Method and system for constructing performance analysis model based on fast recovery diode chip

Abstract

The present invention relates to the technical field of diodes of semiconductor materials, and a method and system for constructing a performance analysis model based on a fast recovery diode chip, comprising: performing classification and sorting operations on a fast recovery diode chip set to obtain a sequential chip set, constructing an experimental parameter set based on the sequential chip set, and performing the following operations on the experimental parameters: constructing a detection circuit based on rated conditions, and installing the experimental chip to the detection circuit to obtain a target parameter curve set and a detection parameter set, associating the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, summarizing the unit model to obtain an initial model, constructing an amplification parameter sequence set based on the experimental parameter set, and constructing a performance analysis model based on the amplification parameter sequence set and the initial model. The present invention can predict the performance of fast recovery diode chips with different transition zone sizes and different doping concentrations in the reverse recovery stage when the diode chip model is known.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor diode technology, and in particular to a method, system, electronic device and computer-readable storage medium for constructing a performance analysis model based on a fast recovery diode chip. Background Art

[0002] The fast recovery diode chip is a chip equipped with a fast recovery diode chip. The fast recovery diode chip is a power chip that is packaged in anti-parallel with the insulated gate double-click transistor and plays a freewheeling role. The characteristics of the reverse recovery stage are an important indicator to measure the performance of the fast recovery diode chip, which directly affects the off-state loss and reliability of the device. The smaller the recovery softness and the smoother the waveform during reverse recovery, the better the reverse recovery characteristics of the fast recovery diode chip.

[0003] In the prior art, there are various types of fast recovery diode chips, and the performance of the fast recovery diode chip in the reverse recovery stage is often optimized by changing the doping concentration and the transition region size.

[0004] Although the above method can optimize the performance of the fast recovery diode chip in the reverse recovery stage, in actual experiments, it is impossible to experiment with each different model, different doping concentration and different model of fast recovery diode chip. Therefore, a performance analysis model is needed to predict the performance data of fast recovery diode chips with different transition zone sizes and different doping concentrations. Summary of the invention

[0005] The present invention provides a method for constructing a performance analysis model based on a fast recovery diode chip. The computer can predict the performance of fast recovery diode chips with different transition zone sizes and different doping concentrations in the reverse recovery stage when the diode chip model is known.

[0006] To achieve the above object, the present invention provides a method for constructing a performance analysis model based on a fast recovery diode chip, comprising:

[0007] Acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration;

[0008] For each experimental parameter in the experimental parameter set, perform the following operations:

[0009] Confirm the fast recovery diode chip corresponding to the experimental parameters, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameters, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions, associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness;

[0010] The step of performing a preprocessing operation on the detection circuit equipped with the experimental chip to obtain the experimental circuit includes:

[0011] Obtain a component set of a detection circuit, wherein the component set includes: a power supply, a first inductor, a second inductor, a switch, and an experimental chip, wherein in the detection circuit, the power supply, the first inductor, the second inductor, and the switch are connected in series, the circuit corresponding to the experimental chip is only connected in parallel with two ends of the second inductor, and the circuit corresponding to the experimental chip has and only has the experimental chip;

[0012] After confirming that the component set meets the pre-built electrical characteristic specifications, an available circuit is obtained, a switch corresponding to the available circuit is closed, and a charging operation is performed on the first inductor and the second inductor based on the available circuit after the switch is closed. When the first inductor and the second inductor in the available circuit are respectively the preset first saturated inductor and the second saturated inductor, the available circuit is disconnected to obtain a chip forward conduction circuit;

[0013] Connecting the pre-built monitoring unit to the experimental chip in the chip forward conduction circuit, and after confirming that the experimental chip is in the preset forward conduction stage, obtaining the experimental circuit;

[0014] Aggregate the unit models to obtain the initial model;

[0015] An amplification parameter sequence set is constructed based on the experimental parameter set, an amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a fill operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. Based on the performance analysis model, the construction of a performance analysis model based on the fast recovery diode chip is completed.

[0016] Optionally, performing a classification and sorting operation on the fast recovery diode chip set to obtain a sequential chip set, and constructing an experimental parameter set based on the sequential chip set includes:

[0017] Extract fast recovery diode chips from the fast recovery diode chip set in sequence to obtain the target chip, and perform the following operations on the target chip:

[0018] Identify the model, transition region size and doping concentration of the target chip, and perform encoding operation on the model, transition region size and doping concentration of the target chip to obtain encoding data, wherein the encoding data includes: model code, transition region size code and doping concentration code;

[0019] The coded data are respectively aggregated according to the model codes corresponding to the coded data to obtain a plurality of model data sets corresponding to the fast recovery diode chip set, wherein the model codes of all the model data in the model data set are the same;

[0020] The following operations are performed on the model datasets in the multiple model datasets:

[0021] Identify and extract the transition zone size code corresponding to each model data in the model data set to obtain the size code to be sorted, summarize all the size codes to be sorted in the order of the size codes to be sorted from small to large, obtain the first sequence column corresponding to the model data set, and use the first sequence column to perform a sorting operation on the model data set to obtain the first sequence data set;

[0022] Perform recursive operations on the first sequence column to obtain a size recursive sequence, and perform the following operations on the size recursive values ​​in the size recursive sequence in turn:

[0023] Extracting a target data set from the first sequential data set based on the size recursive value, performing a sorting operation on the target data set to obtain a concentration sequential data set, wherein the model codes and transition zone size codes corresponding to the target data in the target data set are the same;

[0024] Summarizing the concentration sequential data set according to the order of the size recursive values ​​in the size recursive sequence to obtain a second sequential data set corresponding to the model data set;

[0025] The second sequential data set is summarized in ascending order according to the preset model code to obtain a sequential chip set corresponding to the fast recovery diode chip set, sequential chip data is extracted from the sequential chip set in sequence, and a coding translation operation is performed based on the extracted sequential chip data to obtain unit experimental parameters, and the unit experimental parameters are summarized to obtain an experimental parameter set.

[0026] Optionally, the step of obtaining a target parameter curve set and a detection parameter set after a reverse recovery operation is performed on the experimental circuit under rated conditions includes:

[0027] Acquire a target parameter set, wherein the target parameter set includes a plurality of target parameters, and the plurality of target parameters are: reverse current, reverse voltage and leakage current, and the rated conditions include: a rated voltage corresponding to a power supply, a rated current stress corresponding to a first inductor, and a rated instantaneous current corresponding to a second inductor;

[0028] The monitoring start time is obtained by using the monitoring unit and the experimental circuit, and the following operations are performed on each target parameter in the target parameter set based on the monitoring start time:

[0029] A target parameter coordinate system is constructed according to the preset monitoring frequency, target parameter and monitoring start time, wherein the horizontal axis in the target parameter coordinate system is time, the time corresponding to the origin is the monitoring start time, and the vertical axis is the target parameter;

[0030] Based on the monitoring start time, the experimental circuit is monitored in real time, and a switch in the experimental circuit is closed during the monitoring of the experimental circuit to obtain real-time target parameter data, wherein the real-time target parameter data includes: real-time target parameters and real-time time;

[0031] When it is confirmed that the real-time target parameter in the real-time target parameter data reaches a preset stable state, the real-time time corresponding to the stable state is confirmed as the monitoring termination time;

[0032] Summarize the real-time target parameter data between the monitoring end time and the monitoring start time to obtain a real-time data set, map the real-time data set to the target parameter coordinate system, and fit the mapped real-time data set into a curve to obtain a target parameter curve;

[0033] The target parameter curves are summarized to obtain a target parameter curve set, and the target parameter curve set is parsed to obtain a detection parameter set corresponding to the target parameter curve set.

[0034] Optionally, when confirming that the real-time target parameter in the real-time target parameter data reaches a preset stable state, confirming the real-time time corresponding to reaching the stable state as the monitoring termination time includes:

[0035] Acquire a stability determination window, wherein the stability determination window includes a preset number of blank arrays;

[0036] The real-time target parameter is mapped into the stability determination window to obtain an initial determination window, and a plurality of real-time target parameter data adjacent to the real-time target parameter data are obtained based on the real-time target parameter data and a preset number to obtain a plurality of adjacent sequence data, wherein the sum of the number of the plurality of adjacent sequence data and the number of the real-time target parameter data is equal to the preset number;

[0037] Extracting the real-time target parameter corresponding to each adjacent sequence data in sequence from a plurality of adjacent sequence data, and mapping the extracted real-time target parameters to the initial determination window to obtain a window to be determined, wherein the window to be determined includes a plurality of parameters to be determined, and the plurality of parameters to be determined are sorted in a time-ordered order, and the first parameter to be determined in the plurality of parameters to be determined is the real-time target parameter corresponding to the real-time target parameter data;

[0038] Obtain a fitting curve based on all parameters to be determined in the window to be determined, and calculate a characteristic parameter set of the fitting curve;

[0039] Extract the earliest adjacent sequence data in time from multiple adjacent sequence data, and obtain the real-time target parameters corresponding to the earliest adjacent sequence data, obtain the updated target parameters, use the updated target parameters as the real-time target parameters, return to the step of mapping the real-time target parameters to the stability determination window, until the number of calculated feature parameter sets reaches the preset number of stability evaluations, and obtain the evaluation parameter set;

[0040] Extracting multiple target feature parameter sets from the evaluation parameter set, calculating the standard deviation corresponding to each target feature parameter set in the multiple target feature parameter sets, and obtaining multiple feature standard deviations, wherein the feature standard deviations correspond to the target feature parameter sets one by one;

[0041] If the plurality of characteristic standard deviations are all smaller than the corresponding preset fluctuation thresholds, it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state; otherwise, the process returns to the step of extracting the earliest adjacent sequential data among the plurality of adjacent sequential data until it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state;

[0042] The real time corresponding to reaching the stable state is confirmed as the monitoring termination time.

[0043] Optionally, the calculation of the characteristic parameter set of the fitting curve includes:

[0044] Calculate the characteristic integral and characteristic derivative of the fitting curve, where the calculation formulas for the characteristic integral and characteristic derivative are as follows:

[0045] ;

[0046] ;

[0047] in, represents the characteristic integral, Indicates the earliest parameter to be determined in the window to be determined. The latest parameter to be determined in the determination window. represents the fitting curve, The corresponding judgment time in the table fitting curve is represents the characteristic derivative;

[0048] The characteristic integrals and characteristic derivatives are summarized to obtain the characteristic parameter set of the fitting curve.

[0049] Optionally, constructing an amplification parameter sequence set based on an experimental parameter set comprises:

[0050] Extract all doping concentrations from the experimental parameter set to obtain a doping concentration set, perform a recursive operation on the doping concentration set to obtain multiple concentration recursive values, perform a sorting operation on the multiple concentration recursive values ​​in order from small to large, and obtain a concentration recursive sequence, and perform the following operations on the concentration recursive values ​​in the concentration recursive sequence:

[0051] If the concentration recursive value is not the concentration recursive value with the largest value in the concentration recursive sequence, then extract the next concentration recursive value adjacent to the concentration recursive value from the concentration recursive sequence to obtain an adjacent concentration value, calculate the difference between the adjacent concentration value and the concentration recursive value to obtain a concentration interval, and if the concentration recursive value is the concentration recursive value with the largest value in the concentration recursive sequence, then skip the concentration recursive value;

[0052] Summarize the concentration intervals to obtain a concentration interval set, calculate the greatest common divisor of the concentration interval set, and obtain the initial concentration amplification unit;

[0053] Determining whether the initial concentration amplification unit is less than a preset concentration amplification threshold;

[0054] If the initial concentration amplification unit is less than the concentration amplification threshold, performing a concentration value amplification operation on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence;

[0055] Otherwise, the concentration value amplification operation is performed on the concentration recursive sequence using the initial concentration amplification unit to obtain a concentration amplification sequence;

[0056] Constructing a size amplification sequence based on the size recursive sequence, extracting size amplification values ​​from the size recursive sequence in sequence, and associating the extracted size amplification values ​​with the concentration amplification sequence to obtain a size-concentration set, wherein the concentration amplification sequence includes a plurality of concentration amplification values, the size-concentration set includes a plurality of size-concentration parameters, and one size-concentration parameter includes: a size amplification value and a concentration amplification value;

[0057] Summarizing the size-concentration sets to obtain the size-concentration amplification sets corresponding to the size amplification sequences;

[0058] Extract all the fast recovery diode chip models from the experimental parameter set, identify the model code corresponding to each fast recovery diode chip model among all the fast recovery diode chip models, and perform a sorting operation on all the fast recovery diode chip models in ascending order according to the model code to obtain a chip model set, associate the chip model set with the size-concentration amplification set to obtain an amplification parameter sequence set.

[0059] Optionally, performing a concentration value amplification operation on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence includes:

[0060] Extract the concentration recursive values ​​from the concentration recursive sequence in sequence to obtain the target concentration value, and perform the following operations on the target concentration value:

[0061] Extracting the next concentration recursive value adjacent to the target concentration value from the concentration recursive sequence to obtain a comparison concentration value, performing a cumulative operation on the target concentration value using a concentration amplification threshold to obtain a cumulative concentration value, and calculating the difference between the comparison concentration value and the cumulative concentration value to obtain a residual cumulative total value;

[0062] If the remaining cumulative total value is less than or equal to the concentration amplification threshold, the target concentration value is confirmed as the cumulative concentration value;

[0063] If the remaining accumulated total value is greater than the concentration amplification threshold, the target concentration value is confirmed as the accumulated concentration value, and the accumulated concentration value is used as the target concentration value, and the process returns to the step of performing an accumulation operation on the target concentration value using the concentration amplification threshold;

[0064] The accumulated concentration values ​​and the concentration recursive sequence are summarized, and a deduplication operation is performed on the summarized accumulated concentration values ​​and the concentration recursive sequence to obtain a concentration amplification sequence.

[0065] Optionally, the step of constructing an amplification parameter model based on the amplification parameter sequence set and the initial model, extracting a blank model set from the amplification parameter model, and performing a filling operation on the blank model set using the initial model to obtain a prediction model set includes:

[0066] Building an initial amplification parameter model based on the amplification parameter sequence set, extracting all unit models from the initial model to obtain a plurality of actual amplification parameter models, and mapping the plurality of actual amplification parameter models to the initial amplification parameter model to obtain an initial filling amplification model;

[0067] Using preset training sample parameters to divide the multiple actual amplification parameter models, obtaining a training sample set and a verification sample set, using the training sample set to train a pre-constructed initial performance analysis model, and using the verification sample set to perform an iterative operation on the trained initial performance analysis model to obtain an optimized performance analysis model, wherein the initial performance analysis model is a pre-constructed neural network construction model;

[0068] Extracting a blank model set from the initial fill-in augmentation model, and performing interpolation simulation operations on the blank models in the blank model set using a pre-built interpolation simulation method and a plurality of actual augmentation parameter models, to obtain a first prediction model set;

[0069] The optimized performance analysis model is used to perform prediction operations on the blank models in the blank model set to obtain a second prediction model set, and a weighted integration operation is performed on the first prediction model set and the second prediction model set to obtain a prediction model set.

[0070] To achieve the above object, the present invention further provides a system for constructing a performance analysis model based on a fast recovery diode chip, comprising:

[0071] A diode chip acquisition module is used to acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition zone size, and a doping concentration;

[0072] The performance test module is used to perform the following operations for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameter, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, and obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions;

[0073] The initial model acquisition module is used to associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: reverse current curve, reverse voltage curve and leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness, and summarize the unit models to obtain the initial model;

[0074] The performance analysis model construction module is used to construct an amplification parameter sequence set based on the experimental parameter set, to construct an amplification parameter model based on the amplification parameter sequence set and the initial model, to extract a blank model set from the amplification parameter model, to perform a filling operation on the blank model set using the initial model to obtain a prediction model set, to summarize and integrate the prediction model set and the initial model to obtain a performance analysis model, and to complete the construction of a performance analysis model based on the fast recovery diode chip based on the performance analysis model.

[0075] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0076] A memory storing at least one instruction;

[0077] The processor executes the instructions stored in the memory to implement the above-mentioned method for constructing a performance analysis model based on a fast recovery diode chip.

[0078] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for constructing a performance analysis model based on a fast recovery diode chip.

[0079] In order to solve the problem described in the background technology, the present invention obtains a fast recovery diode chip set, performs classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and constructs an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes multiple fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition zone size and a doping concentration. It can be seen that the present invention performs encoding operations on the model, transition zone size and doping concentration of each fast recovery diode chip for the convenience of sorting, but in the performance analysis model, in order to facilitate researchers to consult, all sequential chip data after the encoding operation are translated into parameters that are easy for researchers to consult. The following operations are performed for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameters, construct a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform preprocessing operations on the detection circuit with the experimental chip installed to obtain the experimental circuit. It can be seen that the present invention constructs an experimental circuit for simulating the normal operation of the fast recovery diode chip, and in order to facilitate monitoring the process of the experimental chip entering the reverse recovery stage, the embodiment of the present invention starts to monitor the experimental circuit before the experimental chip enters the reverse recovery stage and before closing the switch in the experimental circuit, so as to avoid losing the data at the moment of entering the reverse recovery stage. According to the data, the target parameter curve set and the detection parameter set are obtained after the reverse recovery operation is performed on the experimental circuit under rated conditions, and the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip are associated to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness. It can be seen that the present invention monitors the performance parameters of the experimental chip in the reverse recovery stage, and integrates the parameters into a unit model. At the same time, according to the characteristics of the fast recovery diode chip in the reverse recovery stage, the stability judgment window is used to adaptively determine the monitoring termination time, and the unit models are summarized to obtain the initial model. An amplification parameter sequence set is constructed based on the experimental parameter set. An amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a filling operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. Based on the performance analysis model, the performance analysis model based on the fast recovery diode chip is constructed. The present invention explores the performance of different fast recovery diode chip models, different transition zone sizes and different doping concentrations in the reverse recovery stage through experiments, and constructs a performance analysis model based on the initial model obtained from the experiment.Therefore, the present invention can predict the performance of fast recovery diode chips with different transition region sizes and different doping concentrations in the reverse recovery stage when the diode chip model is known. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic flow chart of a method for constructing a performance analysis model based on a fast recovery diode chip provided in one embodiment of the present invention;

[0081] Figure 2 A functional module diagram of a system for building a performance analysis model based on a fast recovery diode chip provided by an embodiment of the present invention;

[0082] Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for constructing a performance analysis model based on a fast recovery diode chip provided by an embodiment of the present invention.

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0085] The embodiment of the present application provides a method for constructing a performance analysis model based on a fast recovery diode chip. The execution subject of the method for constructing a performance analysis model based on a fast recovery diode chip includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for constructing a performance analysis model based on a fast recovery diode chip can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0086] Reference Figure 1 FIG. 1 is a flow chart of a method for constructing a performance analysis model based on a fast recovery diode chip according to an embodiment of the present invention. In this embodiment, the method for constructing a performance analysis model based on a fast recovery diode chip includes:

[0087] S1. Acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set.

[0088] Furthermore, the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration.

[0089] It should be noted that the fast recovery diode chip is a power chip that is packaged in anti-parallel with an insulated gate double-strike transistor and plays a freewheeling role. In the prior art, there are many types of fast recovery diode chips, and inside the fast recovery diode chip, the area between the P-type semiconductor material and the N-type semiconductor material is the transition zone, and the size of the transition zone is the width of the entire area between the P-type semiconductor material and the N-type semiconductor material. The doping concentration is the physical property of the fast recovery diode chip, that is, the concentration of impurity atoms artificially introduced into the semiconductor material.

[0090] It can be understood that the embodiment of the present invention explores the performance of different fast recovery diode chip models, different transition zone sizes and different doping concentrations in the reverse recovery stage through experiments, and predicts the performance of fast recovery diode chips with different transition zone sizes and different doping concentrations in the reverse recovery stage when the diode chip model is known based on the initial model obtained from the experiment.

[0091] Furthermore, performing a classification and sorting operation on the fast recovery diode chip set to obtain a sequential chip set, and constructing an experimental parameter set based on the sequential chip set, includes:

[0092] Extract fast recovery diode chips from the fast recovery diode chip set in sequence to obtain the target chip, and perform the following operations on the target chip:

[0093] Identify the model, transition region size and doping concentration of the target chip, and perform encoding operation on the model, transition region size and doping concentration of the target chip to obtain encoding data, wherein the encoding data includes: model code, transition region size code and doping concentration code;

[0094] The coded data are respectively aggregated according to the model codes corresponding to the coded data to obtain a plurality of model data sets corresponding to the fast recovery diode chip set, wherein the model codes of all the model data in the model data set are the same;

[0095] The following operations are performed on the model datasets in the multiple model datasets:

[0096] Identify and extract the transition zone size code corresponding to each model data in the model data set to obtain the size code to be sorted, summarize all the size codes to be sorted in the order of the size codes to be sorted from small to large, obtain the first sequence column corresponding to the model data set, and use the first sequence column to perform a sorting operation on the model data set to obtain the first sequence data set;

[0097] Perform recursive operations on the first sequence column to obtain a size recursive sequence, and perform the following operations on the size recursive values ​​in the size recursive sequence in turn:

[0098] Extracting a target data set from the first sequential data set based on the size recursive value, performing a sorting operation on the target data set to obtain a concentration sequential data set, wherein the model codes and transition zone size codes corresponding to the target data in the target data set are the same;

[0099] Summarizing the concentration sequential data set according to the order of the size recursive values ​​in the size recursive sequence to obtain a second sequential data set corresponding to the model data set;

[0100] The second sequential data set is summarized in ascending order according to the preset model code to obtain a sequential chip set corresponding to the fast recovery diode chip set, sequential chip data is extracted from the sequential chip set in sequence, and a coding translation operation is performed based on the extracted sequential chip data to obtain unit experimental parameters, and the unit experimental parameters are summarized to obtain an experimental parameter set.

[0101] It can be understood that the encoding operation is to identify the model, transition zone size and doping concentration of the target chip with a unified identification character. For example, the model of the target chip is IN5817, the transition zone size is 40um, and the doping concentration is , the model of the target chip is identified as A, the transition zone size is identified as 40, and the doping concentration is identified as 5-13. At this time, the corresponding model code is A, the transition zone size code is 40, and the doping concentration code is 5-13. Further, the model code, transition zone size code and doping concentration code of the target chip are associated, and the obtained coding data is A-40-5-13.

[0102] It should be noted that the model data set is a set of coded data corresponding to the same model code, and the embodiment of the present invention can realize performance analysis of a large number of different types of fast recovery diode chips. After all the coded data are summarized according to the model code, the coded data in the model data set is the model data.

[0103] For example, all the coded data are: A-40-5-13, A-30-5-13, C-40-1-14, B-40-5-13, then the model codes in all the coded data are: A, B and C, respectively. The coded data with model code A, model code B and model code C are respectively aggregated to obtain multiple model data sets, and the multiple model data sets are: (A-40-5-13, A-30-5-13), (B-40-5-13) and (C-40-1-14).

[0104] Specifically, the size code to be sorted is the transition zone size code extracted from the model data, and the first sequence column is a sequence obtained by sorting all the size codes to be sorted. The sorting operation on the model data set using the first sequence column is: adjusting the order of the model data in the model data set according to the order of the size codes to be sorted in the first sequence column.

[0105] For example, the existing model data set is: (A-40-5-13, A-30-4-13, A-30-1-14, A-20-8-13, A-50-1-14), then based on the model data (A-40-5-13), the transition zone size code is extracted, and the size code to be sorted is 40. Similarly, the multiple size codes to be sorted extracted based on the model data set are: 40, 30, 30, 20 and 50. The multiple size codes to be sorted are summarized in order from small to large, and the first sequence column obtained is: (20, 30, 30, 40, 50). The size codes to be sorted are extracted from the first sequence column in sequence, and the first extracted size code to be sorted is 20, and the corresponding model data is A-20-8-13. Therefore, in the first sequence data set, the first model data is A-20-8-13. Similarly, after sorting the model data set using the first sequence column, the resulting first sequence data set is: (A-20-8-13, A-30-4-13, A-30-1-14, A-40-5-13, A-50-1-14).

[0106] It is understandable that in the first sequence data set of the above example, there are two model data with the same size code to be sorted, namely: A-30-4-13 and A-30-1-14. Therefore, there will be another first sequence data set that conforms to the above-mentioned embodiment of the present invention, namely: (A-20-8-13, A-30-1-14, A-30-4-13, A-40-5-13, A-50-1-14). Therefore, the model data under the same size code to be sorted must also be sorted to determine a unique set of experimental parameters.

[0107] Specifically, the recursive operation performed on the first sequence column is: all different size codes to be sorted are screened out from the first sequence column, and all different size codes to be sorted are arranged in order from small to large. At this time, the sequence obtained after the arrangement is the size recursive sequence.

[0108] For example, the first order is: (20, 30, 30, 40, 50), all the different size codes to be sorted are: 20, 30, 40 and 50 respectively, then the size recursive sequence is: (20, 30, 40, 50), and the size recursive value is the value in the size recursive sequence.

[0109] Optionally, performing a sorting operation on the target data set to obtain a concentration order data set includes:

[0110] Identify and extract the doping concentration code of each target data in the target data set to obtain the concentration to be sorted, summarize all the concentration codes to be sorted in the order of the concentration to be sorted from small to large, obtain the second sequence column corresponding to the target data set, use the second sequence column to perform a sorting operation on the target data set, and obtain a concentration sequence data set.

[0111] Furthermore, the target data set is a set of multiple target data with the same model code and transition zone size code. The target data set is sorted using the second sequence column to obtain the concentration sequence data set by adjusting the sequence of the target data in the target data set according to the sequence of the concentration codes to be sorted in the second sequence column.

[0112] For example, the first sequential data set is: (A-20-8-13, A-30-4-13, A-30-1-14, A-30-5-13, A-50-1-14), then the size recursive sequence is (20, 30, 50), assuming that the extracted size recursive value is 30, then the target data set is: (A-30-4-13, A-30-1-14, A-30-5-13), and the multiple doping concentration codes extracted from the target data set are 4-13, 1-14 and 5-13, respectively, where 4-13 indicates that the doping concentration is , 1-14 indicates the doping concentration is , 5-13 indicates the doping concentration is , therefore, the concentrations to be sorted are: and , in the order of small to large concentration (ie: , and ) All the concentration codes to be sorted (i.e., 4-13, 1-14, and 5-13) are summarized, and the resulting second sequence column is: (4-13, 5-13, 1-14). The target data set is sorted using the second sequence column, and the resulting concentration sequence data set is: (A-30-4-13, A-30-5-13, A-30-1-14).

[0113] It should be noted that aggregating the concentration sequence data set according to the order of the size recursive values ​​in the size recursive sequence means aggregating the concentration sequence data set according to the order of the size recursive values ​​in the size recursive sequence. For example, the first sequential data set is: (A-20-8-13, A-30-4-13, A-30-1-14, A-30-5-13, A-50-1-14), and the corresponding size recursive sequence is: (20, 30, 50). Then the concentration sequential data set corresponding to the size recursive value 20 is: (A-20-8-13), and the concentration sequential data set corresponding to the size recursive value 30 is: (A-30-4-13, A-30-5-13, A-30-1-14), and so on. At this time, the second sequential data set is: (A-20-8-13, A-30-4-13, A-30-5-13, A-30-1-14, A-50-1-14).

[0114] It should be noted that, in the above example, for the convenience of illustration, only one target data is set in the target data set corresponding to the size recursive values ​​of 20 and 50, but in the actual experimental process, each size recursive value corresponds to multiple target data, and the doping concentration corresponding to each size recursive value is different after the recursion, and the doping concentrations corresponding to different size recursive values ​​may be the same after the recursion, thereby ensuring that the number of experimental parameters as samples in the embodiment of the present invention is large enough, so that the prediction of the performance parameters of fast recovery diode chips corresponding to different models, different transition zone sizes and different doping concentrations in the reverse recovery stage is of practical significance.

[0115] It is understandable that the ascending order of the model codes is the preset arrangement order of the model codes. For example, the models IN5817, C81-004 and MBR1654 of the fast recovery diode chips are assigned model codes A, B and C, respectively, where IN5817 corresponds to A, C81-004 corresponds to B, and MBR1654 corresponds to C. At this time, after the second sequential data set is aggregated in the order of AZ, the resulting sequential chip set is: the second sequential data set corresponding to the model code A, the second sequential data set corresponding to the model code B, and the second sequential data set corresponding to the model code C.

[0116] It should be noted that the sequential chip set includes multiple sequential chip data, and the multiple sequential chip data are sorted in ascending order of model code, first sequence column and second sequence column. In the foregoing embodiment of the present invention, for the convenience of sorting, the model, transition zone size and doping concentration of each fast recovery diode chip are coded. However, in the performance analysis model, in order to facilitate researchers to consult, all sequential chip data after the coding operation need to be translated into parameters that are easy for researchers to consult. For example, the model of the fast recovery diode chip corresponding to the sequential chip data is IN5817, the transition zone size is 40um, and the doping concentration is , then the sequential chip data is A-40-5-13. The encoding and translation operation is performed on A-40-5-13 to obtain the unit experimental parameters, namely: the model of the fast recovery diode chip is IN5817, the transition zone size is 40um, and the doping concentration is Therefore, performing the encoding operation and performing the encoding translation operation are inverse processes.

[0117] S2. Perform the following operations for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameter, build a detection circuit based on the rated conditions, and after installing the experimental chip into the detection circuit, perform preprocessing operations on the detection circuit with the experimental chip installed to obtain the experimental circuit.

[0118] Further, the preprocessing operation is performed on the detection circuit equipped with the experimental chip to obtain the experimental circuit, including:

[0119] Obtain a component set of a detection circuit, wherein the component set includes: a power supply, a first inductor, a second inductor, a switch, and an experimental chip, wherein in the detection circuit, the power supply, the first inductor, the second inductor, and the switch are connected in series, the circuit corresponding to the experimental chip is only connected in parallel with two ends of the second inductor, and the circuit corresponding to the experimental chip has and only has the experimental chip;

[0120] After confirming that the component set meets the pre-built electrical characteristic specifications, an available circuit is obtained, a switch corresponding to the available circuit is closed, and a charging operation is performed on the first inductor and the second inductor based on the available circuit after the switch is closed. When the first inductor and the second inductor in the available circuit are respectively the preset first saturated inductor and the second saturated inductor, the available circuit is disconnected to obtain a chip forward conduction circuit;

[0121] The pre-built monitoring unit is connected to the experimental chip in the chip forward conduction circuit, and after confirming that the experimental chip is in the preset forward conduction stage, the experimental circuit is obtained.

[0122] It is understandable that the detection circuit is a circuit constructed for detecting the physical and chemical properties of the experimental chip during the reverse recovery process. The first inductor is the inductor that determines the rated current stress in the rated conditions, and the second inductor is the inductor that determines the rated instantaneous current in the rated conditions. The electrical characteristic specifications are specifications that all components can be used normally. For example, if the voltage determined by the power supply in the rated conditions is 1500V, then the electrical characteristic specifications may include: rated voltage, temperature rise limit, insulation requirements, etc. The embodiment of the present invention does not elaborate on this. Different components correspond to different electrical characteristic specifications. The embodiment of the present invention confirms that the component set meets the pre-built electrical characteristic specifications, aiming to confirm that all components in the component set can work stably.

[0123] It is understandable that when the first inductor is the first saturated inductor and the second inductor is the second saturated inductor and then the available circuit is disconnected, the working process of the experimental chip in the forward conduction stage in the actual working state can be simulated. The first saturated inductor is the first inductor when it reaches the saturated state, and the second saturated inductor is the same, which will not be repeated here.

[0124] It should be noted that the monitoring unit is a unit used to monitor the performance of the experimental chip during the reverse recovery phase. The forward conduction phase is a state in which current is allowed to pass under the voltage applied by the power supply. The reverse recovery phase and the forward conduction phase are inherent performances of the fast recovery diode chip and will not be described in detail here.

[0125] S3. Obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under rated conditions.

[0126] The target parameter curve set includes: reverse current curve, reverse voltage curve and leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness.

[0127] Further, the step of obtaining a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under rated conditions includes:

[0128] Acquire a target parameter set, wherein the target parameter set includes a plurality of target parameters, and the plurality of target parameters are: reverse current, reverse voltage and leakage current, and the rated conditions include: a rated voltage corresponding to a power supply, a rated current stress corresponding to a first inductor, and a rated instantaneous current corresponding to a second inductor;

[0129] The monitoring start time is obtained by using the monitoring unit and the experimental circuit, and the following operations are performed on each target parameter in the target parameter set based on the monitoring start time:

[0130] A target parameter coordinate system is constructed according to the preset monitoring frequency, target parameter and monitoring start time, wherein the horizontal axis in the target parameter coordinate system is time, the time corresponding to the origin is the monitoring start time, and the vertical axis is the target parameter;

[0131] Based on the monitoring start time, the experimental circuit is monitored in real time, and a switch in the experimental circuit is closed during the monitoring of the experimental circuit to obtain real-time target parameter data, wherein the real-time target parameter data includes: real-time target parameters and real-time time;

[0132] When it is confirmed that the real-time target parameter in the real-time target parameter data reaches a preset stable state, the real-time time corresponding to the stable state is confirmed as the monitoring termination time;

[0133] Summarize the real-time target parameter data between the monitoring end time and the monitoring start time to obtain a real-time data set, map the real-time data set to the target parameter coordinate system, and fit the mapped real-time data set into a curve to obtain a target parameter curve;

[0134] The target parameter curves are summarized to obtain a target parameter curve set, and the target parameter curve set is parsed to obtain a detection parameter set corresponding to the target parameter curve set.

[0135] It is understandable that the monitoring start time is the time when the monitoring unit starts to monitor the experimental circuit, and the embodiment of the present invention initializes the monitoring start time to 0us. Since the process of the experimental chip entering the reverse recovery phase is a process with microseconds as the time unit, in order to facilitate the monitoring of the entire process of the experimental chip entering the reverse recovery phase, the experimental circuit is monitored before the experimental chip enters the reverse recovery phase and before the switch in the experimental circuit is closed, thereby avoiding the loss of data at the moment when the experimental chip enters the reverse recovery phase.

[0136] It should be noted that, without affecting the authenticity of the target parameter curve, the method for obtaining the target parameter curve of each target parameter in the target parameter set in the embodiment of the present invention is consistent, because in the reverse recovery stage of the experimental chip, the change trend of the target parameter in the target parameter set is basically consistent. That is, before closing the switch in the experimental circuit, the target parameters in the target parameter set can be considered to be in a stable state and approximately regarded as constants. During the reverse recovery stage, the target parameters in the target parameter set change with time. After the experimental chip recovers stability again, the target parameters in the target parameter set are stable.

[0137] Specifically, the monitoring frequency is the frequency at which the monitoring unit monitors the target parameters in the target parameter set, and the real-time target parameter data is the real-time data obtained by the monitoring unit monitoring the experimental circuit in real time. For example, if the target parameter is the reverse voltage and the real-time moment is 10 microseconds, the real-time target parameter data includes 10 microseconds and the reverse voltage monitored by the monitoring unit at 10 microseconds. The preset stable state is: after the experimental chip is restored to stability again, the target parameters in the target parameter set are all stable. Due to the characteristics of the reverse recovery diode, the real-time target parameters corresponding to the stable state at this time can be approximately regarded as constants, but the monitoring unit has errors and noises in the monitoring process, so the real-time target parameters in the real-time target parameter data are not uniquely determined constants. Therefore, if you want to determine that the experimental chip is restored to stability again, you need to make a stability judgment on the real-time target parameters.

[0138] It should be noted that the monitoring termination time is the time when the monitoring unit stops monitoring the experimental circuit, that is, the real time corresponding to reaching a stable state.

[0139] It is understandable that the real-time data set includes multiple real-time target parameter data, and the real-time moment of each of the multiple real-time target parameter data is used as the horizontal coordinate in the target parameter coordinate system, and the real-time target parameter of the real-time target parameter data is used as the vertical coordinate in the target parameter coordinate system, and mapped to the target parameter coordinate system, and the real-time data set after mapping is fitted into a curve to obtain a target parameter curve. The method of fitting into a curve can be implemented by a variety of existing technologies, which will not be repeated here.

[0140] Specifically, the reverse recovery time is the time required to switch from the forward conduction state to the reverse cutoff state extracted from the target parameter curve set. The target parameter curve set of the embodiment of the present invention includes three different curves, so that three reverse recovery times can be extracted, and the average of the three reverse recovery times is calculated, and the average is confirmed as the reverse recovery time in the detection parameter set. The technology for parsing the reverse recovery time from the target parameter curve is prior art and will not be described in detail here. The reverse recovery start time is the start time when the experimental chip enters the reverse recovery stage, which is different from the monitoring start time. The reverse recovery end time is the same and will not be described in detail here.

[0141] Furthermore, the recovery softness refers to the smoothness of the decrease of the reverse recovery current when the experimental chip switches from the conduction state to the reverse blocking state. The calculation method thereof is prior art and will not be described in detail here.

[0142] Further, when the real-time target parameter in the real-time target parameter data is confirmed to have reached a preset stable state, the real-time moment corresponding to the stable state is confirmed as the monitoring termination moment, including:

[0143] Acquire a stability determination window, wherein the stability determination window includes a preset number of blank arrays;

[0144] The real-time target parameter is mapped into the stability determination window to obtain an initial determination window, and a plurality of real-time target parameter data adjacent to the real-time target parameter data are obtained based on the real-time target parameter data and a preset number to obtain a plurality of adjacent sequence data, wherein the sum of the number of the plurality of adjacent sequence data and the number of the real-time target parameter data is equal to the preset number;

[0145] Extracting the real-time target parameter corresponding to each adjacent sequence data in sequence from a plurality of adjacent sequence data, and mapping the extracted real-time target parameters to the initial determination window to obtain a window to be determined, wherein the window to be determined includes a plurality of parameters to be determined, and the plurality of parameters to be determined are sorted in a time-ordered order, and the first parameter to be determined in the plurality of parameters to be determined is the real-time target parameter corresponding to the real-time target parameter data;

[0146] Obtain a fitting curve based on all parameters to be determined in the window to be determined, and calculate a characteristic parameter set of the fitting curve;

[0147] Extract the earliest adjacent sequence data in time from multiple adjacent sequence data, and obtain the real-time target parameters corresponding to the earliest adjacent sequence data, obtain the updated target parameters, use the updated target parameters as the real-time target parameters, return to the step of mapping the real-time target parameters to the stability determination window, until the number of calculated feature parameter sets reaches the preset number of stability evaluations, and obtain the evaluation parameter set;

[0148] Extracting multiple target feature parameter sets from the evaluation parameter set, calculating the standard deviation corresponding to each target feature parameter set in the multiple target feature parameter sets, and obtaining multiple feature standard deviations, wherein the feature standard deviations correspond to the target feature parameter sets one by one;

[0149] If the plurality of characteristic standard deviations are all smaller than the corresponding preset fluctuation thresholds, it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state; otherwise, the process returns to the step of extracting the earliest adjacent sequential data among the plurality of adjacent sequential data until it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state;

[0150] The real time corresponding to reaching the stable state is confirmed as the monitoring termination time.

[0151] It is understandable that the stability determination window is a window composed of multiple blank arrays. After mapping the real-time target parameter to the position corresponding to the first blank array in the stability determination window, the obtained stability determination window is the initial determination window. For example, assuming the preset number is 4, the stability determination window is: (null, null, null, null), that is, there are four blank arrays in the stability determination window, and the four blank arrays are all empty values. If the real-time target parameter is a, then a is mapped to the position corresponding to the first blank array in the stability determination window, and the obtained initial determination window is: (a, null, null, null).

[0152] Exemplarily, assuming that the preset number is 3, the real-time moment corresponding to the real-time target parameter data is 50us, and the monitoring frequency of the monitoring unit is 1us / time, then the multiple real-time target parameter data adjacent to the real-time target parameter data are obtained based on the real-time target parameter data and the preset number, and the multiple adjacent sequence data are obtained as follows: based on the monitoring frequency and the real-time moment of 50us, the real-time target parameter data with the real-time moment of 51us and the real-time moment of 52us are respectively obtained to obtain multiple adjacent sequence data. At this time, the number of multiple adjacent sequence data is 2, and the sum of the number of multiple adjacent sequence data and the number of real-time target parameter data is 3, which is consistent with the preset number.

[0153] It is understandable that the method of mapping the extracted real-time target parameters to the initial determination window is consistent with the method of mapping the real-time target parameters to the stability determination window, and will not be repeated here.

[0154] It should be noted that the acquisition of the fitting curve based on all the parameters to be determined in the window to be determined is: using a pre-built curve fitting method to fit all the parameters to be determined in the window to be determined into a curve, and the fitted curve is the fitting curve. The curve fitting method is a prior art, and there are many prior arts that can be implemented, which will not be repeated here.

[0155] Exemplarily, suppose the stability determination window is: (null, null, null), and the real-time data set is sorted in chronological order according to the real-time moment: a, b, c, d, e, f, g, then the initial determination window is: (a, null, null), correspondingly, the window to be determined is: (a, b, c), b and c are multiple adjacent sequential data, then the first feature parameter set can be calculated at this time, and the adjacent sequential data with the earliest time among the multiple adjacent sequential data is extracted, that is, b is extracted from b and c, and b is the updated target parameter at this time, and the updated target parameter is the real-time target parameter, and after returning to the step of mapping the real-time target parameter to the stability determination window, the second window to be determined is: (b, c, d), corresponding to the second feature parameter set, and so on. If the preset number of stability evaluations is 3, then when the third window to be determined is: (c, d, e), and corresponds to the third feature parameter set, the first feature parameter set, the second feature parameter set and the third feature parameter set are summarized to obtain the evaluation parameter set.

[0156] It is understandable that the preset number of stability evaluations is a parameter used to control the number of characteristic parameter sets. The experimental chip regaining stability is a process state, that is, when the real-time target parameter remains basically uniform for a period of time, it is considered to be a stable state. Therefore, it is necessary to calculate the characteristic parameter sets corresponding to multiple windows to be determined, and to determine whether the experimental chip is in a stable state at the real-time target parameter by analyzing multiple characteristic parameter sets. The embodiment of the present invention calculates multiple characteristic standard deviations, and believes that when multiple standard deviations are all less than the corresponding preset fluctuation thresholds, the real-time moment corresponding to the real-time target parameter is the monitoring termination moment, that is, at this time, the experimental chip has regained stability again. Therefore, according to the definition of the initial determination window in the embodiment of the present invention, the preset number of stability evaluations also controls the length of the period of time in the period of time after the real-time target parameter.

[0157] It is understandable that the preset numbers and preset numbers of stability evaluations used in the embodiments of the present invention are merely for the sake of illustration. In actual calculations, too large a preset number and a preset number of stability evaluations can improve the accuracy of stability judgments, but will also increase the computing energy consumption of the computer. While too small a preset number and a preset number of stability evaluations can reduce the computing energy consumption of the computer, they will also reduce the accuracy of judgments. Therefore, the preset numbers and the preset number of stability evaluations should be reasonably selected based on experiments, and the embodiments of the present invention do not limit the specific values.

[0158] Specifically, the feature parameter set includes multiple feature parameters, and the multiple feature parameters are different from each other, and each feature parameter can be used to determine whether the real-time target parameter has reached a stable state. The target feature parameter set includes multiple target feature parameters, and the multiple target feature parameters are feature parameters corresponding to different windows to be determined. For example, the feature parameters in the feature parameter set are: feature integral and feature derivative, and the multiple target feature parameter sets are: a set composed of feature integrals and a set composed of feature derivatives. The feature standard deviation is the standard deviation of all target feature parameters in the target feature parameter set. The method for calculating the standard deviation is a prior art and will not be repeated here.

[0159] Further, the preset fluctuation threshold is the fluctuation range allowed by different target characteristic parameters preset manually, and the preset fluctuation thresholds corresponding to different target characteristic parameter sets are different. For example, when the target characteristic parameter is a characteristic integral, the preset fluctuation threshold is: when the real-time target parameter is in a stable state, the maximum standard deviation allowed by multiple characteristic integrals. When the standard deviation of multiple characteristic integrals is less than the preset fluctuation threshold, it is considered that the real-time target parameter in the real-time target parameter data has reached a stable state. Otherwise, it means that the real-time target parameter in the real-time target parameter data has not reached a stable state. Further, the meaning of returning to the step of extracting the earliest adjacent sequential data in time among the multiple adjacent sequential data is: when it is confirmed that the real-time target parameter in the target parameter data has not reached a stable state, the next target parameter data adjacent to the target parameter data is used as the target parameter data, and a cyclic calculation is performed until a real-time target parameter corresponding to a target parameter data is found to have reached a stable state, and the real-time moment corresponding to the stable state is confirmed as the monitoring termination moment.

[0160] Exemplarily, for ease of explanation, continuing the parameters in the previous example, assume that the preset number is 3, the preset number of stability evaluations is also 3, the first window to be determined is: (a, b, c), corresponding to the first feature parameter set, the second window to be determined is: (b, c, d), corresponding to the second feature parameter set, the third window to be determined is: (c, d, e), corresponding to the third feature parameter set, the evaluation parameter set is: the first feature parameter set, the second feature parameter set and the third feature parameter set, assume that the feature parameter set includes feature integrals and feature derivatives, then there are two target feature parameter sets at this time, of which the first target feature parameter set is: the first feature integral, the second feature integral and the third feature integral, and the second target feature parameter set is: the first feature derivative, the second feature derivative and the third feature derivative. The characteristic standard deviations of the first target characteristic parameter set and the second target characteristic parameter set are calculated respectively to obtain the characteristic standard deviation of the characteristic integral and the characteristic standard deviation of the characteristic derivative. If the characteristic standard deviation of the characteristic integral is less than the corresponding preset fluctuation threshold, and the characteristic standard deviation of the characteristic derivative is less than the corresponding preset fluctuation threshold, then it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state. Otherwise, return to the step of extracting the earliest adjacent sequential data in time among the multiple adjacent sequential data. At this time, the first window to be determined constructed is: (b, c, d)), the second window to be determined is: (c, d, e), corresponding to the second characteristic parameter set, the third window to be determined is: (d, e, f), and so on. Examples will not be given one by one here.

[0161] Furthermore, the characteristic parameter set of the calculated fitting curve includes:

[0162] Calculate the characteristic integral and characteristic derivative of the fitting curve, where the calculation formulas for the characteristic integral and characteristic derivative are as follows:

[0163] ;

[0164] ;

[0165] in, represents the characteristic integral, Indicates the earliest parameter to be determined in the window to be determined. The latest parameter to be determined in the determination window. represents the fitting curve, The corresponding judgment time in the table fitting curve is represents the characteristic derivative;

[0166] The characteristic integrals and characteristic derivatives are summarized to obtain the characteristic parameter set of the fitting curve.

[0167] Exemplarily, if the window to be determined is: (a, b, c), since the parameters to be determined in the window to be determined are sorted in chronological order, the earliest parameter to be determined is a, and the latest parameter to be determined is c.

[0168] S4. Associating the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model.

[0169] It is understandable that the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip are associated to obtain a unit model, including:

[0170] The characteristic data corresponding to the target parameter curve set, experimental parameters, detection parameter set and experimental chip are calculated respectively to obtain a characteristic data set, and a unique identifier is calculated using the characteristic data set and a pre-constructed unique identifier calculation formula. The target parameter curve set, experimental parameters, detection parameter set and experimental chip are all identified using the unique identifier, and the identified target parameter curve set, experimental parameters, detection parameter set and experimental chip are summarized to obtain a unit model.

[0171] Exemplarily, the characteristic data of the target parameter curve set are: reverse peak voltage, reverse peak current and maximum leakage current, the characteristic data of the detection parameter set are: reverse recovery time and recovery softness, the characteristic data of the experimental parameters are: model code, transition zone size code and doping concentration code, the characteristic data of the experimental chip is the model of the fast recovery diode chip, so the characteristic data set is: reverse peak voltage, reverse peak current, maximum leakage current, reverse recovery time, recovery softness, model code, transition zone size code, doping concentration code and the model of the fast recovery diode chip. The unique identifier calculation formula is a formula that uses a function to calculate the characteristic data set into a unique identifier. This technology is a prior art and will not be repeated here. It is also an existing technology to use a unique identifier to perform identification operations on a target parameter curve set, experimental parameters, detection parameter set and experimental chip. Its purpose is to use the unique identifier as an index code for the target parameter curve set, experimental parameters, detection parameter set and experimental chip, so that after researchers enter the index code, they can display the target parameter curve set, experimental parameters, detection parameter set and experimental chip. Therefore, the set composed of the identified target parameter curve set, experimental parameters, detection parameter set and experimental chip is a unit model.

[0172] S5. Summarize the unit models to obtain the initial model.

[0173] It is understandable that, in the embodiment of the present invention, each experimental parameter in the experimental parameter set corresponds to a unit model, and the unit models are aggregated according to the arrangement order of the experimental parameters, and the obtained set is the initial model.

[0174] S6. Construct an amplification parameter sequence set based on the experimental parameter set.

[0175] Further, constructing an amplification parameter sequence set based on the experimental parameter set includes:

[0176] Extract all doping concentrations from the experimental parameter set to obtain a doping concentration set, perform a recursive operation on the doping concentration set to obtain multiple concentration recursive values, perform a sorting operation on the multiple concentration recursive values ​​in order from small to large, and obtain a concentration recursive sequence, and perform the following operations on the concentration recursive values ​​in the concentration recursive sequence:

[0177] If the concentration recursive value is not the concentration recursive value with the largest value in the concentration recursive sequence, then extract the next concentration recursive value adjacent to the concentration recursive value from the concentration recursive sequence to obtain an adjacent concentration value, calculate the difference between the adjacent concentration value and the concentration recursive value to obtain a concentration interval, and if the concentration recursive value is the concentration recursive value with the largest value in the concentration recursive sequence, then skip the concentration recursive value;

[0178] Summarize the concentration intervals to obtain a concentration interval set, calculate the greatest common divisor of the concentration interval set, and obtain the initial concentration amplification unit;

[0179] Determining whether the initial concentration amplification unit is less than a preset concentration amplification threshold;

[0180] If the initial concentration amplification unit is less than the concentration amplification threshold, performing a concentration value amplification operation on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence;

[0181] Otherwise, the concentration value amplification operation is performed on the concentration recursive sequence using the initial concentration amplification unit to obtain a concentration amplification sequence;

[0182] Constructing a size amplification sequence based on the size recursive sequence, extracting size amplification values ​​from the size recursive sequence in sequence, and associating the extracted size amplification values ​​with the concentration amplification sequence to obtain a size-concentration set, wherein the concentration amplification sequence includes a plurality of concentration amplification values, the size-concentration set includes a plurality of size-concentration parameters, and one size-concentration parameter includes: a size amplification value and a concentration amplification value;

[0183] Summarizing the size-concentration sets to obtain the size-concentration amplification sets corresponding to the size amplification sequences;

[0184] Extract all the fast recovery diode chip models from the experimental parameter set, identify the model code corresponding to each fast recovery diode chip model among all the fast recovery diode chip models, and perform a sorting operation on all the fast recovery diode chip models in ascending order according to the model code to obtain a chip model set, associate the chip model set with the size-concentration amplification set to obtain an amplification parameter sequence set.

[0185] It is understandable that the method of performing a recursive operation on the doping concentration set is consistent with the method of performing a recursive operation on the first sequence column, and the effect achieved is the same, which will not be repeated here. The method of performing a sorting operation on multiple concentration recursive values ​​in order from small to large is consistent with the method of performing a sorting operation on the target data set using the second sequence column, and the effect achieved is the same, which will not be repeated here.

[0186] It should be noted that there are multiple different concentration recursive values ​​in the concentration recursive sequence. The purpose of constructing the amplification parameter sequence set is to interpolate between multiple different concentration recursive values ​​and different size recursive values, thereby achieving amplification of the experimental parameter set.

[0187] For example, if the concentration recursive sequence is: , , , , ), for the convenience of explanation, the units in the concentration recursive sequence are removed, and the concentration recursive sequence is: ( , , , , ),in, is the maximum recursive concentration value. Let the extracted recursive concentration value , is not the maximum recursive concentration value, so the extraction is The next adjacent concentration recursive value , that is, the adjacent concentration value is , calculate the difference between the adjacent concentration values ​​and the concentration recursive value, and the concentration interval is , and so on, when the concentration recursive value is , then skip The concentration recursive value of , summarizes the concentration intervals, and obtains the concentration interval set. At this time, the concentration interval set is: ( , , 1 , 3 ), the greatest common divisor of the concentration interval set is , so the initial concentration amplification unit is .

[0188] It can be understood that the concentration amplification threshold is a value used to control the accuracy of the amplification parameters in the amplification parameter sequence set. If the amplification concentration threshold is too small, for example, the amplification concentration threshold is 500, then multiple invalid values ​​will appear in the multiple concentration amplification values ​​included in the obtained concentration amplification sequence. For example, the existing concentration interval set is: ( , , 1 , 3 ), where the greatest common divisor is ,by As the initial concentration amplification unit, Based on the doping concentration, the cumulative Each time, a concentration amplification value is obtained until the cumulative result is , at this time and Multiple concentration amplification values ​​are interpolated between the multiple concentration amplification values, and between the multiple concentration amplification values, the performance of the experimental chip corresponding to two adjacent concentration amplification values ​​in the reverse recovery phase is not much different, so it is necessary to set a concentration amplification threshold.

[0189] For example, let the concentration recursive sequence be: , , , , ), the concentration interval set is: ( , , 1 , 3 ), the initial concentration amplification unit is , assuming the preset concentration amplification threshold is , so use The operation to expand the concentration interval set is: and Add together to get the concentration amplification value , and then and Add together to get the concentration amplification value , and so on, until the accumulated concentration amplification value is , summarize the concentration amplification values, and the obtained concentration amplification sequence is: ( , , , , , , , , , , , , ).

[0190] It should be noted that the construction method of the size amplification sequence and the concentration amplification sequence are consistent and have the same effect, so they will not be repeated here.

[0191] Exemplarily, the operation of associating each extracted size amplification value with a concentration amplification sequence to obtain a size-concentration set is: assuming that the extracted size amplification value is 50, and the concentration amplification sequence is: (a, b, c), then the size amplification value a is associated with the concentration amplification sequence as follows: extract the concentration amplification values ​​in the concentration amplification sequence in sequence, and associate them with the extracted size amplification value, so the size-concentration set is: (50-a, 50-b, 50-c).

[0192] It is understandable that the size-concentration amplification set is a set of multiple size-concentration sets, and the chip model set is a set recursively derived from the models of fast recovery diode chips. For example, the models of all fast recovery diode chips include: IN5817 and C81-004, then the chip model set is: (IN5817, C81-004). Associating the chip model set with the size-concentration amplification set is consistent with the method of associating each extracted size amplification value with a concentration amplification sequence, and can achieve the same effect, which will not be repeated here.

[0193] For example, suppose that the models of all fast recovery diode chips are IN5817 and C81-004, the model code of IN5817 is A, and the model code of C81-004 is B. Then, IN5817 and C81-004 are sorted in ascending order according to the model code, and the obtained chip model set is: (IN5817, C81-004). Suppose the size amplification sequence is: (40, 50), and the concentration amplification sequence is: (a, b, c), then there are two size-concentration sets, namely: (40-a, 40-b, 40-c) and (50-a, 50-b, 50-c), At this time, the size-concentration amplification set is (40-a, 40-b, 40-c, 50-a, 50-b, 50-c) and the amplification parameter sequence set is: (IN5817-40-a, IN5817-40-b, IN5817-40-c, IN5817-50-a, IN5817-50-b, IN5817-50-c, C81-004-40-a, C81-004-40-b, C81-004-40-c, C81-004-50-a, C81-004-50-b, C81-004-50-c).

[0194] Furthermore, the concentration value amplification operation is performed on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence, including:

[0195] Extract the concentration recursive values ​​from the concentration recursive sequence in sequence to obtain the target concentration value, and perform the following operations on the target concentration value:

[0196] Extracting the next concentration recursive value adjacent to the target concentration value from the concentration recursive sequence to obtain a comparison concentration value, performing a cumulative operation on the target concentration value using a concentration amplification threshold to obtain a cumulative concentration value, and calculating the difference between the comparison concentration value and the cumulative concentration value to obtain a residual cumulative total value;

[0197] If the remaining cumulative total value is less than or equal to the concentration amplification threshold, the target concentration value is confirmed as the cumulative concentration value;

[0198] If the remaining accumulated total value is greater than the concentration amplification threshold, the target concentration value is confirmed as the accumulated concentration value, and the accumulated concentration value is used as the target concentration value, and the process returns to the step of performing an accumulation operation on the target concentration value using the concentration amplification threshold;

[0199] The accumulated concentration values ​​and the concentration recursive sequence are summarized, and a deduplication operation is performed on the summarized accumulated concentration values ​​and the concentration recursive sequence to obtain a concentration amplification sequence.

[0200] It is understandable that the deduplication operation is the operation of taking the accumulated concentration value after the data is aggregated and the same value in the concentration recursive sequence. The accumulation operation of the target concentration value using the concentration amplification threshold is: adding the concentration amplification threshold to the target concentration value, and the obtained value is the accumulated concentration value.

[0201] For example, referring to the above examples of the embodiments of the present invention, the concentration recursive sequence is: ( , , , , ), the concentration interval set is: ( , , 1 , 3 ), assuming the concentration amplification threshold is , using the concentration amplification threshold Perform an amplification operation on a concentration recursive sequence.

[0202] For example, the extracted target concentration value is , extract the next concentration recursive value adjacent to the target concentration value in the concentration recursive sequence , then the comparison concentration value is The cumulative concentration value is Calculate comparative concentration values With the cumulative concentration value The difference between the remaining cumulative total is 0, which is less than or equal to the concentration amplification threshold. Confirm as the cumulative concentration value. At this time, there are two cumulative concentration values, namely: and By analogy, the fourth target concentration value is extracted as , the comparison concentration value is The cumulative concentration value is The remaining cumulative total is , at this time, the remaining cumulative total value is greater than the concentration amplification threshold, and two cumulative concentration values ​​are added: and .by The step of performing the accumulation operation on the target concentration value by using the concentration amplification threshold is returned for the target concentration value, that is, the fifth target concentration value is confirmed as , at this time the comparison concentration value is still The corresponding cumulative concentration value is The remaining cumulative total is , which is equal to the concentration amplification threshold, then the two cumulative concentration values ​​added again are and . By analogy, the concentration amplification sequence after deduplication is: ( , , , , , , , , ).

[0203] S7. Construct an amplification parameter model based on the amplification parameter sequence set and the initial model, extract a blank model set from the amplification parameter model, and use the initial model to perform a filling operation on the blank model set to obtain a prediction model set.

[0204] Furthermore, the amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model, and the blank model set is filled with the initial model to obtain the prediction model set, including:

[0205] Building an initial amplification parameter model based on the amplification parameter sequence set, extracting all unit models from the initial model to obtain a plurality of actual amplification parameter models, and mapping the plurality of actual amplification parameter models to the initial amplification parameter model to obtain an initial filling amplification model;

[0206] Using preset training sample parameters to divide the multiple actual amplification parameter models, obtaining a training sample set and a verification sample set, using the training sample set to train a pre-constructed initial performance analysis model, and using the verification sample set to perform an iterative operation on the trained initial performance analysis model to obtain an optimized performance analysis model, wherein the initial performance analysis model is a pre-constructed neural network construction model;

[0207] Extracting a blank model set from the initial fill-in augmentation model, and performing interpolation simulation operations on the blank models in the blank model set using a pre-built interpolation simulation method and a plurality of actual augmentation parameter models, to obtain a first prediction model set;

[0208] The optimized performance analysis model is used to perform prediction operations on the blank models in the blank model set to obtain a second prediction model set, and a weighted integration operation is performed on the first prediction model set and the second prediction model set to obtain a prediction model set.

[0209] It is understandable that the initial amplification parameter model includes multiple unit initial amplification models, the amplification parameter sequence set includes multiple amplification parameters, and the amplification parameters correspond to the unit initial amplification models one by one. In the embodiment of the present invention, the initial model is obtained by the detection circuit, that is, all unit models in the initial model are composed of data obtained by actual detection of the detection circuit, and the embodiment of the present invention obtains the amplification parameter sequence set after amplifying the experimental parameter set, so each unit initial amplification model in the amplification parameter sequence set should have the same structure as the unit model, and the meaning of the same structure is: in the embodiment of the present invention, the unit model is a set composed of the target parameter curve set, experimental parameters, detection parameter set and experimental chip after identification, so the unit initial amplification model should be: a set composed of the target parameter curve set, experimental parameters, detection parameter set and experimental chip obtained based on the amplification parameters corresponding to the unit initial amplification model, and the target parameter curve set, experimental parameters, detection parameter set and experimental chip in the unit initial amplification model are all associated by amplification parameters.

[0210] It is understandable that in the embodiment of the present invention, the initial model is a subset of the amplification parameter model, and multiple actual amplification parameter models corresponding to the initial model are mapped to the initial amplification parameter model to obtain the initial filled amplification model. That is, the unit model corresponding to the actual amplification parameter model is found in the initial amplification parameter model, and the data contained in the unit model is mapped to the initial amplification parameter model.

[0211] Specifically, the preset training sample parameter is a numerical value set artificially for dividing multiple actual amplification parameter models. For example, if the preset training sample parameter is 0.2, 20% of the actual amplification parameter models are randomly extracted from the multiple actual amplification parameter models as the verification sample set, and the remaining 80% of the actual amplification parameter models are used as the training sample set. Since the technology of using the training sample set to train the pre-built initial performance analysis model and using the verification sample set to perform iterative operations on the trained initial performance analysis model is an existing technology, it will not be repeated here.

[0212] It is understandable that in the previous examples of the embodiments of the present invention, the characteristic data included in the unit model are exemplified as a set consisting of: reverse peak voltage, reverse peak current, maximum leakage current, reverse recovery time, recovery softness, model code, transition zone size code, doping concentration code and the model of the fast recovery diode chip. Therefore, it is necessary to distinguish the input and output of the initial performance analysis model. In the embodiments of the present invention, the model, transition zone size and doping concentration are input quantities, and when performing the reverse recovery operation, the data obtained are output quantities, such as: reverse peak voltage, reverse peak current, maximum leakage current, reverse recovery time and recovery softness, etc.

[0213] Further, the blank model set is the complement corresponding to multiple actual amplification parameter models in the initial filling amplification model. It is understandable that the interpolation simulation method can be implemented by a variety of existing technologies, such as the Lagrange polynomial interpolation method, and the embodiment of the present invention is not limited to this.

[0214] It can be understood that in the embodiment of the present invention, two different methods are used to interpolate the blank model set to obtain the first prediction model set and the second prediction model set, respectively, and the first weight and the second weight are set for the first prediction model set and the second prediction model set respectively. After the first prediction model set and the second prediction model set are integrated using the first weight and the second weight, the obtained model is the prediction model.

[0215] Exemplarily, the blank model is a blank model with amplification parameters a, b and c, and the output to be predicted is the reverse peak voltage. Therefore, the reverse peak voltage of the first prediction model calculated by the interpolation simulation method is M, and the second prediction model predicted by the blank model using the optimized performance analysis model is N. The first weight of the first prediction model is 0.5, and the second weight of the second prediction model is 0.5. Therefore, the weighted integrated prediction model is K, and K=0.5*M+0.5*N.

[0216] S8. Summarize and integrate the prediction model set and the initial model to obtain a performance analysis model, and complete the construction of a performance analysis model based on the fast recovery diode chip based on the performance analysis model.

[0217] It is understandable that after merging the prediction model set with the initial model, the resulting set is a performance analysis model.

[0218] In order to solve the problem described in the background technology, the present invention obtains a fast recovery diode chip set, performs classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and constructs an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes multiple fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition zone size and a doping concentration. It can be seen that the present invention performs encoding operations on the model, transition zone size and doping concentration of each fast recovery diode chip for the convenience of sorting, but in the performance analysis model, in order to facilitate researchers to consult, all sequential chip data after the encoding operation are translated into parameters that are easy for researchers to consult. The following operations are performed for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameter, construct a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform preprocessing operations on the detection circuit with the experimental chip installed to obtain the experimental circuit. It can be seen that the present invention constructs an experimental circuit for simulating the normal operation of the fast recovery diode chip, and, in order to facilitate monitoring of the process of the experimental chip entering the reverse recovery stage, the embodiment of the present invention starts to monitor the experimental circuit before the experimental chip enters the reverse recovery stage and before closing the switch in the experimental circuit, so as to avoid losing the data at the moment of entering the reverse recovery stage. According to the data, the target parameter curve set and the detection parameter set are obtained after the reverse recovery operation is performed on the experimental circuit under rated conditions, and the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip are associated to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness. It can be seen that the present invention monitors the performance parameters of the experimental chip in the reverse recovery stage, and integrates the parameters into a unit model. At the same time, according to the characteristics of the fast recovery diode chip in the reverse recovery stage, the stability judgment window is used to adaptively determine the monitoring termination time, and the unit models are summarized to obtain the initial model. An amplification parameter sequence set is constructed based on the experimental parameter set. An amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a filling operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. The performance analysis model based on the fast recovery diode chip is constructed based on the performance analysis model. The present invention explores the performance of fast recovery diode chips with different models, different transition zone sizes and different doping concentrations of fast recovery diode chips through experiments, and predicts the performance of fast recovery diode chips with different transition zone sizes and different doping concentrations under known fast recovery diode chip models according to the initial model obtained from the experiment.Therefore, the present invention can predict the performance of fast recovery diode chips with different transition region sizes and different doping concentrations in the reverse recovery stage when the diode chip model is known.

[0219] like Figure 2 1 is a functional module diagram of a system for constructing a performance analysis model based on a fast recovery diode chip provided by an embodiment of the present invention.

[0220] The performance analysis model construction system 100 based on the fast recovery diode chip of the present invention can be installed in an electronic device. According to the functions to be implemented, the performance analysis model construction system 100 based on the fast recovery diode chip can include a diode chip acquisition module 101, a performance test module 102, an initial model acquisition module 103 and a performance analysis model construction module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0221] The diode chip acquisition module 101 is used to acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration;

[0222] The performance test module 102 is used to perform the following operations for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameter, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, and obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions;

[0223] The initial model acquisition module 103 is used to associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: reverse current curve, reverse voltage curve and leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness, and summarize the unit models to obtain the initial model;

[0224] The performance analysis model construction module 104 is used to construct an amplification parameter sequence set based on the experimental parameter set, construct an amplification parameter model based on the amplification parameter sequence set and the initial model, extract a blank model set from the amplification parameter model, use the initial model to perform a filling operation on the blank model set to obtain a prediction model set, summarize and integrate the prediction model set and the initial model to obtain a performance analysis model, and complete the construction of a performance analysis model based on the fast recovery diode chip based on the performance analysis model.

[0225] In detail, each module in the performance analysis model construction system 100 based on the fast recovery diode chip in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the method for building a performance analysis model based on a fast recovery diode chip described in the specification and can produce the same technical effects will not be repeated here.

[0226] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a method for constructing a performance analysis model based on a fast recovery diode chip provided by an embodiment of the present invention.

[0227] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for constructing a performance analysis model based on a fast recovery diode chip.

[0228] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the performance analysis model construction method program based on the fast recovery diode chip, but also can be used to temporarily store data that has been output or is to be output.

[0229] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, a performance analysis model construction method program based on a fast recovery diode chip, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0230] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0231] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0232] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0233] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0234] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0235] The program of the method for constructing a performance analysis model based on a fast recovery diode chip stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0236] Acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration;

[0237] For each experimental parameter in the experimental parameter set, perform the following operations:

[0238] Confirm the fast recovery diode chip corresponding to the experimental parameters, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameters, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions, associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness;

[0239] The step of performing a preprocessing operation on the detection circuit equipped with the experimental chip to obtain the experimental circuit includes:

[0240] Obtain a component set of a detection circuit, wherein the component set includes: a power supply, a first inductor, a second inductor, a switch, and an experimental chip, wherein in the detection circuit, the power supply, the first inductor, the second inductor, and the switch are connected in series, the circuit corresponding to the experimental chip is only connected in parallel with two ends of the second inductor, and the circuit corresponding to the experimental chip has and only has the experimental chip;

[0241] After confirming that the component set meets the pre-built electrical characteristic specifications, an available circuit is obtained, a switch corresponding to the available circuit is closed, and a charging operation is performed on the first inductor and the second inductor based on the available circuit after the switch is closed. When the first inductor and the second inductor in the available circuit are respectively the preset first saturated inductor and the second saturated inductor, the available circuit is disconnected to obtain a chip forward conduction circuit;

[0242] Connecting the pre-built monitoring unit to the experimental chip in the chip forward conduction circuit, and after confirming that the experimental chip is in the preset forward conduction stage, obtaining the experimental circuit;

[0243] Aggregate the unit models to obtain the initial model;

[0244] An amplification parameter sequence set is constructed based on the experimental parameter set, an amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a fill operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. Based on the performance analysis model, the construction of a performance analysis model based on the fast recovery diode chip is completed.

[0245] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0246] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0247] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0248] Acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration;

[0249] For each experimental parameter in the experimental parameter set, perform the following operations:

[0250] Confirm the fast recovery diode chip corresponding to the experimental parameters, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameters, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions, associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness;

[0251] The step of performing a preprocessing operation on the detection circuit equipped with the experimental chip to obtain the experimental circuit includes:

[0252] Obtain a component set of a detection circuit, wherein the component set includes: a power supply, a first inductor, a second inductor, a switch, and an experimental chip, wherein in the detection circuit, the power supply, the first inductor, the second inductor, and the switch are connected in series, the circuit corresponding to the experimental chip is only connected in parallel with two ends of the second inductor, and the circuit corresponding to the experimental chip has and only has the experimental chip;

[0253] After confirming that the component set meets the pre-built electrical characteristic specifications, an available circuit is obtained, a switch corresponding to the available circuit is closed, and a charging operation is performed on the first inductor and the second inductor based on the available circuit after the switch is closed. When the first inductor and the second inductor in the available circuit are respectively the preset first saturated inductor and the second saturated inductor, the available circuit is disconnected to obtain a chip forward conduction circuit;

[0254] Connecting the pre-built monitoring unit to the experimental chip in the chip forward conduction circuit, and after confirming that the experimental chip is in the preset forward conduction stage, obtaining the experimental circuit;

[0255] Aggregate the unit models to obtain the initial model;

[0256] An amplification parameter sequence set is constructed based on the experimental parameter set, an amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a fill operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. Based on the performance analysis model, the construction of a performance analysis model based on the fast recovery diode chip is completed.

[0257] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0258] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0259] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0260] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for constructing a performance analysis model based on a fast recovery diode chip, characterized in that: The method comprises: Acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition region size, and a doping concentration; For each experimental parameter in the experimental parameter set, perform the following operations: Confirm the fast recovery diode chip corresponding to the experimental parameters, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameters, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions, associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: a reverse current curve, a reverse voltage curve and a leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness; The step of performing a preprocessing operation on the detection circuit equipped with the experimental chip to obtain the experimental circuit includes: Obtain a component set of a detection circuit, wherein the component set includes: a power supply, a first inductor, a second inductor, a switch, and an experimental chip, wherein in the detection circuit, the power supply, the first inductor, the second inductor, and the switch are connected in series, the circuit corresponding to the experimental chip is only connected in parallel with two ends of the second inductor, and the circuit corresponding to the experimental chip has and only has the experimental chip; After confirming that the component set meets the pre-built electrical characteristic specifications, an available circuit is obtained, a switch corresponding to the available circuit is closed, and a charging operation is performed on the first inductor and the second inductor based on the available circuit after the switch is closed. When the first inductor and the second inductor in the available circuit are respectively the preset first saturated inductor and the second saturated inductor, the available circuit is disconnected to obtain a chip forward conduction circuit; Connecting the pre-built monitoring unit to the experimental chip in the chip forward conduction circuit, and after confirming that the experimental chip is in the preset forward conduction stage, obtaining the experimental circuit; Aggregate the unit models to obtain the initial model; An amplification parameter sequence set is constructed based on the experimental parameter set, an amplification parameter model is constructed based on the amplification parameter sequence set and the initial model, and a blank model set is extracted from the amplification parameter model. The initial model is used to perform a fill operation on the blank model set to obtain a prediction model set. The prediction model set and the initial model are summarized and integrated to obtain a performance analysis model. Based on the performance analysis model, the construction of a performance analysis model based on the fast recovery diode chip is completed.

2. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 1, characterized in that: The step of performing a classification and sorting operation on the fast recovery diode chip set to obtain a sequential chip set, and constructing an experimental parameter set based on the sequential chip set includes: Extract fast recovery diode chips from the fast recovery diode chip set in sequence to obtain the target chip, and perform the following operations on the target chip: Identify the model, transition region size and doping concentration of the target chip, and perform encoding operation on the model, transition region size and doping concentration of the target chip to obtain encoding data, wherein the encoding data includes: model code, transition region size code and doping concentration code; The coded data are respectively aggregated according to the model codes corresponding to the coded data to obtain a plurality of model data sets corresponding to the fast recovery diode chip set, wherein the model codes of all the model data in the model data set are the same; The following operations are performed on the model datasets in the multiple model datasets: Identify and extract the transition zone size code corresponding to each model data in the model data set to obtain the size code to be sorted, summarize all the size codes to be sorted in the order of the size codes to be sorted from small to large, obtain the first sequence column corresponding to the model data set, and use the first sequence column to perform a sorting operation on the model data set to obtain the first sequence data set; Perform recursive operations on the first sequence column to obtain a size recursive sequence, and perform the following operations on the size recursive values ​​in the size recursive sequence in turn: Extracting a target data set from the first sequential data set based on the size recursive value, performing a sorting operation on the target data set to obtain a concentration sequential data set, wherein the model codes and transition zone size codes corresponding to the target data in the target data set are the same; Summarizing the concentration sequential data set according to the order of the size recursive values ​​in the size recursive sequence to obtain a second sequential data set corresponding to the model data set; The second sequential data set is summarized in ascending order according to the preset model code to obtain a sequential chip set corresponding to the fast recovery diode chip set, sequential chip data is extracted from the sequential chip set in sequence, and a coding translation operation is performed based on the extracted sequential chip data to obtain unit experimental parameters, and the unit experimental parameters are summarized to obtain an experimental parameter set.

3. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 2, characterized in that: The step of obtaining a target parameter curve set and a detection parameter set after a reverse recovery operation is performed on the experimental circuit under rated conditions includes: Acquire a target parameter set, wherein the target parameter set includes a plurality of target parameters, and the plurality of target parameters are: reverse current, reverse voltage and leakage current, and the rated conditions include: a rated voltage corresponding to a power supply, a rated current stress corresponding to a first inductor, and a rated instantaneous current corresponding to a second inductor; The monitoring start time is obtained by using the monitoring unit and the experimental circuit, and the following operations are performed on each target parameter in the target parameter set based on the monitoring start time: A target parameter coordinate system is constructed according to the preset monitoring frequency, target parameter and monitoring start time, wherein the horizontal axis in the target parameter coordinate system is time, the time corresponding to the origin is the monitoring start time, and the vertical axis is the target parameter; Based on the monitoring start time, the experimental circuit is monitored in real time, and a switch in the experimental circuit is closed during the monitoring of the experimental circuit to obtain real-time target parameter data, wherein the real-time target parameter data includes: real-time target parameters and real-time time; When it is confirmed that the real-time target parameter in the real-time target parameter data reaches a preset stable state, the real-time time corresponding to the stable state is confirmed as the monitoring termination time; Summarize the real-time target parameter data between the monitoring end time and the monitoring start time to obtain a real-time data set, map the real-time data set to the target parameter coordinate system, and fit the mapped real-time data set into a curve to obtain a target parameter curve; The target parameter curves are summarized to obtain a target parameter curve set, and the target parameter curve set is parsed to obtain a detection parameter set corresponding to the target parameter curve set.

4. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 3, characterized in that: When the real-time target parameter in the real-time target parameter data is confirmed to have reached a preset stable state, the real-time time corresponding to the reaching of the stable state is confirmed as the monitoring termination time, including: Acquire a stability determination window, wherein the stability determination window includes a preset number of blank arrays; The real-time target parameter is mapped into the stability determination window to obtain an initial determination window, and a plurality of real-time target parameter data adjacent to the real-time target parameter data are obtained based on the real-time target parameter data and a preset number to obtain a plurality of adjacent sequence data, wherein the sum of the number of the plurality of adjacent sequence data and the number of the real-time target parameter data is equal to the preset number; Extracting the real-time target parameter corresponding to each adjacent sequence data in sequence from a plurality of adjacent sequence data, and mapping the extracted real-time target parameters to the initial determination window to obtain a window to be determined, wherein the window to be determined includes a plurality of parameters to be determined, and the plurality of parameters to be determined are sorted in a time-ordered order, and the first parameter to be determined in the plurality of parameters to be determined is the real-time target parameter corresponding to the real-time target parameter data; Obtain a fitting curve based on all parameters to be determined in the window to be determined, and calculate a characteristic parameter set of the fitting curve; Extract the earliest adjacent sequence data in time from multiple adjacent sequence data, and obtain the real-time target parameters corresponding to the earliest adjacent sequence data, obtain the updated target parameters, use the updated target parameters as the real-time target parameters, return to the step of mapping the real-time target parameters to the stability determination window, until the number of calculated feature parameter sets reaches the preset number of stability evaluations, and obtain the evaluation parameter set; Extracting multiple target feature parameter sets from the evaluation parameter set, calculating the standard deviation corresponding to each target feature parameter set in the multiple target feature parameter sets, and obtaining multiple feature standard deviations, wherein the feature standard deviations correspond to the target feature parameter sets one by one; If the plurality of characteristic standard deviations are all smaller than the corresponding preset fluctuation thresholds, it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state; otherwise, return to the step of extracting the earliest adjacent sequential data in the plurality of adjacent sequential data until it is confirmed that the real-time target parameter in the real-time target parameter data has reached a stable state; The real time corresponding to reaching the stable state is confirmed as the monitoring termination time.

5. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 4, characterized in that: The characteristic parameter set of the calculated fitting curve includes: Calculate the characteristic integral and characteristic derivative of the fitting curve, where the calculation formulas for the characteristic integral and characteristic derivative are as follows: ； ； in, represents the characteristic integral, Indicates the earliest parameter to be determined in the window to be determined. Indicates the latest parameter to be determined in the window to be determined, represents the fitting curve, represents the corresponding judgment time in the fitting curve, represents the characteristic derivative; The characteristic integrals and characteristic derivatives are summarized to obtain the characteristic parameter set of the fitting curve.

6. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 5, characterized in that: The step of constructing an amplification parameter sequence set based on the experimental parameter set includes: Extract all doping concentrations from the experimental parameter set to obtain a doping concentration set, perform a recursive operation on the doping concentration set to obtain multiple concentration recursive values, perform a sorting operation on the multiple concentration recursive values ​​in order from small to large, and obtain a concentration recursive sequence, and perform the following operations on the concentration recursive values ​​in the concentration recursive sequence: If the concentration recursive value is not the concentration recursive value with the largest value in the concentration recursive sequence, then extract the next concentration recursive value adjacent to the concentration recursive value from the concentration recursive sequence to obtain an adjacent concentration value, calculate the difference between the adjacent concentration value and the concentration recursive value to obtain a concentration interval, and if the concentration recursive value is the concentration recursive value with the largest value in the concentration recursive sequence, then skip the concentration recursive value; Summarize the concentration intervals to obtain a concentration interval set, calculate the greatest common divisor of the concentration interval set, and obtain the initial concentration amplification unit; Determining whether the initial concentration amplification unit is less than a preset concentration amplification threshold; If the initial concentration amplification unit is less than the concentration amplification threshold, performing a concentration value amplification operation on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence; Otherwise, the concentration value amplification operation is performed on the concentration recursive sequence using the initial concentration amplification unit to obtain a concentration amplification sequence; Constructing a size amplification sequence based on the size recursive sequence, extracting size amplification values ​​from the size recursive sequence in sequence, and associating the extracted size amplification values ​​with the concentration amplification sequence to obtain a size-concentration set, wherein the concentration amplification sequence includes a plurality of concentration amplification values, the size-concentration set includes a plurality of size-concentration parameters, and one size-concentration parameter includes: a size amplification value and a concentration amplification value; Summarizing the size-concentration sets to obtain the size-concentration amplification sets corresponding to the size amplification sequences; Extract all the fast recovery diode chip models from the experimental parameter set, identify the model code corresponding to each fast recovery diode chip model among all the fast recovery diode chip models, and perform a sorting operation on all the fast recovery diode chip models in ascending order according to the model code to obtain a chip model set, associate the chip model set with the size-concentration amplification set to obtain an amplification parameter sequence set.

7. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 6, characterized in that: The method performs a concentration value amplification operation on the concentration recursive sequence based on the concentration amplification threshold to obtain a concentration amplification sequence, including: Extract the concentration recursive values ​​from the concentration recursive sequence in sequence to obtain the target concentration value, and perform the following operations on the target concentration value: Extracting the next concentration recursive value adjacent to the target concentration value from the concentration recursive sequence to obtain a comparison concentration value, performing a cumulative operation on the target concentration value using a concentration amplification threshold to obtain a cumulative concentration value, and calculating the difference between the comparison concentration value and the cumulative concentration value to obtain a residual cumulative total value; If the remaining cumulative total value is less than or equal to the concentration amplification threshold, the target concentration value is confirmed as the cumulative concentration value; If the remaining accumulated total value is greater than the concentration amplification threshold, the target concentration value is confirmed as the accumulated concentration value, and the accumulated concentration value is used as the target concentration value, and the process returns to the step of performing an accumulation operation on the target concentration value using the concentration amplification threshold; The accumulated concentration values ​​and the concentration recursive sequence are summarized, and a deduplication operation is performed on the summarized accumulated concentration values ​​and the concentration recursive sequence to obtain a concentration amplification sequence.

8. The method for constructing a performance analysis model based on a fast recovery diode chip according to claim 7, characterized in that: The method constructs an amplification parameter model based on the amplification parameter sequence set and the initial model, extracts a blank model set from the amplification parameter model, and uses the initial model to perform a filling operation on the blank model set to obtain a prediction model set, including: Building an initial amplification parameter model based on the amplification parameter sequence set, extracting all unit models from the initial model to obtain a plurality of actual amplification parameter models, and mapping the plurality of actual amplification parameter models to the initial amplification parameter model to obtain an initial filling amplification model; Using preset training sample parameters to divide the multiple actual amplification parameter models, obtaining a training sample set and a verification sample set, using the training sample set to train a pre-constructed initial performance analysis model, and using the verification sample set to perform an iterative operation on the trained initial performance analysis model to obtain an optimized performance analysis model, wherein the initial performance analysis model is a pre-constructed neural network construction model; Extracting a blank model set from the initial fill-in augmentation model, and performing interpolation simulation operations on the blank models in the blank model set using a pre-built interpolation simulation method and a plurality of actual augmentation parameter models, to obtain a first prediction model set; The optimized performance analysis model is used to perform prediction operations on the blank models in the blank model set to obtain a second prediction model set, and a weighted integration operation is performed on the first prediction model set and the second prediction model set to obtain a prediction model set.

9. A system for constructing a performance analysis model based on a fast recovery diode chip, used to implement the method according to claim 1, characterized in that: The system comprises: A diode chip acquisition module is used to acquire a fast recovery diode chip set, perform classification and sorting operations on the fast recovery diode chip set to obtain a sequential chip set, and construct an experimental parameter set based on the sequential chip set, wherein the fast recovery diode chip set includes a plurality of fast recovery diode chips, and the experimental parameter set includes: a model of a fast recovery diode chip, a transition zone size, and a doping concentration; The performance test module is used to perform the following operations for each experimental parameter in the experimental parameter set: confirm the fast recovery diode chip corresponding to the experimental parameter, obtain the experimental chip, obtain the rated conditions corresponding to the experimental parameter, build a detection circuit based on the rated conditions, and after installing the experimental chip in the detection circuit, perform a preprocessing operation on the detection circuit with the experimental chip installed to obtain the experimental circuit, and obtain a target parameter curve set and a detection parameter set after performing a reverse recovery operation on the experimental circuit under the rated conditions; The initial model acquisition module is used to associate the target parameter curve set, the experimental parameters, the detection parameter set and the experimental chip to obtain a unit model, wherein the target parameter curve set includes: reverse current curve, reverse voltage curve and leakage current curve, and the detection parameter set includes: rated conditions, reverse recovery start time, reverse recovery time, reverse recovery end time and recovery softness, and summarize the unit models to obtain the initial model; The performance analysis model construction module is used to construct an amplification parameter sequence set based on the experimental parameter set, to construct an amplification parameter model based on the amplification parameter sequence set and the initial model, to extract a blank model set from the amplification parameter model, to perform a filling operation on the blank model set using the initial model to obtain a prediction model set, to summarize and integrate the prediction model set and the initial model to obtain a performance analysis model, and to complete the construction of a performance analysis model based on the fast recovery diode chip based on the performance analysis model.

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