Noise power spectrum-based GaN device life prediction method and device, medium and product
By obtaining the gate current noise power spectrum of GaN devices and building a functional model of noise spectrum density and failure time, the problem of GaN device life prediction is solved, fast and accurate life prediction is achieved, and the reliability of the device is improved under high frequency and high temperature conditions.
Patent Information
- Application Number
- CN202510220896.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-27
- Publication Date
- 2025-06-13
AI Technical Summary
The prior art is difficult to quickly and accurately predict the lifetime of GaN devices, especially under high frequency and high temperature conditions.
By obtaining the gate current noise power spectrum of the GaN device and using a functional model of noise spectrum density and failure time to predict the failure time, the failure time of the device is determined, thereby predicting its lifetime.
It realizes fast and accurate prediction of GaN device life, and improves device reliability under high frequency and high temperature conditions.
Smart Images
Figure CN120145668A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of semiconductor technology, and particularly to a method, device, medium and product for predicting the lifetime of a GaN device based on a noise power spectrum. Background Art
[0002] The third-generation semiconductor materials represented by gallium nitride (GaN) have the advantages of wide bandgap, high breakdown electric field, high withstand voltage, high saturated electron velocity, high temperature resistance and good anti-radiation characteristics. Therefore, GaN materials are the best materials for preparing high-frequency, high-temperature, high-power and anti-radiation electronic components. Therefore, for GaN devices applied under high-frequency and high-temperature conditions, it is very important to improve the lifetime of the devices to operate normally. Due to the improvement of modern integration technology and the increasing use of more semiconductor materials, the update and iteration speed of semiconductor devices has become faster. However, the lifetime of semiconductors has also increased, from a short time at the beginning to more than ten years for most semiconductor devices today. Therefore, the technology of predicting the lifetime of GaN devices more quickly and accurately is becoming more and more important. Summary of the Invention
[0003] The purpose of the present application is to provide a method, device, medium and product for predicting the lifetime of a GaN device based on a noise power spectrum, so as to realize the lifetime prediction of a GaN device.
[0004] To achieve the above purpose, the present application provides the following solutions:
[0005] In the first aspect, the present application provides a method for predicting the lifetime of a GaN device based on a noise power spectrum, including:
[0006] Obtain the gate current noise power spectrum of the GaN device to be predicted; the horizontal axis of the gate current noise power spectrum is the noise frequency, and the vertical axis is the noise spectral density;
[0007] Based on the gate current noise power spectrum of the GaN device to be predicted and a failure time prediction model, obtain the failure time of the GaN device to be predicted; the failure time prediction model is a function of the noise spectral density and the failure time;
[0008] Determine the failure time of the GaN device to be predicted as the lifetime of the GaN device to be predicted.
[0009] Optionally, based on the gate current noise power spectrum of the GaN device to be predicted and a failure time prediction model, obtaining the failure time of the GaN device to be predicted includes:
[0010] According to the gate current noise power spectrum of the GaN device to be predicted, determine the noise spectral density of the GaN device to be predicted at a preset noise frequency;
[0011] Input the noise spectral density of the GaN device to be predicted at a preset frequency into the failure time prediction model to obtain the failure time of the GaN device to be predicted at a preset noise frequency.
[0012] Optionally, the determination process of the failure time prediction model includes:
[0013] Obtain the gate current noise power spectra of multiple target GaN devices under different preset gate voltages and the failure times under different preset gate stresses;
[0014] Average the failure times of each target GaN device under different preset gate stresses to obtain the final failure time of the target GaN device;
[0015] Determine the failure time prediction model based on the gate current noise power spectra and the final failure times of each target GaN device under different preset gate voltages.
[0016] Optionally, determining the failure time prediction model based on the gate current noise power spectra and the final failure times of each target GaN device under different preset gate voltages includes:
[0017] Construct an initial model; the initial model is a proportional function of the noise spectral density and the failure time;
[0018] Determine the noise spectral density of each target GaN device at different noise frequencies under different preset gate voltages according to the gate current noise power spectra of each target GaN device under different preset gate voltages;
[0019] Determine the proportionality coefficient of the initial model at different noise frequencies under different preset gate voltages of each target GaN device according to the noise spectral density and the final failure time of each target GaN device at different noise frequencies under different preset gate voltages;
[0020] Average the proportionality coefficients of the initial model at different noise frequencies under different preset gate voltages of each target GaN device to obtain the final proportionality coefficient of the initial model;
[0021] Substitute the final proportionality coefficient into the initial model to obtain the failure time prediction model.
[0022] Optionally, before obtaining the gate current noise power spectra of multiple target GaN devices under different preset gate voltages and the failure times under different preset gate stresses, it further includes:
[0023] Conduct electrical tests on multiple initial GaN devices to obtain the gate current-voltage curves of the multiple initial GaN devices; the horizontal axis of the gate current-voltage curve is the gate current and the vertical axis is the gate voltage;
[0024] Judge whether each initial GaN device meets the preset screening conditions based on the gate current-voltage curve of each initial GaN device; the screening condition is that when the gate voltage is less than 6V, the gate current is less than 100uA;
[0025] If so, determine the corresponding initial GaN device as the target GaN device.
[0026] Optionally, each of the preset gate stresses is higher than the normal operating voltage of the target GaN device and lower than the breakdown gate voltage of the target GaN device.
[0027] Optionally, the GaN device to be predicted includes: a p-GaN / AlGaN / GaN HEMT device.
[0028] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the method for predicting the life of a GaN device based on the noise power spectrum described in any one of the above.
[0029] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for predicting the life of a GaN device based on the noise power spectrum described in any one of the above.
[0030] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for predicting the life of a GaN device based on the noise power spectrum described in any one of the above.
[0031] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:
[0032] The present application discloses a method, device, medium and product for predicting the life of a GaN device based on the noise power spectrum. First, obtain the gate current noise power spectrum of the GaN device to be predicted; the horizontal axis of the gate current noise power spectrum is the noise frequency, and the vertical axis is the noise spectral density; then, based on the gate current noise power spectrum of the GaN device to be predicted and the failure time prediction model, obtain the failure time of the GaN device to be predicted; the failure time prediction model is a function of the noise spectral density and the failure time; finally, determine the failure time of the GaN device to be predicted as the life of the GaN device to be predicted. The present application uses a failure time prediction model constructed based on the noise spectral density and the failure time to achieve the life prediction of the GaN device. Description of the Drawings
[0033] To more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0034] Figure 1 Schematic diagram of the lifespan prediction method for GaN devices based on the noise power spectrum provided by an embodiment of the present application;
[0035] Figure 2 Schematic diagram of the p-GaN / AlGaN / GaN HEMT device structure;
[0036] Figure 3 Schematic diagram of the gate structure of the p-GaN / AlGaN / GaN HEMT device;
[0037] Figure 4 Schematic diagram of the gate current noise power spectrum test system;
[0038] Figure 5 Schematic diagram of the gate stress test system;
[0039] Figure 6 Schematic diagram of the gate current noise power spectrum of the p-GaN / AlGaN / GaN HEMT device under different gate voltages (V G ) in the initial state;
[0040] Figure 7 Breakdown time characteristic diagram of the p-GaN / AlGaN / GaN HEMT device when the gate stress (V G_stress ) is 10V;
[0041] Figure 8 Breakdown time Weibull distribution diagram of the p-GaN / AlGaN / GaN HEMT device when the gate stress (V G_stress ) is 10V;
[0042] Figure 9 Characteristic diagram of the relationship between the noise spectral density and the failure time and the predicted fitting relationship diagram of the p-GaN / AlGaN / GaN HEMT device at a gate voltage of 1.5V and 0.1Hz;
[0043] Figure 10 Characteristic diagram of the relationship between the noise spectral density and the failure time and the predicted fitting relationship diagram of the p-GaN / AlGaN / GaN HEMT device at a gate voltage of 3V and 1Hz;
[0044] Figure 11Schematic diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners
[0045] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0046] The purpose of the present application is to provide a method, device, medium and product for predicting the lifetime of a GaN device based on the noise power spectrum, aiming to realize the lifetime prediction of the GaN device.
[0047] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific implementation manners.
[0048] In an exemplary embodiment, as Figure 1 shown, a method for predicting the lifetime of a GaN device based on the noise power spectrum is provided, including:
[0049] Step 1: Obtain the gate current noise power spectrum of the GaN device to be predicted; the horizontal axis of the gate current noise power spectrum is the noise frequency, and the vertical axis is the noise spectral density.
[0050] As an optional implementation manner, the GaN device to be predicted includes: a p-GaN / AlGaN / GaN HEMT device.
[0051] In fact, the method of the present application is not limited to the lifetime prediction of GaN devices, but can also be used for the lifetime prediction of devices such as diodes, field effect transistors, and bipolar junction transistors.
[0052] Specifically, as Figure 2 and Figure 3 shown, the p-GaN / AlGaN / GaN HEMT device includes: an Si (silicon) substrate, a gate, a source (S), a drain (D), and a silicon nitride layer. The gate includes GaN, AlGaN, p-GaN, and Ni / Cr arranged from bottom to top.
[0053] Step 2: Based on the gate current noise power spectrum of the GaN device to be predicted and the failure time prediction model, obtain the failure time of the GaN device to be predicted; the failure time prediction model is a function of the noise spectral density and the failure time.
[0054] As an optional implementation manner, step 2 includes:
[0055] Step 211: Determine the noise spectral density of the to-be-predicted GaN device at a preset noise frequency according to the gate current noise power spectrum of the to-be-predicted GaN device.
[0056] Step 212: Input the noise spectral density of the to-be-predicted GaN device at the preset frequency into the failure time prediction model to obtain the failure time of the to-be-predicted GaN device at the preset noise frequency.
[0057] As an optional implementation manner, the determination process of the failure time prediction model in Step 2 includes:
[0058] Step 221: Obtain the gate current noise power spectra of multiple target GaN devices under different preset gate voltages and the failure times under different preset gate stresses.
[0059] Specifically, each preset gate voltage is within the set range of the threshold voltage of the target GaN device and does not exceed the normal operating voltage of the target GaN device. Generally, the preset gate voltage is within the range of ±20% of the threshold voltage of the target GaN device.
[0060] Specifically, use the Figure 4 shown gate current noise power spectrum test system to obtain the gate current noise power spectrum of the GaN device. The main device of the gate current noise power spectrum test system is an SR 570 low-noise current amplifier, which combines an extended current of a current amplifier inside. It can amplify currents below 4 mA and output voltages below 4 V, and can amplify the relatively low gate current of the GaN device at the order of nA. It also includes a high and low temperature probe station that can be used to measure wafers or a circuit test board designed to measure packaged devices. First, apply a stable and low-noise dry battery to the gate voltage of the GaN device, and then output the gate current of the GaN device to the SR 570 low-noise current amplifier for appropriate range amplification and output. The mode of the SR 570 low-noise current amplifier should be selected as the low-noise mode, which can accurately amplify the tiny current noise of the GaN device. The gate current is output as a voltage to the oscilloscope by the SR 570 low-noise current amplifier, and data can also be collected through a data acquisition card. The output voltage can be displayed and saved through the FFT function of the oscilloscope itself, or analyzed and saved by a professional spectrum analyzer. At the same time, the saved original output voltage can be subjected to spectrum analysis through relevant software (MATLAB) on the PC side. This process converts the original output signal from the time state to the frequency state, so that the gate current noise power spectrum of the GaN device can be obtained.
[0061] Use the Figure 5The gate stress test system shown performs stress tests on GaN devices under different preset gate stresses to obtain the failure times under different preset gate stresses. The gate stress test system mainly uses a Keysight B2912 semiconductor parameter tester, which can not only perform basic electrical tests on GaN devices, but also use the I-tsampling program in it to perform gate stress tests. The gate voltage applied during the stress test should be higher than the normal operating voltage of the GaN device, but lower than the breakdown gate voltage of the GaN device. Due to the differences in GaN devices themselves, the stress application times vary. Some may take several hours to break down, while some can be broken down in as short as a few hundred seconds.
[0062] As an alternative implementation, before step 221, it further includes:
[0063] Step 2201: Perform electrical tests on multiple initial GaN devices to obtain the gate current-voltage curves of the multiple initial GaN devices; the horizontal axis of the gate current-voltage curve is the gate current, and the vertical axis is the gate voltage.
[0064] Step 2202: Based on the gate current-voltage curves of the respective initial GaN devices, determine whether each initial GaN device meets the preset screening conditions; the screening condition is that when the gate voltage is less than 6V, the gate current is less than 100uA.
[0065] Step 2203: If so, determine the corresponding initial GaN device as the target GaN device.
[0066] Specifically, in actual application, the number of target GaN devices is not less than 20.
[0067] Step 222: Calculate the average value of the failure times of each target GaN device under different preset gate stresses to obtain the final failure time of the target GaN device.
[0068] Step 223: Based on the gate current noise power spectra and the final failure times of each target GaN device under different preset gate voltages, determine the failure time prediction model.
[0069] As an alternative implementation, step 223 includes:
[0070] Step 2231: Construct an initial model; the initial model is a proportional function of the noise spectral density and the failure time.
[0071] Step 2232: Based on the gate current noise power spectra of each target GaN device under different preset gate voltages, determine the noise spectral densities of each target GaN device at different noise frequencies under different preset gate voltages.
[0072] Step 2233: Determine the proportionality coefficients of the initial model for each target GaN device at different preset gate voltages and different noise frequencies according to the noise spectral density and the final failure time of each target GaN device at different preset gate voltages and different noise frequencies.
[0073] Step 2234: Calculate the average value of the proportionality coefficients of the initial model for each target GaN device at different preset gate voltages and different noise frequencies to obtain the final proportionality coefficient of the initial model.
[0074] Step 2235: Substitute the final proportionality coefficient into the initial model to obtain the failure time prediction model.
[0075] Specifically, the expression of the failure time prediction model is:
[0076] Sig = Kt.
[0077] Where Sig is the noise spectral density; K is the final proportionality coefficient; t is the failure time.
[0078] As an optional implementation manner, each preset gate stress is higher than the normal operating voltage of the target GaN device and lower than the breakdown gate voltage of the target GaN device.
[0079] Specifically, when the gate stress is 1.5 - 2 times the normal operating voltage, the preset gate stress is 1.5 times - 2 times the normal operating voltage.
[0080] Step 3: Determine the failure time of the GaN device to be predicted as the lifetime of the GaN device to be predicted.
[0081] This application also verifies the lifetime prediction method of GaN devices based on the noise power spectrum by predicting the lifetime of p-GaN / AlGaN / GaN HEMT devices, as follows.
[0082] (1) Obtain the gate current noise power spectra of multiple p-GaN / AlGaN / GaN HEMT devices at different preset gate voltages (1.5V, 3V, 4.5V, 6V). The gate current noise power spectra are as Figure 6 shown. Figure 6 In the figure, the horizontal axis is the noise frequency and the vertical axis is the noise spectral density. Since the gate currents of p-GaN / AlGaN / GaN HEMT devices at the four gate voltages are in the nA and uA levels, the amplification ranges of the SR570 low-noise current amplifier should also be selected in the nA / V and uA / V levels to prevent the amplified voltage from exceeding the maximum range of the SR570 low-noise current amplifier.
[0083] (2) Obtain the failure times of p-GaN / AlGaN / GaN HEMT devices under different preset gate stresses. Since the p-GaN / AlGaN / GaN HEMT device is of Schottky type, the normal operating gate voltage is 0 - 6V, and the breakdown voltage is 12.5V. The gate stresses used are 9.5V and 10V. Record the failure times of the p-GaN / AlGaN / GaN HEMT devices for graph plotting. The breakdown time characteristics of the p-GaN / AlGaN / GaN HEMT device under a gate stress of 10V are as Figure 7 shown. The Weibull distribution of the breakdown time of the p-GaN / AlGaN / GaN HEMT device under a gate stress of 10V is as Figure 8 shown. Among them, Time is time, with the unit of second (s), and t BD is the breakdown time, that is, the failure time.
[0084] (3) By extracting the noise spectral density of 0.1Hz of the p-GaN / AlGaN / GaN HEMT device at a gate voltage of 1.5V and the noise spectral density of 1Hz of the p-GaN / AlGaN / GaN HEMT device at a gate voltage of 3V, and then combining the noise spectral density with the failure time of the p-GaN / AlGaN / GaN HEMT device under stress test to plot a related graph and obtain a prediction curve at this gate voltage, as Figure 9 and Figure 10 .
[0085] (4) Finally, inversely deduce the normal operating life of the p-GaN / AlGaN / GaN HEMT device through the obtained data and the established failure time prediction model.
[0086] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement a method for predicting the life of a GaN device based on the noise power spectrum.
[0087] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for predicting the life of a GaN device based on the noise power spectrum is implemented.
[0088] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, a method for predicting the life of a GaN device based on the noise power spectrum is implemented.
[0089] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for predicting the lifetime of a GaN device based on the noise power spectrum.
[0090] Those skilled in the art can understand that Figure 11 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0091] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memories (RAMs) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0092] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.
[0093] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.
[0094] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.
[0095] In this text, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. To sum up, the content of this specification should not be construed as a limitation to the present application.
Claims
1. A method for predicting the lifetime of a GaN device based on noise power spectrum, characterized in that: The life prediction method of the GaN device based on the noise power spectrum includes: Obtaining a gate current noise power spectrum of a GaN device to be predicted; wherein the horizontal axis of the gate current noise power spectrum is the noise frequency, and the vertical axis is the noise spectrum density; Based on the gate current noise power spectrum of the GaN device to be predicted and a failure time prediction model, the failure time of the GaN device to be predicted is obtained; the failure time prediction model is a function of the noise spectrum density and the failure time; The failure time of the GaN device to be predicted is determined as the life of the GaN device to be predicted.
2. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 1, characterized in that: Based on the gate current noise power spectrum of the GaN device to be predicted and the failure time prediction model, the failure time of the GaN device to be predicted is obtained, including: Determine the noise spectrum density of the GaN device to be predicted at a preset noise frequency according to the gate current noise power spectrum of the GaN device to be predicted; The noise spectrum density of the GaN device to be predicted at a preset frequency is input into the failure time prediction model to obtain the failure time of the GaN device to be predicted at the preset noise frequency.
3. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 1, characterized in that: The determination process of the failure time prediction model includes: Obtain gate current noise power spectra of multiple target GaN devices under different preset gate voltages and failure times under different preset gate stresses; averaging the failure time of each target GaN device under different preset gate stresses to obtain a final failure time of the target GaN device; The failure time prediction model is determined based on the gate current noise power spectrum and final failure time of each target GaN device at different preset gate voltages.
4. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 3, characterized in that: Based on the gate current noise power spectrum and final failure time of each target GaN device at different preset gate voltages, the failure time prediction model is determined, including: Constructing an initial model; the initial model is a directly proportional function of noise spectral density and failure time; Determine the noise spectrum density of each target GaN device at different noise frequencies at different preset gate voltages according to the gate current noise power spectrum of each target GaN device at different preset gate voltages; Determine the proportional coefficient of the initial model at different noise frequencies of each target GaN device at different preset gate voltages according to the noise spectrum density and final failure time of each target GaN device at different noise frequencies at different preset gate voltages; averaging the direct proportional coefficients of the initial model at different noise frequencies under different preset gate voltages of each target GaN device to obtain a final direct proportional coefficient of the initial model; Substituting the final positive proportional coefficient into the initial model, the failure time prediction model is obtained.
5. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 3, characterized in that: Before obtaining gate current noise power spectra of multiple target GaN devices under different preset gate voltages and failure times under different preset gate stresses, the method further includes: Conducting electrical tests on a plurality of initial GaN devices to obtain gate current-voltage curves of the plurality of initial GaN devices; wherein the horizontal axis of the gate current-voltage curve is the gate current and the vertical axis is the gate voltage; Determining whether each initial GaN device meets a preset screening condition based on a gate current-voltage curve of each initial GaN device; the screening condition is that when the gate voltage is less than 6V, the gate current is less than 100uA; If so, the corresponding initial GaN device is determined as the target GaN device.
6. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 3, characterized in that: Each of the preset gate stresses is higher than the normal operating voltage of the target GaN device and lower than the breakdown gate voltage of the target GaN device.
7. The method for predicting the lifetime of a GaN device based on noise power spectrum according to claim 1, characterized in that: The GaN device to be predicted includes: p-GaN / AlGaN / GaN HEMT device.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the lifetime of a GaN device based on noise power spectrum as described in any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the lifetime of a GaN device based on noise power spectrum as described in any one of claims 1 to 7 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the lifetime of a GaN device based on noise power spectrum as described in any one of claims 1 to 7 is implemented.