameters, the effective electric field static stress and the real electric field stress.

[0151] The acquisition module 11 is specifically used to acquire the signal characteristic period of the electromagnetic interference signal; and acquire the periodic interference signal from the electromagnetic interference signal according to the signal characteristic period.

[0152] The simulation module 12 is specifically used to obtain a target Gaussian mixture model corresponding to the number of peaks of the periodic interference signal according to the number of peaks of the periodic interference signal; and to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model using an expected maximum algorithm to obtain a time domain function of the Gaussian mixture interference signal.

[0153] The simulation module 12 is specifically used to convert the voltage intensity in the time domain of the periodic interference signal into a probability density in the time domain, and the probability density is determined according to the ratio of the voltage intensity value to the total voltage intensity value; the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model are obtained according to the preset distribution data, and the Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions, and the optimal parameters include the weight, distribution mean and standard deviation of each Gaussian distribution function; according to the Gaussian mixture model probability function and the optimal parameters, a Gaussian mixture interference signal time domain function is generated.

[0154] The simulation module 12 is specifically used to obtain different parameter combinations, the parameter combinations including the weight, distribution mean and standard deviation of each Gaussian distribution function; calculate the distribution probability of the preset distribution data belonging to each Gaussian distribution function under each parameter combination, and update the parameter combination according to the distribution probability; determine the target parameter combination, the target parameter combination is the parameter combination whose error value before and after the update is less than the preset error value.

[0155] The simulation module 14 is specifically used to determine multiple simulation points in the rectangular pulse signal; input the voltages corresponding to the multiple simulation points as the gate voltage of the transistor to be simulated in sequence, and perform a preset number of simulations. The simulation is considered to be one time when all the simulation points of the rectangular pulse signal are input; detect the current drain current of the transistor to be simulated after the preset number of simulations, and determine the degradation rate based on the current drain current and the initial drain current.

[0156] The reliability simulation device 10 is described above from the perspective of functional modules in conjunction with the accompanying drawings. The functional modules can be implemented in hardware form, can be implemented in software form, or can be implemented in combination with hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor 30, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware coding processor for execution, or can be completed by a combination of hardware and software modules in the coding processor. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor 30 reads the information in the memory and completes the steps in the above method embodiment in combination with its hardware.

[0157] See also Figure 2 The simulation device 100 of the embodiment of the present application includes a memory 40 and a processor 30. The memory 40 stores a computer program. When the computer program is executed by the processor 30, the processor 30 executes the reliability simulation method of any of the above embodiments. For the sake of brevity, it will not be repeated here.

[0158] See also Fig.16 The embodiment of the present application also provides a computer-readable storage medium 300 on which a computer program 310 is stored. When the computer program 310 is executed by the processor 30, the steps of the reliability simulation method of any of the above-mentioned embodiments are implemented. For the sake of brevity, they are not repeated here.

[0159] In the description of this specification, the descriptions with reference to the terms "certain embodiments", "in an example", "exemplarily", etc., mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0160] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0161] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A reliability simulation method, characterized in that: include: Acquire a periodic interference signal of an electromagnetic interference signal, wherein the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals; Performing Gaussian mixture simulation on the periodic interference signal to obtain a Gaussian mixture interference signal time domain function; According to the Gaussian mixed interference signal time domain function and the preset acceleration factor model, the periodic interference signal is converted into a rectangular pulse signal, the preset acceleration factor model is the ratio of the expected failure time under normal working conditions to the actual failure time under the preset accelerated stress condition, and the preset acceleration factor model satisfies the degradation consistency condition, and the degradation consistency condition is that for the dynamic stress within the preset time length, the effective electric field static stress and dynamic stress generated by the integral acquisition are consistent; and The rectangular pulse signal is used as an input boundary condition and input into a preset simulation software to simulate a preset number of times, so as to output a degradation rate of the transistor to be simulated, wherein the degradation rate is configured to characterize the reliability of the transistor to be simulated; The step of converting the periodic interference signal into a rectangular pulse signal according to the Gaussian mixture interference signal time domain function and a preset acceleration factor model includes: Determining the preset acceleration factor model according to the preset stress parameters of the transistor to be simulated; Obtaining an equivalent pulse voltage according to the degradation consistency condition, the preset acceleration factor model, the Gaussian mixture interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal, and the effective end time of the periodic interference signal; and The rectangular pulse signal is determined according to the equivalent pulse voltage, the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

2. The reliability simulation method according to claim 1, characterized in that: The step of determining the preset acceleration factor model according to the preset stress parameters of the transistor to be simulated includes: Performing an accelerated electric field experiment on a plurality of test transistors at a constant temperature, and obtaining an actual failure time of each test transistor by applying a constant electric field of different strengths to each test transistor, wherein the specifications and models of the plurality of test transistors are the same; The constant electric field and the corresponding actual failure time of each test transistor are taken as a set of data, and a failure time model is obtained by fitting the multiple sets of data, wherein the specification and model of the test transistor are the same as the specification and model of the transistor to be simulated; Obtaining a preset stress parameter according to the failure time model and any one of the multiple sets of data; and The preset acceleration factor model is obtained according to the preset stress parameter, the effective electric field static stress and the real electric field stress.

3. The reliability simulation method according to claim 1, characterized in that: The degenerate consistency conditions include: Wherein, P is the preset duration, is the preset acceleration factor model, t is time, and the preset duration is determined according to the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

4. The reliability simulation method according to claim 1, characterized in that: The step of obtaining a periodic interference signal of an electromagnetic interference signal comprises: Acquiring a signal characteristic period of the electromagnetic interference signal; and A periodic interference signal is obtained from the electromagnetic interference signal according to the signal characteristic period.

5. The reliability simulation method according to claim 1, characterized in that: The performing Gaussian mixture simulation on the periodic interference signal to obtain a Gaussian mixture interference signal time domain function includes: According to the number of peaks of the periodic interference signal, obtaining a target Gaussian mixture model corresponding to the number of peaks; and An expected maximum algorithm is adopted to perform Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain a time domain function of the Gaussian mixture interference signal.

6. The reliability simulation method according to claim 5, characterized in that: The method of adopting the expected maximum algorithm and performing Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain a Gaussian mixture interference signal time domain function includes: Converting the voltage intensity of the periodic interference signal in the time domain into a probability density in the time domain, wherein the probability density is determined according to the ratio of the voltage intensity value to the total voltage intensity value; Obtaining optimal parameters of a Gaussian mixture model probability function corresponding to the target Gaussian mixture model according to preset distribution data, wherein the Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions, and the optimal parameters include a weight, a distribution mean, and a standard deviation of each Gaussian distribution function; and The Gaussian mixture interference signal time domain function is generated according to the Gaussian mixture model probability function and the optimal parameters.

7. The reliability simulation method according to claim 6, characterized in that: The step of obtaining the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model according to the preset distribution data includes: Obtaining different parameter combinations, wherein the parameter combinations include a weight, a distribution mean, and a standard deviation of each of the Gaussian distribution functions; Calculating the distribution probability of the preset distribution data belonging to each of the Gaussian distribution functions under each of the parameter combinations, and updating the parameter combination according to the distribution probability; A target parameter combination is determined, where the target parameter combination is the parameter combination whose error value between the parameter combination before and after the update is less than a preset error value.

8. The reliability simulation method according to claim 1, characterized in that: The rectangular pulse signal is used as an input boundary condition and input into a preset simulation software to simulate a preset number of times to output the degradation rate of the transistor to be simulated, including: determining a plurality of simulation points in the rectangular pulse signal; Inputting the voltages corresponding to the plurality of simulation points as the gate voltages of the transistor to be simulated in sequence, and performing a preset number of simulations, wherein one simulation is considered as the case where the plurality of simulation points of the rectangular pulse signal are all inputted; and The current drain current of the transistor to be simulated after a preset number of simulations is detected, and the degradation rate is determined according to the current drain current and the initial drain current.

9. The reliability simulation method according to claim 8, characterized in that: The degradation rate is characterized by a first ratio of the current drain current to the initial drain current, or the degradation rate is characterized by a second ratio of a difference between the initial drain current and the current drain current to the initial drain current.

10. The reliability simulation method according to claim 1, characterized in that: The number of simulation points when simulating with the rectangular pulse signal is less than the number of simulation points when simulating with the periodic interference signal.

11. The reliability simulation method according to claim 1, characterized in that: The simulation software performs simulation according to a preset reliability simulation model, and the reliability simulation model is determined according to the type of the transistor to be simulated.

12. The reliability simulation method according to claim 11, characterized in that: The transistor to be simulated includes a laterally diffused metal oxide semiconductor transistor, and the reliability simulation model includes a reaction diffusion model.

13. The reliability simulation method according to claim 1, characterized in that: The multiple electromagnetic pulse signals are respectively generated by multiple gas-insulated switchgears operating in parallel at the same frequency.

14. The reliability simulation method according to claim 1, characterized in that: The transistor to be simulated includes a diode or a triode.

15. A reliability simulation device, characterized in that: The reliability simulation device comprises: An acquisition module, used for acquiring a periodic interference signal of an electromagnetic interference signal, wherein the electromagnetic interference signal is formed by superposition of multiple electromagnetic pulse signals; A simulation module, used for performing Gaussian mixture simulation on the periodic interference signal to obtain a Gaussian mixture interference signal time domain function; a conversion module, configured to convert the periodic interference signal into a rectangular pulse signal according to the Gaussian mixed interference signal time domain function and a preset acceleration factor model, wherein the preset acceleration factor model is a ratio of an expected failure time under normal working conditions to an actual failure time under a preset accelerated stress condition, and the preset acceleration factor model satisfies a degradation consistency condition, wherein the degradation consistency condition is that for dynamic stress within a preset time length, the generation of effective electric field static stress and dynamic stress obtained by integration is consistent; and A simulation module, used to input the rectangular pulse signal as an input boundary condition into a preset simulation software for a preset number of simulations to output a degradation rate of the transistor to be simulated, wherein the degradation rate is configured to characterize the reliability of the transistor to be simulated; Among them, the conversion module is also used to: determine the preset acceleration factor model according to the preset stress parameters of the transistor to be simulated; obtain the equivalent pulse voltage according to the degradation consistency condition, the preset acceleration factor model, the Gaussian mixed interference signal time domain function, the gate oxide thickness of the transistor to be simulated, the effective start time of the periodic interference signal and the effective end time of the periodic interference signal; and determine the rectangular pulse signal according to the equivalent pulse voltage, the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

16. The reliability simulation device according to claim 15, characterized in that: The conversion module is also used to: perform an accelerating electric field experiment on multiple test transistors at a constant temperature, and obtain the actual failure time of each test transistor by applying a constant electric field of different strengths to each test transistor, wherein the specifications and models of the multiple test transistors are the same; use the constant electric field of each test transistor and the corresponding actual failure time as a set of data, and obtain a failure time model based on multiple sets of data fitting, wherein the specifications and models of the test transistors are the same as the specifications and models of the transistor to be simulated; obtain preset stress parameters based on the failure time model and any one of the multiple sets of data; and obtain the preset acceleration factor model based on the preset stress parameters, the effective electric field static stress, and the real electric field stress.

17. The reliability simulation device according to claim 15, characterized in that: The degenerate consistency conditions include: Wherein, P is the preset duration, is the preset acceleration factor model, t is time, and the preset duration is determined according to the effective start time of the periodic interference signal and the effective end time of the periodic interference signal.

18. The reliability simulation device according to claim 15, characterized in that: The acquisition module is further used to acquire a signal characteristic period of the electromagnetic interference signal; and to acquire a periodic interference signal from the electromagnetic interference signal according to the signal characteristic period.

19. The reliability simulation device according to claim 15, characterized in that: The simulation module is also used to obtain a target Gaussian mixture model corresponding to the number of peaks of the periodic interference signal according to the number of peaks of the periodic interference signal; and to use an expected maximum algorithm to perform a Gaussian mixture simulation on the periodic interference signal according to the target Gaussian mixture model to obtain a time domain function of the Gaussian mixture interference signal.

20. The reliability simulation device according to claim 19, characterized in that: The simulation module is also used to convert the voltage intensity in the time domain of the periodic interference signal into a probability density in the time domain, and the probability density is determined according to the ratio of the voltage intensity value to the total voltage intensity value; the optimal parameters of the Gaussian mixture model probability function corresponding to the target Gaussian mixture model are obtained according to preset distribution data, and the Gaussian mixture model probability function is obtained by weighted summation of one or more Gaussian distribution functions, and the optimal parameters include the weight, distribution mean and standard deviation of each Gaussian distribution function; and the Gaussian mixture interference signal time domain function is generated according to the Gaussian mixture model probability function and the optimal parameters.

21. The reliability simulation device according to claim 20, characterized in that: The simulation module is also used to obtain different parameter combinations, which include the weight, distribution mean and standard deviation of each Gaussian distribution function; calculate the distribution probability of the preset distribution data belonging to each Gaussian distribution function under each parameter combination, and update the parameter combination according to the distribution probability; determine the target parameter combination, which is the parameter combination whose error value of the parameter combination before and after the update is less than the preset error value.

22. The reliability simulation device according to claim 15, characterized in that: The simulation module is also used to determine multiple simulation points in the rectangular pulse signal; input the voltages corresponding to the multiple simulation points as the gate voltages of the transistor to be simulated in sequence, and perform a preset number of simulations, and one simulation is performed when all the multiple simulation points of the rectangular pulse signal are input; and detecting a current drain current of the transistor to be simulated after a preset number of simulations, and determining the degradation rate according to the current drain current and an initial drain current.

23. The reliability simulation device according to claim 22, characterized in that: The degradation rate is characterized by a first ratio of the current drain current to the initial drain current, or the degradation rate is characterized by a second ratio of a difference between the initial drain current and the current drain current to the initial drain current.

24. The reliability simulation device according to claim 15, characterized in that: The number of simulation points when simulating with the rectangular pulse signal is less than the number of simulation points when simulating with the periodic interference signal.

25. The reliability simulation device according to claim 15, characterized in that: The simulation software performs simulation according to a preset reliability simulation model, and the reliability simulation model is determined according to the type of the transistor to be simulated.

26. The reliability simulation device according to claim 24, characterized in that: The transistor to be simulated includes a laterally diffused metal oxide semiconductor transistor, and the reliability simulation model includes a reaction diffusion model.

27. The reliability simulation device according to claim 15, characterized in that: The multiple electromagnetic pulse signals are respectively generated by multiple gas-insulated switchgears operating in parallel at the same frequency.

28. The reliability simulation device according to claim 15, characterized in that: The transistor to be simulated includes a diode or a triode.

29. A simulation device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and is characterized in that when the computer program is executed by the processor, the processor executes the reliability simulation method according to any one of claims 1 to 14.

30. A non-volatile computer-readable storage medium containing a computer program, wherein when the computer program is executed by a processor, the processor executes the reliability simulation method according to any one of claims 1 to 14.

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