CN115774185A - Vehicle gauge grade chip DPAT detection method and device - Google Patents
Vehicle gauge grade chip DPAT detection method and device Download PDFInfo
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- CN115774185A CN115774185A CN202310101574.7A CN202310101574A CN115774185A CN 115774185 A CN115774185 A CN 115774185A CN 202310101574 A CN202310101574 A CN 202310101574A CN 115774185 A CN115774185 A CN 115774185A
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Abstract
The invention discloses a method and a device for detecting a vehicle gauge grade chip DPAT. The method comprises the following steps: after a batch of tests is finished, obtaining test data of the batch, and ensuring that the test data meet set DPAT calculation conditions; determining production influence factors according to the test items, wherein each production influence factor X has a plurality of production objects in the test process, each production object corresponds to a group of test data, and the data distribution of the test data of each production object is determined; judging whether the data distribution accords with a drift removal adjustment condition, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X; and calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch, and comparing the test item values with the specification threshold to identify abnormal chips. The invention considers the influence of the drift of the process parameters on the test item value in the chip production process and improves the accuracy of DPAT detection through drift removal processing.
Description
Technical Field
The invention relates to the field of chip testing, in particular to a DPAT (differential pulse amplitude modulation) detection method and device for a vehicle-gauge chip.
Background
The vehicle gauge chip is a chip applied to an automobile, and compared with consumer-grade and industrial-grade chips, the vehicle gauge chip faces the challenges of large cold and hot temperature range, large humidity change, much dust, harmful gas erosion, bumping, impact and the like in the using process, and has higher requirement on reliability. Moreover, the automobile is closely related to personal safety, so that the automobile cannot be carelessly missed, and the requirement on safety is extremely high. Therefore, the automotive-scale chip has more strict requirements on quality, and generally requires that the number of defective products in each million Defect opportunities (DPPM) is controlled within 10, even 0, which requires that defects and abnormal products are found out as much as possible in the chip testing process to avoid flowing into downstream automobile applications.
For a test item of reliability of a vehicle-specification-level chip, the american automotive electronics committee AEC-Q001 specification recommends a Dynamic Part Average Testing (DPAT) method, which has the basic idea that: and performing sampling test and data statistics on the products according to batches, determining a specification threshold value suitable for the products of the current batch, and screening the abnormal products of the current batch by using the specification threshold value as a criterion. DPAT defines outliers as the values of test items within a batch that deviate significantly from the overall distribution. However, in an actual business scenario, the test result of the chip often generates a difference due to the drift of related process parameters, which causes a change in data distribution, and the simple and general use of the existing DPAT method inevitably generates a missing judgment or a misjudgment, which causes an abnormal product to flow out, thereby bringing an uncontrollable risk.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides the DPAT detection method for the vehicle gauge chip, which can improve the detection accuracy and further avoid the outflow of abnormal products caused by the missed judgment or the misjudgment.
The invention also provides a vehicle gauge grade chip DPAT detection device, computer equipment and a computer storage medium.
The technical scheme is as follows: according to a first aspect of the invention, a DPAT detection method for a vehicle-scale chip comprises the following steps:
after a batch of test is finished, obtaining test data of the batch, judging whether the test data accords with set DPAT calculation conditions or not, and ensuring that the test data accords with the set DPAT calculation conditions;
determining production influence factors according to the test items, wherein each production influence factor X has a plurality of production objects in the test process, each production object corresponds to a group of test data, and the data distribution condition of the test data of each production object under the production influence factors is determined;
judging whether the data distribution condition accords with a drift removal adjustment condition, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X;
calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch, comparing the test item values with the specification threshold, giving a new classification to the test item values exceeding the specification threshold, and identifying the corresponding chips as abnormal products.
As a preferred embodiment, the set DPAT calculation conditions include: the number of chips with good test results in the batch reaches a preset threshold value, and the data of all wafers are complete.
As a preferred embodiment, the determining the data distribution of the test data of each production object under the production impact factor includes: taking the 50% quantile value Q2 of the data set corresponding to each production object under the production influence factor X as a steady average value
The mean represents a median;
obtaining 25% quantile numerical value Q1 and 75% quantile numerical value Q3 according to the data set corresponding to each production object under the production influence factor X, and calculating the steady standard deviation value
；
Robust mean value based Robustmean and robust standard deviation valueRobustSigmaThe data distribution coefficient Cofficient is calculated according to the following formula:
，
the larger the Cofficient is, the larger the drift among the production objects under the production influence factor X is, and the more obvious the data presents a plurality of distributions; wherein the content of the first and second substances,AllRobustMeansthe robust average value set includes a robust average value calculated from a data set corresponding to each production object, and is represented as:
wherein X is 1 RM represents a robust average calculated from the data set corresponding to the first production object under the production impact factor X, X n RM represents a robust average value calculated by a data set corresponding to the nth production object;
AllRobustSigmasis a robust standard deviation set, which contains the robust standard deviation calculated from the data set corresponding to each production object, and is expressed as:
wherein X is 1 RS represents a robust standard deviation value calculated from a data set corresponding to a first production object under the production impact factor X, X n RS represents the robust standard deviation value calculated for the data set corresponding to the nth production object.
As a preferred embodiment, the determining whether the data distribution condition meets the drift removal adjustment condition includes: and judging whether the data distribution coefficient Cofficient is greater than a specified discrete threshold value M, and if so, conforming to the de-drift adjustment condition.
As a preferred embodiment, the performing the drift removal adjustment on the data set includes: obtaining the Mean value of each robust average value in the robust average value set (AllRobusMeans) and the robust average value X of the ith production object i The delta of the difference between the RMs is,
；
compensating the original test value OriginalValue by using the delta difference to obtain a value after the drift is removed:
；
and putting the test data of the batch of chips after the drift removal together for subsequent specification threshold calculation.
As a preferred embodiment, calculating the DPAT specification threshold based on the deshifted adjusted data set comprises: according to
Calculating an upper limit of the specification threshold based on
And calculating the lower limit of the specification threshold.
In a preferred embodiment, the production influencing factors include a test Site where the chip is located and a mask used in etching exposure.
According to a second aspect of the invention, a vehicle gauge level chip DPAT detection device comprises:
the data acquisition module is used for acquiring the test data of a batch after the test of the batch is finished, judging whether the test data meets the set DPAT calculation condition or not and ensuring that the test data meets the set DPAT calculation condition;
the data distribution determining module is used for determining production influence factors according to the test items, each production influence factor X is provided with a plurality of production objects in the test process, each production object corresponds to one group of test data, and the data distribution condition of the test data of each production object under the production influence factors is determined;
the drift removal processing module is used for judging whether the data distribution condition accords with a drift removal adjustment condition or not, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X;
and the detection and identification module is used for calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch and comparing the test item values with the specification threshold, endowing the test item values exceeding the specification threshold with new classification, and identifying the corresponding chips as abnormal products.
According to a third aspect of the invention, a computer device comprises:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors, implement the steps of the vehicle scale chip DPAT detection method as described above.
According to a fourth aspect of the present invention, a computer storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the vehicle gauge level chip DPAT detection method as described above.
Has the advantages that: the invention provides a DPAT detection method and a DPAT detection device for a vehicle gauge chip aiming at the requirements of high reliability and high safety of vehicle gauge chip testing, wherein the influence of data distribution change of a test result is caused by combing the difference generated by drift of related process parameters in a test flow, factors influencing the test result are identified as production influence factors, each production influence factor X is provided with a plurality of production objects in the test process, each production object corresponds to a group of test data, whether the test result drifts or not is identified by determining the data distribution condition of the test data of each production object under the production influence factors, drift removal processing is carried out, DPAT detection is carried out based on a data set after drift removal adjustment, the accuracy of chip testing is improved, abnormal product outflow caused by missing judgment or misjudgment is avoided, and the factory product quality of a chip manufacturer is improved.
Drawings
FIG. 1 is a flow chart of a DPAT detection method of a vehicle gauge chip according to the present invention;
FIG. 2 is a graph of the overall data distribution of test item data in an embodiment of the present invention;
FIG. 3 is a diagram illustrating a data distribution diagram corresponding to two sites in an embodiment of the present invention;
FIG. 4 is a graph of the overall data distribution after de-drift in the embodiment of the present invention;
fig. 5 is a Wafer graph of an abnormal product detected by the method in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
Referring to fig. 1, the DPAT detection method for the vehicle gauge grade chip provided by the invention comprises the following steps:
After a batch of tests is completed, all the Test Data files generated by the batch of tests are collected, typically STDF (Standard Test Data Format) files, and all the Test Data can be obtained after parsing and processing. The test data here are test items for chip functions, including current, voltage, and the like, such as a common IDDQ leakage current test.
And (4) judging whether the data meet the set DPAT calculation conditions, and if so, entering the step 2. According to the embodiment of the present invention, the DPAT calculation condition includes: the number of chips with good test results in the batch reaches a preset threshold value, and the data of all wafers are complete. For example, the Good product is identified as Good Die in the test result, and the DPAT calculation condition may be that the batch test at least contains 100/200/300 or other Good dies, and the data of all wafers are complete. All the Wafer data are complete, which is a link of data verification, for example, 10 wafers are tested in actual production, and the DPAT calculation can be performed only by waiting for the 10 wafers to be successfully analyzed. In order to ensure that DPAT calculations are performed on a reasonable and efficient data set, and to avoid false positives, both conditions need to be met simultaneously. If the calculation conditions are not met, the DPAT calculation is not carried out, and the batch is specially recorded for subsequent manual judgment.
And 2, determining production influence factors according to the test items, wherein each production influence factor has a plurality of production objects in the test process, each production object corresponds to one group of test data, and the data distribution condition of the batch of test data is determined.
Chip testing is mainly classified into three major categories: chip function test, performance test and reliability test. In order to realize these tests, various means are required to perform several items of tests and inspections on the chip, and the main means are CP test and FT test. Wafer CP (Chip combining) testing is often applied to functional testing and performance testing, to know whether the Chip functions are normal, and to screen out faulty chips in the wafer. The CP test is to contact the chip on the Wafer with a Probe (Probe), input various signals into the chip, grab the output response of the chip, compare and calculate. The devices to be used mainly include Automatic Test Equipment (ATE), probe stations (Prober), and some instruments, and a hardware Probe Card (Probe Card) is required to be manufactured. To save cost in a tester, probe cards are often tested in multiple sites (Site). The FT test of the packaged finished product is often applied to a functional test, a performance test and a reliability test, checks whether the chip functions normally or not, and whether defects are generated during the packaging process, and helps to detect whether the chip is still operable or not after passing through a harsh environment in the reliability test. The equipment that needs to use mainly has Automatic Test Equipment (ATE), arm (Handler) and corresponding instrument and meter, and the hardware that needs to make is test panel (Loadboard, also called support plate or load board), test Socket (Socket, also called base) etc.. The packaged chip is arranged on a Socket, a loader is placed on a tester table, the tester table adds required voltage and current to an interface on the loader, the voltage and current are loaded on the Socket through a circuit on the loader, and the Socket adds the voltage and current to the chip. In order to speed up the test efficiency, a plurality of sites (test bits) are often set on one Loadboard, so that the test of a plurality of chips can be performed simultaneously.
The chip test items are various, and in an actual service scene, the test item value distribution caused by the drift of some process parameters is deviated/drifted, namely, a certain deviation is generated compared with a normal value. For example, some probes corresponding to Site (test bit) are dirty, which causes contact instability, and may cause test result drift. For example, the test result is shifted due to exposure unevenness of a Reticle (also called mask) in the semiconductor front end process. In the present invention, the drift of these process parameters that affect the test results is referred to as a production impact factor, i.e., the production impact factor indicates what factor the test results are affected by. In actual business, the production influence factors can be determined according to the test items, and according to the current working practice of the applicant, the production influence factors with actual production significance mainly comprise Site (test Site) and Reticle (mask), are suitable for CP test and FT test, and are used as recommendation factors. Each production impact factor corresponds to a plurality of production objects in the test process, and each production object corresponds to a group of test data. Of course, the case of only one production object for one production influencing factor is not excluded. For example, when testing chips, each Tester corresponds to a Loadboard, a plurality of sites are arranged on one Loadboard, a plurality of jacks are arranged on a similar wiring board, and a chip is placed on each Site for testing. Therefore, when Site is selected as the influence factor, all sites (Site 1/Site 2.) are produced. If a particle is selected as the impact factor, the production objects are all particles (particle 1/particle 2.). Each Site or each particle corresponds to a set of test data.
The number of production objects under a production impact factor is expressed in the present invention by a factor level. For each set production impact factor X, a plurality of production objects are included
For example, if there are 8 sites on the tester during a certain test, site1, site2, and Site8 are production objects, the factor level is 8, and each production object corresponds to a set of test data. The invention determines the data distribution of the batch test based on the test data.
The measure of the data distribution is mainly to look at the data difference degree, that is, the data deviation exceeds a certain threshold value. According to the invention, the drift degree of each production object under the production influence factor can be identified by acquiring the data distribution condition. Eliminating these drifts will help to improve the accuracy of test detection. According to the embodiment of the invention, the measurement of the data distribution situation is represented by a data distribution coefficient coficient. The larger the Cofficient is, the larger the drift between the production objects under the production influence factor X is, and the more obvious the data presents a plurality of distributions. In one embodiment, cofficient is calculated as follows:
wherein, max () represents the maximum value, min () represents the minimum value, robustmean () represents the stability and health average value; the AllRobusMeans is a set of robust average values comprising the robust average (RobusMean) calculated from the data set corresponding to each production object, expressed as:
wherein X is 1 RM represents a robust average calculated from the data set corresponding to the first production object under a production impact factor X, RM is an abbreviation for Robustmean, X n RM represents a robust average value calculated by a data set corresponding to the nth production object;
AllRobustSigmas is a robust standard deviation set comprising robust standard deviations (RobustSigma) calculated from datasets corresponding to respective production objects, expressed as:
wherein X is 1 RS represents a robust standard deviation value calculated from a data set corresponding to a first production object under a production impact factor X, RS is an abbreviation of RobustSigma, X n RS represents the robust standard deviation value calculated for the data set corresponding to the nth production object.
According to an embodiment of the present invention, the robust mean RobustMean and robust standard deviation RobustSigma are calculated as follows:
wherein Q1, Q2 and Q3 respectively represent 25%, 50% and 75% quantile.
The expression takes 50% quantile value of corresponding test item data, the mean represents median, for example, for current and voltage test data, 50% quantile value of current and voltage is obtained as Q2, classSimilarly, Q1 and Q3 represent the 25% and 75% quantile values of the test data, respectively.
Taking the production impact factor Site as an example, a test machine for a certain batch of tests has 2 sites, and each Site corresponds to a set of test data, namely X 1 ，X 2 . RM and RS are calculated by taking the Q1, Q2, Q3 quantile values:
the data distribution coefficient is as follows:
and 3, judging whether the data distribution condition accords with a drift removal adjustment condition, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X.
And (3) judging whether the data distribution coefficient Cofficient obtained by calculation in the step (2) exceeds a set discrete threshold value M, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X. The discrete threshold value may be obtained by calculating a group of distribution coefficients Cofficient according to historical batch data within a period of time, and selecting the data according to the distribution of the group of data, for example, selecting 5000 batches of data in the past half year, calculating 5000 Cofficient, and taking a median as the threshold value. In batch test, chips corresponding to different production objects are often relatively consistent, and only because the center drift is caused by test production process parameters, the influence caused by the factors is eliminated by performing drift removal processing. After the drift is removed, the test data of the batch of chips are put together and considered integrally, so that the test data can be considered as an ideal data set.
In one embodiment, for each production object's dataset at the production impact factor X, the de-drifted data values are calculated according to the following method:
OriginalValue is the original test value, and delta is the Mean of all the robust averages in the set of robust averages (AllRobusMeans) and the robust average X of the ith production object i The difference between RMs.
Taking the production impact factor Site as an example, a test machine for a certain batch of tests has 2 sites, and each Site corresponds to a set of test data, namely X 1 、X 2 . The way to calculate the data value after de-centering is as follows:
and 4, calculating upper and lower limits by applying a traditional DPAT method to the data set in the step 3, loading corresponding test item values of all chips in the batch, judging whether the upper and lower limits are exceeded, assigning new BIN classification to the chips if the upper and lower limits are exceeded, modifying test result data, and treating the chips as abnormal products in subsequent processes.
The formula for calculating the upper and lower limits by the traditional DPAT method is as follows:
the calculation method of Robustmean and RobustSigma is described in step 2, and will not be described herein.
In step 2, if a plurality of production influence factors are selected, the steps 2, 3 and 4 are applied one by one, and if one is found to be abnormal, the product is determined to be an abnormal product.
In one embodiment, after a lot test of a certain vehicle-scale chip, 25 STDF files generated in the wafer test process are collected, and statistics are performed to obtain total data of 25 wafers of the selected lot, wherein 10350 Good Die is in total and meets a set condition (the lot at least comprises 100 Good Die), and data of a certain IDDQ leakage current test item is selected to be applied to DPAT detection. For comparison, the original DPAT method and the DPAT method of the present invention are used for detection.
According to the original DPAT method, for convenience of viewing, the data of the test item is plotted as a histogram shown in fig. 2, and the abscissa in fig. 2 represents the test value, which refers to the IDDQ leakage current test item in unit mv in the example, and the ordinate represents the frequency, which represents the number of times of occurrence of the corresponding test value. The data for this test item was calculated to obtain the relevant parameters shown in table 1.
TABLE 1 correlation parameters detected by the original DPAT method
| Data set | Q1 | Q2 | Q3 | Robust Mean | Robust Sigma |
| Integral body | 0.546 | 2.6203 | 4.1224 | 2.6203 | 2.6492 |
The upper and lower DPAT limits were obtained by calculation based on the relevant parameters in table 1:
and loading all chip data of the test item in the batch based on the upper and lower limits of the DPAT, and judging whether the upper and lower limits are exceeded or not, wherein the exceeded product is an abnormal product. Through analysis and calculation, the original DPAT method is adopted for detection, and no abnormal product is found.
According to the method, sites are selected as production influence factors, 2 Sites exist on a testing machine in the test process of the batch, test item data corresponding to the 2 Sites are drawn into a histogram shown in figure 3 for convenience of viewing, and the meanings of horizontal and vertical coordinates are the same as those of figure 2. And respectively calculating the test item data corresponding to the 2 sites to obtain the relevant parameters shown in the table 2.
Table 2 Site's relevant parameters
| Data set | Q1 | Q2 | Q3 | Robust Mean | Robust Sigma |
| Site1 | 3.3693 | 4.1015 | 4.8105 | 4.1015 | 1.0676 |
| Site2 | -0.4762 | 0.5461 | 1.5327 | 0.5461 | 1.4881 |
And calculating to obtain a data distribution coefficient 2.7825 of the production influence factor Site based on the relevant parameters of the Site.
And judging whether the data distribution coefficient exceeds a set threshold value or not. Here, the threshold value is set to 0.6, i.e., the center drift distance by the production influence factor Site should not exceed 0.6 average dispersion. Because the data distribution coefficient 2.7825 exceeds the threshold of 0.6, it is necessary to perform drift removal adjustment on the central drift caused by the production impact factor Site, and the test data of the batch of chips are taken into consideration integrally after the drift removal. For the data sets of Site1 and Site2, the adjustment is performed in the following manner, respectively.
For ease of viewing, the de-drifted data was plotted as a histogram shown in fig. 4. The horizontal and vertical axes in FIG. 4 are as defined in FIG. 2. The data for this test item was calculated to obtain the relevant parameters shown in table 3.
TABLE 3 related parameters detected by the DPAT method of the present invention
| Data set | Q1 | Q2 | Q3 | Robust Mean | Robust Sigma |
| Integral body | 1.4563 | 2.3238 | 3.1472 | 2.3238 | 1.2525 |
Based on the above, new upper and lower limits of DPAT are obtained through calculation:
and loading all chip data of the test item in the batch, and judging whether the upper limit and the lower limit are exceeded or not, wherein the exceeded limit is an abnormal product. After analysis and calculation, three abnormal products are found by adopting the DPAT method for detection, and are respectively shown in the following table 4.
TABLE 4 abnormal products detected by the DPAT method of the present invention
| Serial number | Test value after DeDrift | Raw data value | Wafer Num | Coordinate position |
| 1 | -6.9401 | -8.7176 | 7 | （32,17） |
| 2 | -7.0243 | -8.8020 | 12 | （28,14） |
| 3 | -7.4105 | -9.1882 | 18 | （16,15） |
Fig. 5 is a Wafer map showing one of the abnormal products, and the position indicated by an arrow is the abnormal product detected by the DPAT method after the improvement of the invention, and the abnormal product is assigned to BIN97 type in the test.
By comparing the detection results of the two methods, the improved DPAT detection method can effectively discover abnormal products which cannot be discovered by the original DPAT method. In an actual production scene, the abnormal products found in the same way are tested and verified for many times, and the fact that the abnormal products have problems is determined. If the abnormal chip flows into the downstream automobile market, the automobile is broken down if the abnormal chip is light, and accidents are caused if the abnormal chip is heavy, so that immeasurable consequences are brought. The invention considers the influence of the drift of the process parameters on the test item value in the chip production process, improves the accuracy of DPAT detection by drift removal processing, avoids abnormal product outflow caused by missing judgment or misjudgment and enhances the practical production application significance of DPAT.
Another embodiment of the present invention further provides a DPAT detection device for a vehicle gauge chip, including:
the data acquisition module is used for acquiring the test data of a batch after the test of the batch is finished, judging whether the test data meets the set DPAT calculation condition or not and ensuring that the test data meets the set DPAT calculation condition;
the data distribution determining module is used for determining production influence factors according to the test items, each production influence factor X is provided with a plurality of production objects in the test process, each production object corresponds to one group of test data, and the data distribution condition of the test data of each production object under the production influence factors is determined;
the drift removal processing module is used for judging whether the data distribution condition accords with a drift removal adjustment condition or not, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X;
and the detection and identification module is used for calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch and comparing the test item values with the specification threshold, endowing the test item values exceeding the specification threshold with new classification, and identifying the corresponding chips as abnormal products.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Embodiments of the present invention also provide a computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the vehicle-scale chip DPAT detection method.
It should be noted that the computing device is a computing device corresponding to the method, and all implementation manners in the embodiment of the method are applicable to the embodiment of the computing device, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the vehicle gauge-level chip DPAT detection method as described above.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step may be decomposed and/or recombined. These decompositions and/or recombinations are to be regarded as equivalents of the present invention. Also, the steps of performing the series of processes described above may naturally be performed chronologically in the order described, but need not necessarily be performed chronologically, and some steps may be performed in parallel or independently of each other. It will be understood by those skilled in the art that all or any of the steps or elements of the method and apparatus of the present invention may be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or any combination thereof, which can be implemented by those skilled in the art using their basic programming skills after reading the description of the present invention.
Thus, the objects of the invention may also be achieved by running a program or a set of programs on any computing device. The computing device may be a general purpose device as is well known. The object of the invention is thus also achieved solely by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is to be understood that the storage medium may be any known storage medium or any storage medium developed in the future.
Claims (10)
1. A DPAT detection method for a vehicle-scale chip is characterized by comprising the following steps:
after a batch of test is finished, obtaining test data of the batch, judging whether the test data accords with set DPAT calculation conditions or not, and ensuring that the test data accords with the set DPAT calculation conditions;
determining production influence factors according to the test items, wherein each production influence factor X has a plurality of production objects in the test process, each production object corresponds to a group of test data, and the data distribution condition of the test data of each production object under the production influence factors is determined;
judging whether the data distribution condition accords with a drift removal adjustment condition, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X;
calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch, comparing the test item values with the specification threshold, giving a new classification to the test item values exceeding the specification threshold, and identifying the corresponding chips as abnormal products.
2. The DPAT detection method of the vehicle-scale chip of claim 1, wherein the set DPAT calculation conditions comprise: the number of chips with good test results in the batch reaches a preset threshold value, and the data of all wafers are complete.
3. The DPAT detection method of claim 1, wherein the determining the data distribution of the test data of each production object under the production impact factor comprises: taking the 50% quantile value Q2 of the data set corresponding to each production object under the production influence factor X as a steady average value
The mean represents a median;
obtaining 25% quantile numerical value Q1 and 75% quantile numerical value Q3 according to the data set corresponding to each production object under the production influence factor X, and calculating the steady standard deviation value
；
Based on robust averageRobustMeanAnd robust standard deviation valueRobustSigmaThe data distribution coefficient Cofficient is calculated according to the following formula:
the larger the Cofficient is, the larger the drift among the production objects under the production influence factor X is, and the more obvious the data presents a plurality of distributions; wherein the content of the first and second substances,AllRobustMeansthe robust average value set includes a robust average value calculated from a data set corresponding to each production object, and is represented as:
wherein X is 1 RM stands for productionRobust average value calculated from data set corresponding to first production object under influence factor X, X n RM represents a robust average value calculated by a data set corresponding to the nth production object;
AllRobustSigmasis a robust standard deviation set, which contains the robust standard deviation calculated from the data set corresponding to each production object, and is expressed as:
wherein X is 1 RS represents a robust standard deviation value calculated from a data set corresponding to a first production object under the production impact factor X, X n RS represents the robust standard deviation value calculated for the data set corresponding to the nth production object.
4. The DPAT detection method of the vehicle-scale chip of claim 3, wherein the determining whether the data distribution meets the de-drift adjustment condition comprises: and judging whether the data distribution coefficient Cofficient is greater than a specified discrete threshold value M, and if so, conforming to the de-drift adjustment condition.
5. The vehicle gauge-grade chip DPAT detection method of claim 3, wherein the de-drift adjusting of the data set comprises: obtaining the Mean value of each robust average value in the robust average value set (AllRobusMeans) and the robust average value X of the ith production object i The delta of the difference between the RMs is,
；
compensating the original test value OriginalValue by using the delta difference to obtain a value after the drift removal:
；
and putting the test data of the batch of chips after the drift removal together for subsequent specification threshold calculation.
6. The vehicle gauge grade chip DPAT detection method of claim 3, wherein calculating the DPAT gauge threshold based on the de-drift adjusted data set comprises: according to
Calculating an upper limit of the specification threshold based on
And calculating the lower limit of the specification threshold.
7. The DPAT detection method of the vehicle-scale chip as claimed in claim 1, wherein the production impact factors include a test Site where the chip is located and a mask used in etching exposure.
8. The utility model provides a car rule level chip DPAT detection device which characterized in that includes:
the data acquisition module is used for acquiring the test data of a batch after the test of the batch is finished, judging whether the test data meets the set DPAT calculation condition or not and ensuring that the test data meets the set DPAT calculation condition;
the data distribution determining module is used for determining production influence factors according to the test items, each production influence factor X is provided with a plurality of production objects in the test process, each production object corresponds to one group of test data, and the data distribution condition of the test data of each production object under the production influence factors is determined;
the drift removal processing module is used for judging whether the data distribution condition accords with a drift removal adjustment condition or not, and if so, performing drift removal adjustment on the data set of each production object under the production influence factor X;
and the detection and identification module is used for calculating the specification threshold of the DPAT based on the data set after the drift removal adjustment, loading the test item values of all the chips in the batch and comparing the test item values with the specification threshold, endowing the test item values exceeding the specification threshold with new classification, and identifying the corresponding chips as abnormal products.
9. A computer device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of the vehicle scale chip DPAT detection method of any of claims 1-7.
10. A computer storage medium having a computer program stored thereon, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the vehicle gauge-level chip DPAT detection method according to any one of claims 1 to 7.
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