CN114705965A - Third generation semiconductor reliability data analysis system based on big data - Google Patents
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Abstract
The invention belongs to the field of semiconductors, relates to a data analysis technology, and is used for solving the problem that the reliability detection result is not high in accuracy because the existing semiconductor reliability detection cannot simulate the real application environment of a semiconductor, in particular to a third generation semiconductor reliability data analysis system based on big data, which comprises a processor, wherein the processor is in communication connection with an environment test module, a service life analysis module and a storage module; the environment test module is used for detecting and analyzing the environment tolerance capability of the third generation semiconductor: the test space e comprises a simulated temperature and a simulated humidity, the simulated temperature and the simulated humidity of the test space are assigned, and the analysis objects distributed to the test space e are subjected to test analysis; according to the invention, the simulated temperature and the simulated humidity of the test space are assigned, so that an independent test environment can be established in the test space, and finally the actual real environment of the semiconductor is simulated through the test environment of each test space.
Description
Technical Field
The invention belongs to the field of semiconductors, relates to a data analysis technology, and particularly relates to a third generation semiconductor reliability data analysis system based on big data.
Background
The third generation semiconductor material can realize better electronic concentration and motion control, is more suitable for manufacturing high-temperature, high-frequency, anti-radiation and high-power electronic devices, has important application value in the fields of photoelectron and microelectronics, and is an important application field of the third generation semiconductor in 5G base stations, new energy automobiles, quick charging and the like of market heat.
The reliability detection of the third generation semiconductor mainly comprises two major items of an environment test and a service life test, in the prior art, the process of the environment test is to detect and analyze the working state of the semiconductor under the environment of high temperature, low temperature and temperature alternation respectively, however, the environment corresponding to the semiconductor in the practical application is more complicated and more changeable than the simulation environment in the environment test, so the environment test in the prior art can not simulate the real application environment of the semiconductor, and the reference of the reliability detection result is not high.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide a third generation semiconductor reliability data analysis system based on big data, which is used for solving the problem that the reliability detection result is not high in accuracy because the existing semiconductor reliability detection cannot simulate the real application environment of a semiconductor;
the technical problems to be solved by the invention are as follows: how to provide a third generation semiconductor reliability data analysis system which can simulate the real application environment of the semiconductor.
The purpose of the invention can be realized by the following technical scheme:
the third generation semiconductor reliability data analysis system based on big data comprises a processor, wherein the processor is in communication connection with an environment test module, a service life analysis module and a storage module;
the environment test module is used for detecting and analyzing the environment tolerance capability of the third generation semiconductor: marking a third-generation semiconductor to be detected as an analysis object i, wherein i is 1, 2, …, n is a positive integer, establishing a test space e, wherein e is 1, 2, …, m is a positive integer, m is a divisor of n, and distributing the analysis object i into m test spaces on average; the test space e comprises a simulated temperature and a simulated humidity, the simulated temperature and the simulated humidity of the test space are assigned, and after the assignment is completed, the analysis object distributed to the test space e is subjected to test analysis to obtain a test coefficient SYi of the analysis object i;
obtaining a test threshold value SYmax through a storage module, and comparing the test coefficient SYi of the analysis object i with the test threshold value SYmax:
if the test coefficient SYi is smaller than the test threshold SYmax, judging that the test of the analysis object i is qualified;
if the test coefficient SYi is larger than or equal to the test threshold SYmax, the analysis object i is judged to be unqualified in the test, and the environment test module sends a test unqualified signal to the processor.
As a preferred embodiment of the present invention, the specific process of assigning the simulated temperature and the simulated humidity of the test space includes: acquiring a proper temperature range and a proper humidity range of a third-generation semiconductor during working, selecting L1 temperature test values in the proper temperature range, dividing the proper temperature range into L1+1 temperature intervals by the L1 temperature test values, and enabling the temperature difference of the L1+1 temperature intervals to be the same; selecting L2 humidity test values in the suitable humidity range, dividing the suitable humidity range into L2+1 humidity intervals by the L2 humidity test values, wherein the humidity differences of the L2+1 humidity intervals are the same, and L1 and L2 are both quantity constants;
randomly extracting one temperature test value from the L1 temperature test values, and taking the value as a simulation temperature value;
randomly extracting one humidity test value from L2 humidity test values and taking the value as a simulated humidity assignment value.
As a preferred embodiment of the present invention, the specific process of performing the test analysis on the analysis object allocated to the test space e includes: firstly powering up to run a test program for initial test detection, gradually adjusting the environment temperature and the environment humidity to the simulation temperature and the simulation humidity respectively under the condition that the analysis object i does not work, powering up to run the test program for L3 hours after the environment temperature and the environment humidity are stable, taking out the analysis object i after the test is finished and the environment temperature and the environment humidity return to initial values, and recovering the analysis object i for L4 hours under normal atmospheric pressure to obtain parameters of the analysis object i so as to obtain color difference data SCi and quality difference data ZCi of the analysis object i.
As a preferred embodiment of the present invention, the acquisition process of the color difference data SCi of the analysis object i includes: carrying out image shooting on an analysis object i, amplifying the shot image into a pixel grid image, carrying out gray scale conversion on the pixel grid image to obtain an average gray value of the pixel grid image, and marking an absolute value of a difference value between the average gray value of the pixel grid image and a gray value of the analysis object before the beginning of an experiment as color difference data SCi; the acquisition process of the quality difference data ZCi of the analysis object i includes: the analysis object i is subjected to weight measurement, the absolute value of the difference between the obtained weight value and the weight value of the analysis object before the experiment is marked as quality difference data ZCi, and the experiment coefficient SYi of the analysis object i is obtained by numerically calculating the color difference data SCi and the quality difference data ZCi.
As a preferred embodiment of the present invention, the specific process of performing the test analysis on the analysis object allocated to the test space e further includes: the number of the analysis objects which are unqualified in the test space e is marked as BHe, the ratio of BHe to the total number of the analysis objects in the test space is marked as an environmental anomaly coefficient HYe of the test space e, an environmental anomaly threshold HYmax is obtained through the storage module, and the environmental anomaly coefficient HYe is compared with the environmental anomaly threshold HYmax:
if the environment abnormity coefficient HYe is smaller than an environment abnormity threshold value HYmax, judging that the environment of the test space e is normal;
if the environment abnormity coefficient HYe is greater than or equal to the environment abnormity threshold value HYmax, the environment abnormity of the test space e is judged, the simulated temperature and the simulated humidity corresponding to the test space e are respectively marked as abnormal temperature and abnormal humidity, and the environment test module sends the abnormal temperature and the abnormal humidity to the processor.
As a preferred embodiment of the present invention, the specific process of performing the test analysis on the analysis object allocated to the test space e further includes: marking the average value of the test coefficient SYi of the analysis object in the test space e as a test representation value SBe of the test space, establishing a test set { SB1, SB2, …, SBn } for the test representation value SBe of the test space e, carrying out variance calculation on the test set to obtain a tolerance coefficient NS of the third-generation semiconductor to be detected, obtaining tolerance thresholds NSmin and NSmax through a storage module, and comparing the tolerance coefficient NS with the tolerance thresholds NSmin and NSmax:
if the NS is less than or equal to NSmin, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is a grade;
if NSmin is less than NS and less than NSmax, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is two grades;
if the NS is larger than or equal to NSmax, judging that the environmental tolerance of the analysis object is unqualified, and judging that the tolerance grade of the analysis object is three grades;
the environmental test module sends the tolerance level of the analysis object to the processor.
In a preferred embodiment of the present invention, the lifetime analysis module is configured to perform detection and analysis on a lifetime of a third generation semiconductor: when the third-generation semiconductor is scrapped, acquiring the storage time and the working time of the third-generation semiconductor, obtaining the life coefficient SM of the third-generation semiconductor by carrying out numerical calculation on the storage time and the working time, acquiring the life coefficient SMmin through a storage module, and comparing the life coefficient SM with a life threshold value SMmin:
if the service life coefficient SM is less than or equal to the service life threshold SMmin, judging that the service life of the third-generation semiconductor does not meet the requirement, and sending a service life unqualified signal to the processor by the service life analysis module;
and if the service life coefficient SM is greater than the service life threshold SMmin, judging that the service life of the third-generation semiconductor meets the requirement, and sending a service life qualified signal to the processor by the service life analysis module.
The invention has the following beneficial effects:
1. the simulation temperature and the simulation humidity of the test space are assigned, so that an independent test environment can be established in the test space, and finally the actual real environment of the semiconductor is simulated through the test environment of each test space, so that the adaptability of the semiconductor in different test environments is judged, and the accuracy of the reliability detection result of the semiconductor is ensured;
2. the test qualification rate of an analysis object in a test space can be reflected through the environment abnormal coefficient, so that the degree that the simulated humidity and the simulated temperature corresponding to the test space are suitable for semiconductor work can be judged, the simulated humidity and the simulated temperature which are generally not beneficial to the semiconductor work are marked, the simulated humidity and the simulated temperature are prevented from occurring simultaneously in the subsequent semiconductor work monitoring, and the working environment of the semiconductor is improved;
3. the differences of the working states of the semiconductor under different environments can be analyzed through the established test set, and the larger the numerical value of the tolerance coefficient is, the larger the differences of the working states of the semiconductor under different environments are, so that the poorer the adaptability of the semiconductor to different environments is;
4. the service life analysis module can be used for analyzing the service life of the third-generation semiconductor in combination with the storage time and the working time of the third-generation semiconductor, the accuracy of a service life analysis result is guaranteed in combination with the storage time of the third-generation semiconductor when the service life of the semiconductor is analyzed, and the reliability of the semiconductor is accurately analyzed in cooperation with an experimental result of the environment test module.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The purpose of chip testing is to save cost as much as possible while finding out a chip without problems, the complexity of the chip is higher and higher, in order to ensure that the quality of the chip leaving factory does not have any problem, the chip needs to be tested before leaving factory to ensure the functional integrity, and the like, and the chip is used as a large-scale production object, the large-scale automatic testing is a unique solution, and the task cannot be completed by manual or benchmark testing;
the reliability detection of the third generation semiconductor mainly comprises two major items of an environment test and a service life test, in the prior art, the process of the environment test is to detect and analyze the working state of the semiconductor under the high-temperature, low-temperature and temperature alternating environments respectively, however, the environment corresponding to the semiconductor in the practical application is more complicated and more changeable than the simulation environment in the environment test, so that the simulation of the actual working environment of the semiconductor is difficult; therefore, the technical scheme is provided for solving the problem that the environmental test accuracy is not high due to the difficulty in simulating the working environment of the semiconductor.
As shown in fig. 1, the third generation semiconductor reliability data analysis system based on big data includes a processor, and an environment test module, a life analysis module and a storage module are communicatively connected to the processor.
The environment test module is used for detecting and analyzing the environment tolerance capability of the third generation semiconductor: the method comprises the steps that a third-generation semiconductor to be detected is marked as an analysis object i, i is 1, 2, …, n and n are positive integers, a test space e is set, e is 1, 2, …, m and m are positive integers, m is a divisor of n, the analysis object i is evenly distributed into m test spaces, a proper temperature range and a proper humidity range of the third-generation semiconductor during working are obtained, the proper temperature range and the proper humidity range of the third-generation semiconductor during working can be directly obtained through a storage module, L1 temperature test values are selected from the proper temperature range, the proper temperature range is divided into L1+1 temperature ranges by the L1 temperature test values, and the temperature difference of the L1+1 temperature ranges is the same; selecting L2 humidity test values in the suitable humidity range, dividing the suitable humidity range into L2+1 humidity intervals by the L2 humidity test values, wherein the humidity differences of the L2+1 humidity intervals are the same, and L1 and L2 are both quantity constants; the test space e comprises simulated temperature and simulated humidity, one temperature test value is randomly extracted from L1 temperature test values, and the value of the temperature test value is taken as a simulated temperature assignment value; randomly extracting a humidity test value from L2 humidity test values, taking a value as a simulated humidity value, and combining the temperature test value and the humidity test value in an assignment process, wherein an environment network is formed by a plurality of test environments, so that the comprehensive coverage degree of the simulated environment is improved, and the accuracy of the semiconductor environment test result is improved; the simulated temperature and the simulated humidity of the test space are assigned, so that an independent test environment can be established in the test space, and finally the actual real environment of the semiconductor is simulated through the test environment of each test space, so that the adaptability of the semiconductor in different test environments is judged, and the accuracy of the reliability detection result of the semiconductor is ensured.
After the assignment is completed, performing a test analysis on the analysis objects allocated to the test space e: firstly powering up to run a test program for initial test detection, gradually adjusting the environment temperature and the environment humidity to the simulation temperature and the simulation humidity respectively under the condition that an analysis object i does not work, powering up to run the test program for L3 hours after the environment temperature and the environment humidity are stable, taking out the analysis object i after the test is finished and the environment temperature and the environment humidity return to initial values, recovering the analysis object i for L4 hours under normal atmospheric pressure, and obtaining parameters of the analysis object i to obtain color difference data SCi and quality difference data ZCi of the analysis object i, wherein L3 and L4 are both constant in quantity; the acquisition process of the color difference data SCi of the analysis object i includes: the method comprises the steps of shooting an image of an analysis object i, amplifying the shot image into a pixel grid image, and carrying out gray level transformation on the pixel grid image to obtain an average gray level value of the pixel grid image, wherein the gray level transformation refers to a method for changing the gray level value of each pixel in a source image point by point according to a certain transformation relation according to a certain target condition, and aims to improve the image quality and enable the display effect of the image to be clearer. The white and black are divided into several levels according to the logarithmic relation, called as "gray scale", ranging from 0 to 255, white being 255 and black being 0, so that the black and white picture is also called as gray image, and has wide application in the fields of medicine and image recognition. Marking the absolute value of the difference value between the average gray value of the pixel grid image and the gray value of the analysis object before the beginning of the experiment as color difference data SCi; the acquisition process of the quality difference data ZCi of the analysis object i includes: measuring the weight of an analysis object i, marking the absolute value of the difference between the obtained weight value and the weight value of the analysis object before the experiment as quality difference data ZCi, obtaining a test coefficient SYi of the analysis object i through a formula SYi ═ alpha 1 × SCi + alpha 2 × ZCi, wherein the test coefficient is the numerical value of the change degree of a reaction analysis object before and after the experiment, the larger the numerical value of the test coefficient is, the larger the change degree of the analysis object before and after the experiment is, namely, the reliability of the analysis object is worse, wherein alpha 1 and alpha 2 are both proportional coefficients, and alpha 2 is more than alpha 1; obtaining a test threshold value SYmax through a storage module, and comparing a test coefficient SYi of an analysis object i with the test threshold value SYmax: if the test coefficient SYi is smaller than the test threshold SYmax, judging that the test of the analysis object i is qualified; if the test coefficient SYi is larger than or equal to the test threshold SYmax, the analysis object i is judged to be unqualified in the test, and the environment test module sends a test unqualified signal to the processor.
The number of analysis objects which are unqualified in the test space e is marked as BHe, an environmental anomaly coefficient HYe of the test space e is obtained through a formula HYe which is BHe × m/n, the environmental anomaly coefficient represents the test qualification rate of the analysis objects in the test space, the larger the value of the environmental anomaly coefficient is, the lower the test qualification rate of the analysis objects in the test space is, and the more unsuitable the simulation temperature and the simulation humidity corresponding to the test space are for semiconductor work; obtaining the environmental anomaly threshold value HYmax through the storage module, and comparing the environmental anomaly coefficient HYe with the environmental anomaly threshold value HYmax: if the environment abnormity coefficient HYe is smaller than an environment abnormity threshold value HYmax, judging that the environment of the test space e is normal; if the environment abnormal coefficient HYe is greater than or equal to the environment abnormal threshold value HYmax, the environment abnormality of the test space e is judged, the simulation temperature and the simulation humidity corresponding to the test space e are respectively marked as abnormal temperature and abnormal humidity, the environment test module sends the abnormal temperature and the abnormal humidity to the processor, the environment abnormal coefficient can reflect the test passing rate of an analysis object in the test space, the simulation humidity and the simulation temperature corresponding to the test space can be judged to be suitable for semiconductor work, the simulation humidity and the simulation temperature which are generally not beneficial to semiconductor work are marked, the simulation humidity and the simulation temperature are avoided to be generated simultaneously in subsequent semiconductor work monitoring, and the working environment of the semiconductor is improved.
Marking the average value of the test coefficient SYi of the analysis object in the test space e as a test representation value SBe of the test space, establishing a test set { SB1, SB2, …, SBn } for the test representation value SBe of the test space e, carrying out variance calculation on the test set to obtain a tolerance coefficient NS of the third-generation semiconductor to be detected, obtaining tolerance thresholds NSmin and NSmax through a storage module, wherein NSmin is a minimum tolerance threshold and NSmax is a maximum tolerance threshold, and comparing the tolerance coefficient NS with the tolerance thresholds NSmin and NSmax: if the NS is less than or equal to NSmin, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is a grade; if NSmin is less than NS and less than NSmax, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is two grades; if the NS is larger than or equal to NSmax, judging that the environmental tolerance of the analysis object is unqualified, and judging that the tolerance grade of the analysis object is three grades; the environment test module sends the tolerance level of the analysis object to the processor, the state difference of the semiconductor working under different environments can be analyzed through the established test set, and the larger the numerical value of the tolerance coefficient is, the larger the state difference of the semiconductor working under different environments is, so that the poorer the adaptability of the semiconductor to different environments is.
The service life analysis module is used for detecting and analyzing the service life of the third generation semiconductor: when a third-generation semiconductor is scrapped, acquiring storage time and working time of the third-generation semiconductor, respectively marking the storage time and the working time as CS and GS, acquiring a life coefficient SM of the third-generation semiconductor by a formula SM (beta 1 × CS + beta 2 × GS), acquiring a life coefficient SMmin by a storage module, and comparing the life coefficient SM with a life threshold SMmin: if the service life coefficient SM is less than or equal to the service life threshold SMmin, judging that the service life of the third-generation semiconductor does not meet the requirement, and sending a service life unqualified signal to the processor by the service life analysis module; if the service life coefficient SM is larger than the service life threshold SMmin, the service life of the third-generation semiconductor is judged to meet the requirement, the service life analysis module sends a service life qualified signal to the processor, the service life of the third-generation semiconductor can be analyzed by combining the storage time and the working time of the third-generation semiconductor through the service life analysis module, the accuracy of a service life analysis result is guaranteed by combining the storage time of the third-generation semiconductor when the service life of the semiconductor is analyzed, and the reliability of the semiconductor is accurately analyzed by matching with an experiment result of the environment test module.
When the third generation semiconductor reliability data analysis system based on big data works, an analysis object i is evenly distributed into m test spaces through an environment test module; the test space e comprises a simulated temperature and a simulated humidity, the simulated temperature and the simulated humidity of the test space are assigned, after the assignment is completed, the analysis objects distributed to the test space e are subjected to test analysis to obtain test coefficients SYi of the analysis objects i, the test results of the analysis objects are judged according to the numerical values of the test coefficients, the number of the analysis objects which are unqualified in the test space e is marked as BHe, the ratio of BHe to the total number of the analysis objects in the test space is marked as an environmental anomaly coefficient HYe of the test space e, and whether the simulated environment in the test space is suitable for semiconductor work or not is judged according to the numerical values of the environmental anomaly coefficient.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: the formula SYi ═ α 1 × SCi + α 2 × ZCi; collecting multiple groups of sample data by technicians in the field and setting corresponding test coefficients for each group of sample data; substituting the set test coefficient and the acquired sample data into formulas, forming a linear equation set by any two formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1 and alpha 2 which are respectively 2.87 and 3.54;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding test coefficient is preliminarily set for each group of sample data by a person skilled in the art; the proportional relation between the parameters and the quantized numerical values is not affected, for example, the trial coefficient is in direct proportion to the numerical value of the color difference data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. The third generation semiconductor reliability data analysis system based on big data comprises a processor and is characterized in that the processor is in communication connection with an environment test module, a service life analysis module and a storage module;
the environment test module is used for detecting and analyzing the environment tolerance capability of the third generation semiconductor: marking a third-generation semiconductor to be detected as an analysis object i, wherein i is 1, 2, …, n is a positive integer, establishing a test space e, wherein e is 1, 2, …, m is a positive integer, m is a divisor of n, and distributing the analysis object i into m test spaces on average; the test space e comprises a simulated temperature and a simulated humidity, the simulated temperature and the simulated humidity of the test space are assigned, and after the assignment is completed, the analysis object distributed to the test space e is subjected to test analysis to obtain a test coefficient SYi of the analysis object i;
obtaining a test threshold value SYmax through a storage module, and comparing a test coefficient SYi of an analysis object i with the test threshold value SYmax:
if the test coefficient SYi is smaller than the test threshold SYmax, judging that the test of the analysis object i is qualified;
if the test coefficient SYi is larger than or equal to the test threshold SYmax, the analysis object i is judged to be unqualified in the test, and the environment test module sends a test unqualified signal to the processor.
2. The big-data based third-generation semiconductor reliability data analysis system according to claim 1, wherein the specific process of assigning the simulated temperature and the simulated humidity of the test space comprises: acquiring a suitable temperature range and a suitable humidity range of a third-generation semiconductor during working, selecting L1 temperature test values in the suitable temperature range, dividing the suitable temperature range into L1+1 temperature intervals by the L1 temperature test values, and keeping the same temperature difference of the L1+1 temperature intervals; selecting L2 humidity test values in the suitable humidity range, dividing the suitable humidity range into L2+1 humidity intervals by the L2 humidity test values, wherein the humidity differences of the L2+1 humidity intervals are the same, and L1 and L2 are both quantity constants;
randomly extracting a temperature test value from L1 temperature test values and taking the value as a simulated temperature assignment value;
randomly extracting one humidity test value from L2 humidity test values and taking the value as a simulated humidity assignment value.
3. The big-data-based third-generation semiconductor reliability data analysis system according to claim 1, wherein the specific process of performing the test analysis on the analysis objects allocated to the test space e comprises: firstly powering up to run a test program for initial test detection, gradually adjusting the environment temperature and the environment humidity to the simulation temperature and the simulation humidity respectively under the condition that the analysis object i does not work, powering up to run the test program for L3 hours after the environment temperature and the environment humidity are stable, taking out the analysis object i after the test is finished and the environment temperature and the environment humidity return to initial values, and recovering the analysis object i for L4 hours under normal atmospheric pressure to obtain parameters of the analysis object i so as to obtain color difference data SCi and quality difference data ZCi of the analysis object i.
4. The big-data-based third-generation semiconductor reliability data analysis system according to claim 3, wherein the obtaining process of the color difference data SCi of the analysis object i comprises: carrying out image shooting on an analysis object i, amplifying the shot image into a pixel grid image, carrying out gray scale conversion on the pixel grid image to obtain an average gray value of the pixel grid image, and marking an absolute value of a difference value between the average gray value of the pixel grid image and a gray value of the analysis object before the beginning of an experiment as color difference data SCi;
the acquisition process of the quality difference data ZCi of the analysis object i includes: the analysis object i is subjected to weight measurement, the absolute value of the difference between the obtained weight value and the weight value of the analysis object before the experiment is marked as quality difference data ZCi, and the experiment coefficient SYi of the analysis object i is obtained by numerically calculating the color difference data SCi and the quality difference data ZCi.
5. The big-data-based third-generation semiconductor reliability data analysis system according to claim 1, wherein the specific process of performing the test analysis on the analysis objects allocated to the test space e further comprises: the number of the analysis objects which are unqualified in the test space e is marked as BHe, the ratio of BHe to the total number of the analysis objects in the test space is marked as an environmental anomaly coefficient HYe of the test space e, an environmental anomaly threshold HYmax is obtained through the storage module, and the environmental anomaly coefficient HYe is compared with the environmental anomaly threshold HYmax:
if the environment abnormity coefficient HYe is smaller than an environment abnormity threshold value HYmax, judging that the environment of the test space e is normal;
if the environment abnormity coefficient HYe is greater than or equal to the environment abnormity threshold value HYmax, the environment abnormity of the test space e is judged, the simulated temperature and the simulated humidity corresponding to the test space e are respectively marked as abnormal temperature and abnormal humidity, and the environment test module sends the abnormal temperature and the abnormal humidity to the processor.
6. The big-data-based third-generation semiconductor reliability data analysis system according to claim 1, wherein the specific process of performing the test analysis on the analysis objects allocated to the test space e further comprises: marking the average value of the test coefficient SYi of the analysis object in the test space e as a test representation value SBe of the test space, establishing a test set { SB1, SB2, …, SBn } for the test representation value SBe of the test space e, carrying out variance calculation on the test set to obtain a tolerance coefficient NS of the third-generation semiconductor to be detected, obtaining tolerance thresholds NSmin and NSmax through a storage module, and comparing the tolerance coefficient NS with the tolerance thresholds NSmin and NSmax:
if the NS is less than or equal to NSmin, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is a grade;
if NSmin is less than NS and less than NSmax, judging that the environmental tolerance capability of the analysis object is qualified, and judging that the tolerance grade of the analysis object is two grades;
if the NS is larger than or equal to NSmax, judging that the environmental tolerance of the analysis object is unqualified, and judging that the tolerance grade of the analysis object is three grades;
the environmental test module sends the tolerance level of the analysis object to the processor.
7. The big-data based third generation semiconductor reliability data analysis system of claim 1, wherein the lifetime analysis module is configured to perform a detection analysis on the lifetime of a third generation semiconductor: when the third-generation semiconductor is scrapped, acquiring the storage time and the working time of the third-generation semiconductor, obtaining a life coefficient SM of the third-generation semiconductor by carrying out numerical calculation on the storage time and the working time, acquiring the life coefficient SMmin through a storage module, and comparing the life coefficient SM with a life threshold value SMmin: if the service life coefficient SM is less than or equal to the service life threshold SMmin, judging that the service life of the third-generation semiconductor does not meet the requirement, and sending a service life unqualified signal to the processor by the service life analysis module; and if the service life coefficient SM is greater than the service life threshold SMmin, judging that the service life of the third-generation semiconductor meets the requirement, and sending a service life qualified signal to the processor by the service life analysis module.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116147833A (en) * | 2023-04-19 | 2023-05-23 | 苏州森斯缔夫传感科技有限公司 | Pressure sensor performance analysis method and system based on data mining |
| CN117269731A (en) * | 2023-11-07 | 2023-12-22 | 千思跃智能科技(苏州)股份有限公司 | PCBA performance automatic test system based on Internet of things |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116147833A (en) * | 2023-04-19 | 2023-05-23 | 苏州森斯缔夫传感科技有限公司 | Pressure sensor performance analysis method and system based on data mining |
| CN117269731A (en) * | 2023-11-07 | 2023-12-22 | 千思跃智能科技(苏州)股份有限公司 | PCBA performance automatic test system based on Internet of things |
| CN117269731B (en) * | 2023-11-07 | 2024-04-30 | 千思跃智能科技(苏州)股份有限公司 | PCBA performance automatic test system based on Internet of things |
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