MEMS device test data processing method and device, equipment and medium

Abstract

The invention discloses an MEMS device test data processing method and device, equipment and a medium. The method comprises the steps that a test data file is acquired; analyzing the test data file according to at least one set analysis dimension to obtain a structured test data sequence corresponding to each dimension; extracting valid data from the test data sequence by adopting an event isolation window to form a valid data sequence; determining a test group according to the dimension corresponding to each valid data sequence; according to the effective data sequence of each test group, the reliability index of the test equipment is calculated, the performance of the test system can be comprehensively evaluated from multiple dimensions such as the data dispersion degree, the process specification meeting capability and the measurement reliability of the test system, the efficiency and accuracy of MEMS device test data processing are improved through the automatic processing flow, and the reliability of the test system is improved. Problems in test links can be found in time, and the test quality of batch MEMS devices is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, apparatus, device and medium for processing MEMS device test data. Background Technology

[0002] In semiconductor manufacturing, particularly in automotive (IATF 16949) and industrial applications, various testing equipment is used to test MEMS (Micro-Electro-Mechanical Systems) devices. MEMS devices include, for example, accelerometers, gyroscopes, barometers, and hygrometers. One example of testing equipment is the Strength Pareto Evolutionary Algorithm Device-Oriented Tester (SPEA DOT).

[0003] MEMS device test equipment typically includes multiple test points and can perform parallel testing on batches of MEMS devices. This test equipment must undergo MSA (Measurement Systems Analysis) and GR&R (Gauge Repeatability and Reproducibility) analysis to verify its reliability.

[0004] In existing technologies, determining the reliability of testing equipment requires manual analysis of test data, which is often very time-consuming. This is because it involves extracting valid data from massive amounts of unstructured test data and then calculating indicators such as the Process Capability Index (CpK) and GR&R, resulting in high time costs. Therefore, it is necessary to solve the data management or statistical calculation problems existing in current technologies. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for processing MEMS device test data to solve the problems of low efficiency and low accuracy in MEMS device test data processing.

[0006] According to one aspect of the present invention, a method for processing MEMS device test data is provided, comprising:

[0007] The test data file is obtained; the test data included in the test data file consists of test values ​​generated by parallel testing of the set attributes of a batch of MEMS devices through multiple test stations of multiple test devices; from the test data file, structured test data sequences corresponding to each set parsing dimension are obtained respectively; wherein the set parsing dimension includes at least one of the following: test device, test station, MEMS device, set attribute, test time, and test configuration; valid data is extracted from the test data sequence using an event isolation window to form a valid data sequence; test groups are determined according to the dimension corresponding to each valid data sequence; and the reliability index of the test device is calculated according to the valid data sequence of each test group; wherein the reliability index includes: process capability index, standard deviation, repeatability, and reproducibility.

[0008] According to another aspect of the present invention, a processing apparatus for MEMS device test data is provided, comprising:

[0009] The file acquisition module is used to acquire test data files. The test data files contain test data generated by parallel testing of set attributes of batch MEMS devices through multiple test stations of multiple test devices. The test parsing module is used to parse and acquire structured test data sequences corresponding to each dimension from the test data files, according to at least one set parsing dimension. The set parsing dimension includes at least one of the following: test device, test station, MEMS device, set attribute, test time, and test configuration. The window extraction module is used to extract valid data from the test data sequences using an event isolation window to form valid data sequences. The test grouping module is used to determine test groups based on the dimensions corresponding to each valid data sequence. The index calculation module is used to calculate the reliability index of the test device based on the valid data sequences of each test group. The reliability index includes CpK, standard deviation, repeatability, and reproducibility.

[0010] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0011] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the MEMS device test data processing method according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the MEMS device test data processing method according to any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the method as described in any embodiment of the present invention.

[0014] The technical solution of this invention, by acquiring test data files generated by multiple test devices and their multiple test stations for parallel testing of batch MEMS devices, can cover the data requirements of high-capacity test scenarios at once, avoiding data fragmentation. By parsing the file according to at least one set parsing dimension, structured test data sequences corresponding to each dimension can be obtained, thus determining the data correlation between different dimensions. Using an event isolation window to extract valid data from the test data sequences to form valid data sequences can eliminate redundant and interfering data, ensuring that subsequent analysis is based on valid data from real responses, improving the accuracy of MEMS device test data processing. Determining test groups based on the dimensions corresponding to the valid data sequences matches the calculation logic of reliability indicators. Calculating reliability indicators based on the valid data sequences of each test group allows for a comprehensive evaluation of the test system performance from multiple dimensions, including data dispersion, process compliance with specifications, and test system measurement reliability. The automated processing flow improves the efficiency of MEMS device test data processing, ensuring timely detection of problems in the testing process and guaranteeing the test quality of batch MEMS devices.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for processing MEMS device test data according to Embodiment 1 of the present invention;

[0018] Figure 2 This is a flowchart of another method for processing MEMS device test data according to Embodiment 2 of the present invention;

[0019] Figure 3 This is a flowchart of another method for processing MEMS device test data according to Embodiment 3 of the present invention;

[0020] Figure 4 This is a time-domain diagram of MEMS detection data applicable to embodiments of the present invention;

[0021] Figure 5 This is a heat map applicable to embodiments of the present invention, overlaid on a load board or socket board layout diagram that associates test results with the location of physical test sites;

[0022] Figure 6 This is a schematic diagram of a test grouping based on repeatability and reproducibility indicators applicable to an embodiment of the present invention;

[0023] Figure 7 This is a schematic diagram of a MEMS device test data processing device according to Embodiment 4 of the present invention;

[0024] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the MEMS device test data processing method of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1This is a flowchart of a method for processing MEMS device test data according to Embodiment 1 of the present invention. This embodiment is applicable to processing test data of MEMS devices. The method can be executed by a MEMS device test data processing device, which can be implemented in hardware and / or software and is generally configured in an electronic device. Correspondingly, as... Figure 1 As shown, the method includes:

[0029] S110. Obtain the test data file. The test data file contains test values ​​generated by parallel testing of the set attributes of a batch of MEMS devices through multiple test stations of multiple test devices.

[0030] In this embodiment of the invention, the test data file can be specifically understood as a file storing all data generated during the MEMS device testing process. Multiple test devices can be specifically understood as multiple hardware devices used to conduct MEMS device testing, capable of executing test tasks simultaneously and in parallel, improving batch testing efficiency. Multiple test devices, and multiple test stations on the same device, simultaneously perform parallel testing on the set attributes of different MEMS devices. A test station can be specifically understood as a specific location on each test device used to place and test one or more MEMS devices (such as different test stations on the device), serving as a direct data acquisition node. Batch MEMS devices can be specifically understood as multiple MEMS devices that need to be tested simultaneously, such as accelerometers and gyroscopes. Set attributes can be specifically understood as specific performance parameters of the MEMS device that need to be tested, such as the X, Y, and Z-axis responses of the accelerometer and the angular rate of the gyroscope.

[0031] Specifically, each testing device utilizes its own multiple testing stations, placing batches of MEMS devices on their respective stations. Through the collaborative work of multiple testing devices and stations, the set attributes of each MEMS device are tested in parallel. During the testing process, the test values ​​of the set attributes of each device are collected in real time, and all test values ​​are aggregated and stored as a test data file. The test data file is then acquired from the connected testing devices using the test system's data acquisition function.

[0032] S120. From the test data file, according to at least one set parsing dimension, obtain the structured test data sequence corresponding to each dimension. The set parsing dimension includes at least one of the following: test equipment, test site, MEMS device, set attributes, test time, and test configuration.

[0033] In this embodiment of the invention, setting the parsing dimension can be specifically understood as: a classification basis used to split and categorize the original data to achieve data structuring. The structured test data sequence can be specifically understood as: an ordered set with a unified format and clear correlation after splitting according to the set parsing dimension, specifically in the form of "dimension identifier - corresponding test data", such as "test device A - test data [1.2g, 1.3g, ...]", where g represents acceleration. The test device dimension can be specifically understood as: using the hardware device performing the test as the classification basis, splitting the original data according to different test devices (such as tester 1 and tester 2) to distinguish the test data generated by different devices. The test site dimension can be specifically understood as: using the specific test station on the test device as the classification basis, further splitting the data by site (such as site 1 and site 2 of device A) under the same device to locate the specific data acquisition node. The MEMS device dimension can be specifically understood as: using the MEMS device under test as the classification basis, splitting the data by device number (such as DUT001 and DUT002) to associate each group of test data with the specific device. Here, DUT (Device Under Test) represents the test equipment. The attribute dimension can be understood as: classifying data based on the test performance parameters of MEMS devices, splitting data by attribute type (such as X-axis acceleration, Y-axis acceleration, and angular rate), and distinguishing test values ​​for different performance indicators. The test time dimension can be understood as: classifying data based on the test execution time node or time period, splitting data by time sequence (such as 2025-11-20 10:00 and 10:05), reflecting the temporal correlation of the data. The test configuration dimension can be understood as: classifying data based on parameter settings and environmental information during the test process, splitting data by configuration type (such as test program version, sampling rate, excitation type, and load board version), and associating the test environment parameters at the time the data was generated.

[0034] Specifically, based on subsequent analysis needs (such as distinguishing between test equipment and sites when calculating GR&R indicators, and associating test time when calculating performance trends), at least one analytical dimension can be selected as the classification standard for data splitting from test equipment, test sites, MEMS devices, set attributes, test time, and test configuration. The test data file is read, and the format and associated identifiers of the original data in the file are identified through preset identification rules, such as device number, device number, attribute type, and test value in the original data, establishing a mapping relationship between the original data and the analytical dimension. The original data is matched and split one by one according to the selected analytical dimension. For example, all data is classified into the corresponding device subset according to the test equipment dimension, and then split into specific site subsets according to the test site dimension within each device subset. Finally, all test values ​​of the same attribute are extracted from each site subset according to the set attribute dimension, resulting in a structured test data sequence in the form of "Test Equipment 1 - Site 1 - X-axis Acceleration - Test Data [1.2g, 1.3g, 1.25g, ...]", providing a data foundation for subsequent extraction of effective data and determination of test groups.

[0035] Optionally, based on the above embodiments, parsing the test data file according to at least one set parsing dimension to obtain the structured test data sequence corresponding to each dimension may include: identifying the test configuration of the test device based on the header data and / or file description data of the test data file; determining the corresponding target parsing rule from the parsing rule set according to the test configuration; wherein the target parsing rule includes the parsing configuration of the test device, test site, MEMS device, set attributes, test time and test configuration; and using the target parsing rule to parse the test data file to obtain the structured test data sequence corresponding to each dimension.

[0036] In this embodiment of the invention, the header data can be specifically understood as: information lines located before the data body in the test data file to explain the meaning of columns, used to identify the correspondence between data fields and dimensions. The file description data can be specifically understood as: embedded metadata contained within the test data file content, used to describe the test background, including general attributes such as filename and creation time (not at the operating system level), specifically including test program name, sensor type, firmware version, load board or socket board version number, test timestamp, sampling rate, excitation type, and environmental temperature and humidity, etc. The embedded metadata can be specifically understood as: structured information embedded within the test data file content as data lines or comment lines, not external file attributes and not part of the filename, in the form of key-value pairs at the beginning of the file (e.g., "TestProgram:Accel_RevB", indicating test program: accelerometer version B) or lines with the comment identifier "#" (e.g., "#TestProgram:XXX", indicating test program: XXX). By reading this metadata embedded in the test data file content, the system automatically determines which test configuration the current file belongs to, thereby calling the corresponding parsing rules (e.g., identifying data from DUT3 and site 3). The parsing rule set can be understood as a collection that stores the parsing logic corresponding to various test configurations. Each configuration corresponds to a set of dimension and field mapping rules (e.g., the Site column corresponds to the test site dimension, and the CurrentTIME column corresponds to the test time dimension under a certain configuration). The target parsing rule can be understood as matching the specific parsing logic of the current test configuration from the parsing rule set, clarifying the field positions, data formats (such as numeric types and units), and extraction methods of each set parsing dimension in the data file.

[0037] Specifically, the test data file is read, and the header data (identifying the meaning of each column's corresponding field) and file description data (obtaining test-related background information) are extracted. These two types of information are used to jointly determine the test configuration for this test (e.g., test configuration based on tester 1, for device 1, and sampling rate 10). The parsing rule set is invoked, and the corresponding target parsing rules are matched according to the identified test configuration to determine the mapping relationship between each parsing dimension and file fields. This may also include parsing configurations such as data format (e.g., numerical units as % or frames / second) and invalid data filtering thresholds. The data in the test data file is extracted and categorized line by line and field by field according to the target parsing rules. For example, test device dimension identifiers, test site dimension identifiers, and set attribute dimension data are extracted from embedded metadata, and integrated according to the relationship between test device, test site, and set attribute to generate a structured test data sequence corresponding to each dimension.

[0038] By automatically reading header and file description data to identify test configurations, match target parsing rules, and execute parsing, this method significantly improves test data processing efficiency and reduces labor costs compared to traditional manual parsing, which requires manually viewing files, matching configurations and templates, and handling massive amounts of test files. Dual verification enhances recognition accuracy, and pre-mapped field mappings in target parsing rules prevent configuration misjudgments and column mismatches that may occur during manual operations, thus improving field matching accuracy. The parsing rule set can be pre-stored; adding new configurations only requires supplementing the rules, eliminating the need to refactor the process. It can flexibly identify configuration information for different test equipment, components, and program versions, improving maintainability and compatibility.

[0039] Optionally, based on the above embodiments, the parsing rule set may include at least one of the following: a header mapping template, column sorting rules, and unit conversion logic of at least two test programs executed by the test device.

[0040] In this embodiment of the invention, the header mapping template can be specifically understood as: a template that establishes a correspondence between the header of the test data file and the set parsing dimensions, used to determine the mapping rules between header fields and dimensions or data types. The column sorting rules can be specifically understood as: the correspondence rules between the arrangement order of each data column in the test data file and the parsing logic, used to handle the problem of different positions of data columns of the same dimension due to differences in test program versions or device models (e.g., test time is in column 6 in version A program of test instrument 1, and in column 8 in version B program), ensuring accurate location of target dimension data when extracting data by column. During parsing, the test configuration is first identified, and then the corresponding column sorting rules are called to locate the data columns. The unit conversion logic can be specifically understood as: a unified conversion rule formulated for different data formats output by different test programs of the test equipment, used to convert the original data format into a standardized format, for example, program A outputs acceleration values ​​in grav (gravitational acceleration), program B outputs acceleration values ​​in units of... (meters per second), the unit conversion logic presets 1 grav=9.81 The conversion formula reads the original data unit during parsing and calls the corresponding formula to convert the data uniformly. This eliminates unit inconsistencies caused by differences in program settings and ensures data comparability. A test program can be specifically understood as a software program running on the test equipment, including test steps and parameter configurations. Different programs may output test data in different formats or units, requiring unit conversion logic to unify the data format and units.

[0041] The parsing rule set includes a header mapping template, column sorting rules, and unit conversion logic for at least two test programs on the test device. The header mapping template establishes a correspondence between header fields and parsing dimensions, eliminating ambiguity or errors caused by manual header identification and ensuring that data columns are correctly categorized into the target dimensions. The column sorting rules, addressing differences in data column positions across different test configurations, pre-define the mapping relationship between device, program version, and column position, automatically locating the target column without requiring manual index adjustments. This significantly improves parsing efficiency when batch processing multiple versions of files. By covering the unit conversion logic of at least two test programs, the data format and units are standardized, eliminating data incomparability issues and providing a consistent data foundation for subsequent reliability indicator calculations. This avoids indicator deviations caused by unit confusion. These three elements work together to adapt to complex test scenarios involving multiple devices and multiple programs, replacing manual judgment with automated rules, thus improving the efficiency and accuracy of batch file parsing.

[0042] Optionally, based on the above embodiments, the MEMS device is a multi-axis sensor, the test data is an acceleration value, and the set resolution dimension may include: device serial number, test site identifier, load board or socket board position, and sampling rate.

[0043] Specifically, MEMS devices are multi-axis sensors, that is, microelectromechanical system sensors with multi-dimensional (such as X, Y, and Z axes) physical quantity detection capabilities. For example, a multi-axis accelerometer can simultaneously acquire acceleration data in three orthogonal directions: X, Y, and Z. Correspondingly, the test data is the acceleration value, and the unit can be grav or... , it needs to be associated with the set analysis dimensions (such as test equipment and test sites). The set analysis dimensions are classification criteria preset for the multi-axis acceleration sensor test scenario and used to split the original acceleration data, which can include: equipment serial number, test site identifier, load board or socket board position, and sampling rate, all of which are key test context dimensions affecting the accuracy of acceleration values. Among them, the equipment serial number can be specifically understood as: the unique identifier of the test equipment, used to distinguish different physical test equipment and avoid the inability to trace the deviation of acceleration data caused by hardware differences of the equipment (such as circuit accuracy or calibration status). The test site identifier can be specifically understood as: the independent test position identifier on the test equipment for placing MEMS devices. The multi-axis sensor test is executed in parallel at multiple sites, and the acceleration data of different sites needs to be distinguished through this identifier to detect abnormal data caused by local problems such as poor contact at the site. The load board or socket board position can be specifically understood as: the position identifier of the hardware component supporting the test site. The load board is used to connect the test equipment and the sensor, and the socket board is used to fix the sensor. Their positions directly affect the connection stability between the sensor and the test circuit, and the position information can assist in positioning the drift of acceleration values caused by hardware contact deviation. The sampling rate can be specifically understood as: the frequency at which the test equipment collects acceleration data, which determines the time density of the acceleration data. A high sampling rate is suitable for capturing dynamic acceleration changes, and a low sampling rate is suitable for static tests. It is a dimensional index for judging whether the acceleration data is suitable for test requirements (such as dynamic or static acceleration calibration).

[0044] For the MEMS device of the multi-axis sensor, the equipment serial number, test site identifier, load board or socket board position, and sampling rate are used as the set analysis dimensions of the acceleration value test data. The equipment serial number can uniquely identify the test hardware. If there is a deviation in a batch of acceleration values, the specific equipment can be quickly determined to check for calibration or fault problems and avoid the spread of problems; the test site identifier can locate single-site anomalies and directly locate contact or circuit problems at that site; the load board or socket board position is associated with the hardware connection stability. If the X-axis acceleration drifts at the same position corresponding to multiple sites, the line impedance of the load board at that position can be preferentially checked to shorten the hardware fault troubleshooting cycle. The sampling rate dimension can distinguish the acceleration data in static and dynamic scenarios, avoid analysis deviations caused by sampling rate mismatches, and at the same time, the multi-dimensional indicators can construct a complete data context of the equipment, site, hardware position, and sampling rate, provide structured data for reliability index calculation, support quality evaluations such as consistency analysis of different sites of the same equipment and stability comparison of different hardware positions at the same site, and improve the quality control accuracy and problem response speed of batch testing of multi-axis sensors.

[0045] S130. Extract valid data from the test data sequence using an event isolation window to form a valid data sequence.

[0046] In this embodiment of the invention, the event isolation window can be specifically understood as: a time or data segment filtering interval set for MEMS test data, used to filter out local data segments corresponding to valid test events (such as the sensor stable acquisition phase or a specific excitation response phase) from a continuous test data sequence, excluding invalid interference data (such as fluctuating data at the start of the test or truncated data at the end of the test). The boundary of the window can be defined by time thresholds, data stability conditions, or event identifiers. Valid data can be specifically understood as: data that meets the test target requirements and can truly reflect the performance of MEMS. This data needs to meet conditions such as stability, integrity, and no abnormal fluctuations. For example, for multi-axis sensor MEMS devices, in static zero-drift testing, the acceleration value continuously output after the sensor is powered on and stabilizes (e.g., 10 seconds after power-on), with a fluctuation range between -0.05grav and 0.05grav; in dynamic impact testing, the transient impact data at the start of the test and the zeroing data after the test ends are excluded, and the continuous acceleration response data output during the application of the excitation signal is used. The effective data sequence can be understood as: after filtering through the event isolation window, only the effective data is retained to form a structured sequence. The data format is consistent with the original test data sequence, but invalid interference items are removed, and it can be directly used for subsequent reliability index calculations.

[0047] Specifically, the boundary rules for the event isolation window are defined according to the test type (e.g., static or dynamic test). For static zero-drift tests, the window boundary can be set according to a time threshold (e.g., from 10 seconds after test start to 5 seconds before test end, excluding start-up fluctuations and end-of-test truncation data). For dynamic impact tests, the window boundary can be set according to data triggering conditions (e.g., the window starts when the acceleration value exceeds 0.5grav and ends when the acceleration value falls below 0.1grav, isolating data during the stimulus response phase). The event isolation window is applied to the structured test data sequence. Each test data in the sequence is traversed, and it is determined whether it falls within the window interval. Test data falling within the window interval is marked as valid. All marked valid test data are re-integrated according to the original sequence's time or dimensional correlation to form a valid data sequence. This ensures that the sequence retains the original analytical dimensional correlation and only contains valid data that meets the test requirements, eliminating interference for subsequent analysis.

[0048] Optionally, based on the above embodiments, extracting valid data from the test data sequence using an event isolation window to form a valid data sequence may include: acquiring pre-excitation data of the test data in the test data sequence and calculating the corresponding baseline value; setting a dynamic threshold around the baseline value according to the expected MEMS device test data; wherein the dynamic threshold is determined based on the type of the MEMS device and the test environment conditions; sequentially detecting the relationship between each test data and the dynamic threshold from the test data sequence, and when test data reaching the dynamic threshold is detected from the test data sequence, taking the test time corresponding to the test data as the starting point of the event isolation window; performing signal attenuation analysis and energy integration analysis on the test data sequence to determine the ending point of the valid data in the test data, which is taken as the ending point of the event isolation window; and acquiring data falling into the event isolation window from the test data sequence as valid data.

[0049] In this embodiment of the invention, the pre-excitation data can be specifically understood as: the stable output data of the MEMS device before receiving a test excitation (such as applying an acceleration or voltage signal), reflecting the initial state of the device without external excitation (such as static zero drift value), and serving as the basis for calculating the baseline value, for example, the test data output by a multi-axis accelerometer before an impact test. The baseline value can be specifically understood as: a benchmark reference value (such as the arithmetic mean or median) calculated based on the pre-excitation data, representing the normal output level of the device in the unexcited state, used to determine whether subsequent data produces an effective response due to excitation.

[0050] The dynamic threshold can be understood as a critical value set around the baseline value and dynamically adjusted according to the type of MEMS device (such as an accelerometer or gyroscope) and the test environment (such as temperature or humidity). It is used to distinguish between the effective response caused by the excitation and noise interference. For example, the dynamic threshold for an accelerometer can be -0.05grav to 0.05grav. In high-temperature environments, the dynamic threshold of the accelerometer can be expanded to adapt to the temperature drift characteristics of the device. The event isolation window start point can be understood as the time point corresponding to the first test data in the test data sequence that reaches the dynamic threshold. It marks the beginning of the effective test event (such as the excitation response phase). For example, the time point when the acceleration value first exceeds the baseline value +0.05grav (dynamic threshold) in an impact test. Signal attenuation analysis can be understood as analyzing the signal change trend after the excitation response in the test data sequence to determine whether the signal has fallen from the peak to a stable state (such as approaching the baseline value). It is used to determine the end boundary of effective data. For example, the process analysis of the acceleration value gradually falling back to the preset stable threshold after the impact response. Energy integration analysis can be understood as follows: by calculating the cumulative energy value of the test data sequence over a certain period of time (such as the square integral of the acceleration signal), it is determined whether the signal energy has dropped to the noise level, thus helping to determine the end point of valid data. For example, when the energy integration value is lower than a preset energy threshold for a preset number of consecutive sampling periods, the valid response is considered to have ended. The event isolation window end point can be understood as: the valid data termination time point determined after signal attenuation analysis and energy integration analysis, marking the end of the excitation response phase. For example, in an impact test, the time point when the acceleration value stably falls back to the baseline value and the energy integration is lower than the threshold.

[0051] Specifically, pre-excitation data (such as a preset number of sampling points after test initiation or before excitation application) is separated from the test data sequence, and the baseline value is obtained by calculating its average or median. Based on the MEMS device type and test environment conditions, a dynamic threshold is set around the baseline value to ensure that the threshold can both filter noise interference and capture the effective signal caused by the excitation. The test data sequence is traversed, and when the first test data reaching the dynamic threshold is detected, its corresponding time is marked as the starting point of the event isolation window, signifying the beginning of the effective response. Signal attenuation analysis (observing whether the data falls from the peak to a stable state) and energy integration analysis (determining whether the signal energy drops to the noise level) are performed on the test data after the starting point. The end point of the effective data, i.e., the end point of the window, is determined by combining the results of both analyses (when both are satisfied or only one of the analysis results is satisfied). All data falling within the interval between the starting and ending points is extracted from the test data sequence to form the effective data sequence, ensuring that only data related to the excitation response is retained, excluding pre-excitation static data and post-excitation noise data.

[0052] The baseline value is calculated based on the pre-excitation data, using the intrinsic state of the device as a benchmark to avoid baseline drift errors caused by fixed reference values, thus providing a basis for valid signal judgment. Dynamic thresholds are set based on MEMS device type and test environment conditions. This allows for both narrowing the threshold to capture subtle responses in specific devices and widening it in harsh environments to avoid misjudgments due to temperature drift and other interference, adapting to various devices and scenarios. The relationship between the data and the dynamic threshold is detected sequentially to locate the starting point of the event isolation window and eliminate static redundant data before excitation. The ending point is determined through dual verification using signal attenuation analysis and energy integration analysis to resist signal glitches and ensure the complete excitation response process is included. Data within the window is extracted to form a valid sequence, eliminating invalid interference and providing a clean data source for subsequent performance index calculations. This avoids index deviations caused by data truncation or noise contamination, improving the reliability of test analysis.

[0053] S140. Determine the test group based on the dimension corresponding to each valid data sequence.

[0054] Specifically, based on subsequent test and analysis objectives (such as equipment consistency analysis or site stability analysis), the grouping priority of dimensions can be defined (e.g., grouping by test equipment first, then by test site, and finally by sampling rate). All valid data sequences are traversed, and the complete dimensional information bound to each sequence (such as test equipment, test site, and sampling rate) is extracted. According to the set dimensional priority, valid data sequences with the same dimensional characteristics are grouped together. For example, all valid data sequences for Test Equipment 1 are first filtered out. Then, within this equipment group, they are further split by test site, such as aggregating the sequences for site 3 into the group "Test Equipment 1 - Site 3". Finally, within the site group, they are split into subgroups by sampling rate, forming test groups with clear hierarchy and unified dimensional characteristics. This ensures that valid data sequences within the same group have the same core dimensional conditions, meeting the needs of subsequent comparative analysis and indicator calculation.

[0055] S150. Calculate the reliability index of the test equipment based on the valid data sequence of each test group; wherein the reliability index includes: CpK, standard deviation, repeatability, and reproducibility.

[0056] In this embodiment of the invention, reliability indicators can be specifically understood as parameters that quantify the testing accuracy, stability, and consistency of the testing equipment. These parameters are used to evaluate whether the equipment can stably output test data that meets the requirements. Specifically, they can include CpK, standard deviation, repeatability, and reproducibility. CpK can be specifically understood as: measuring the degree of matching between the test data output by the testing equipment and preset specification limits (such as the upper and lower limits of allowable acceleration errors for MEMS devices). A larger value indicates higher testing accuracy and better compliance with specifications. The calculation formula involves the data mean, specification limits, and standard deviation. Standard deviation can be specifically understood as: reflecting the dispersion of the effective data sequence within the same test group. A smaller value indicates more concentrated data and more stable test equipment output. It is the basic parameter for calculating CpK and repeatability. Repeatability can be specifically understood as: the degree of consistency of the effective data sequence when the same testing equipment and the same test station repeatedly test the same MEMS device, reflecting the testing stability of the equipment itself (e.g., when the same equipment repeatedly tests the same accelerometer a preset number of times, the smaller the data difference, the better the repeatability). Reproducibility can be specifically understood as the degree of consistency of the effective data sequence when different test equipment (or different test sites) test the same MEMS device, reflecting the test consistency between equipment (or sites) (e.g., when equipment A and equipment B test the same accelerometer, the smaller the data difference, the better the reproducibility).

[0057] Specifically, for each test group, all valid data sequences within the group are extracted, and the data format and units are standardized. Reliability indicators are calculated step-by-step: When calculating the standard deviation, the deviation of all data within the group from the mean is calculated using a formula to obtain a value reflecting data dispersion. When calculating CpK, the mean and standard deviation of the data are substituted into the preset MEMS device test specification limits, and the matching degree between the device output data and specification requirements is calculated using the CpK formula. When calculating repeatability, the standard deviation or coefficient of variation is calculated for repeated test data sequences of the same device within the same group to quantify the stability of repeated testing of the device. When calculating reproducibility, valid data sequences of the same device from different test groups are compared, and the consistency between devices is quantified by calculating the differences in data between groups (such as mean difference or standard deviation). The results of each indicator are correlated with the test group dimension (e.g., "Device 1 - CpK: 1.8 - Standard Deviation: 0.005grav") to generate a device reliability assessment report, used to determine whether the device meets the test accuracy and stability requirements.

[0058] The technical solution of this invention, by acquiring test data files generated by multiple test devices and their multiple test stations for parallel testing of batch MEMS devices, can cover the data requirements of high-capacity test scenarios at once, avoiding data fragmentation. By parsing the file according to at least one set parsing dimension, structured test data sequences corresponding to each dimension can be obtained, thus determining the data correlation between different dimensions. Using an event isolation window to extract valid data from the test data sequences to form valid data sequences can eliminate redundant and interfering data, ensuring that subsequent analysis is based on valid data from real responses, improving the accuracy of MEMS device test data processing. Determining test groups based on the dimensions corresponding to the valid data sequences matches the calculation logic of reliability indicators. Calculating reliability indicators based on the valid data sequences of each test group allows for a comprehensive evaluation of the test system performance from multiple dimensions, including data dispersion, process compliance with specifications, and test system measurement reliability. The automated processing flow improves the efficiency of MEMS device test data processing, ensuring timely detection of problems in the testing process and guaranteeing the test quality of batch MEMS devices.

[0059] Example 2

[0060] Figure 2 This is a flowchart of another method for processing MEMS device test data provided in Embodiment 2 of the present invention. This embodiment is a refinement of the step of "determining test groups based on the dimensions corresponding to each valid data sequence" in the above embodiment. Accordingly, as... Figure 2 As shown, the method includes:

[0061] S210. Obtain the test data file. The test data file contains test values ​​generated by parallel testing of the set attributes of a batch of MEMS devices through multiple test stations of multiple test devices.

[0062] S220. From the test data file, according to at least one set parsing dimension, obtain the structured test data sequence corresponding to each dimension. The set parsing dimension includes at least one of the following: test equipment, test site, MEMS device, set attributes, test time, and test configuration.

[0063] S230. Use an event isolation window to extract valid data from the test data sequence to form a valid data sequence.

[0064] S240. Group the data according to the test equipment, test site, and test cycle number in the test configuration corresponding to each valid data sequence to determine multiple test groups.

[0065] In this embodiment of the invention, the test cycle number can be specifically understood as: in the same test process, when the same MEMS device (or the same batch of devices) is repeatedly tested under the same test equipment and the same test site, the sequence number is used to distinguish each repeated test. The valid data sequence of each repeated test can be traced through the sequence number, which is used to verify the repeatability of the test.

[0066] Specifically, the test device identifier (e.g., device A and device B), test site identifier (e.g., site 1 and site 2), and test cycle number (e.g., cycle 1 and cycle 2) bound to all valid data sequences are extracted. Valid data sequences with the same dimensional characteristics are grouped sequentially according to the hierarchical order of test device, test site, and test cycle number. For example, first, sequences belonging to the same test device are filtered and aggregated into device-level groups. Then, within each group of the same test device, sequences belonging to the same site are further split into site-level subgroups. Finally, within each subgroup, sequences belonging to the same cycle are split into cycle-level final test groups. Through three-level dimensional matching, multiple test groups are formed, ensuring that the valid data sequences within each group come from the same device, the same site, and the same test cycle, providing a data foundation for subsequent analysis of the device's test stability at specific sites and in specific cycles.

[0067] S250. Calculate the reliability index of the test equipment based on the valid data sequences of each test group. The reliability index includes: CpK, standard deviation, repeatability, and reproducibility.

[0068] Optionally, based on the above embodiments, calculating the repeatability and reproducibility index of the test device according to the effective data sequence of each test group may include at least one of the following: selecting the test group with the highest consistency of effective data sequence from the effective data sequence of each test group, calculating repeatability and reproducibility as the best-case index; selecting the test group with the lowest consistency of effective data sequence from the effective data sequence of each test group, calculating repeatability and reproducibility as the worst-case index; randomly selecting test groups from the effective data sequence of each test group to form a set number of test group combinations, calculating repeatability and reproducibility as the average-case index.

[0069] In this embodiment of the invention, the best-case metric can be specifically understood as: repeatability and reproducibility calculated from the test group with the highest data consistency (e.g., smallest standard deviation), representing the performance of the test system under ideal conditions. The worst-case metric can be specifically understood as: repeatability and reproducibility calculated from the test group with the lowest data consistency (e.g., largest standard deviation), used to verify the reliability of the system under stress conditions. The average-case metric can be specifically understood as: repeatability and reproducibility calculated by randomly selecting a set number of test group combinations (e.g., a combination of 3 devices × 3 sites), reflecting the overall average performance of the system. A test group combination can be specifically understood as: a set of multiple test groups selected according to set rules (e.g., 3 devices × 3 sites), used to simulate random scenarios in actual testing.

[0070] Specifically, for all test groups, data consistency analysis (such as calculating the standard deviation or CpK of each group's valid data sequences) is used to identify the test groups with the smallest data fluctuations (e.g., smallest standard deviation or highest CpK) and the largest fluctuations (e.g., largest standard deviation or lowest CpK). For the group with the highest consistency, repeatability (consistency of different cycles of data within the same group) and reproducibility (consistency of this group with other groups under the same conditions) are calculated as best-case indicators, reflecting the ideal performance of the system. The same calculations are performed on the group with the lowest consistency as worst-case indicators, verifying the system's resilience. Multiple test group combinations are generated according to set rules (e.g., randomly selecting a 3×3 equipment site combination). Repeatability and reproducibility are calculated independently for each combination, and then the average case indicator (e.g., mean or pass rate) is obtained through statistical averaging to reflect the overall robustness of the system.

[0071] In a specific example, the system selects the site combinations with the highest data consistency that meet the set rules (extracting 3×3 device site combinations), constructs a "test device-test site-test cycle number" matrix, and calculates the GR&R (reproducibility and repeatability) of this combination, representing the system's optimal performance under ideal conditions. For worst-case indicators, the system selects the site combinations with the largest data fluctuations that meet the set rules, calculates their GR&R, and verifies whether the system meets compliance requirements under extreme conditions. For average-case indicators, the system uses Monte Carlo simulation to randomly select a preset number of combinations that meet the set rules, calculates the GR&R for each combination independently, and calculates the pass rate to reflect the overall robustness of the system. Understandably, spatial metadata (such as the physical location of sites) can assist in optimizing combination selection (e.g., selecting adjacent sites to simulate thermal effects), and the indicator distribution can be visualized through heatmaps to aid in root cause analysis.

[0072] By selecting the group with the highest data consistency to calculate the best-case metric, the performance ceiling of the test system under ideal conditions can be determined. By selecting the group with the lowest data consistency to calculate the worst-case metric, extreme risks of the system can be proactively exposed, problematic groups can be located, and hardware failures (such as probe wear) or program defects can be investigated, thereby improving the risk screening rate before mass production and avoiding batch test failures. By randomly sampling a set number of combinations to calculate the average metric, the overall robustness of the system can be reflected through statistical distribution, improving sampling accuracy and simulating the randomness of mass production. The three-way collaborative full-dimensional evaluation closed loop shortens the evaluation time and improves the reliability of the evaluation system performance.

[0073] Furthermore, based on the above embodiments, after calculating the reliability index of the test equipment according to the valid data sequence of each test group, the method may further include: marking test equipment that does not meet the requirements according to the reliability index and the set tolerance limit.

[0074] In this embodiment of the invention, the tolerance limit can be specifically understood as: a preset threshold for qualified indicators based on MEMS device testing requirements (such as automotive electronics or consumer electronics applications) or industry standards (such as IATF 16949), for example, GR&R less than 30% (acceptable), CpK greater than or equal to 1.33 (meets precision testing), and standard deviation less than or equal to 0.01grav (upper limit of data fluctuation). Test equipment that does not meet the requirements can be specifically understood as: after comparison, test equipment with at least one reliability indicator exceeding the tolerance limit, which is considered equipment requiring labeling. Labeling can be specifically understood as: visually or structurally marking test equipment that does not meet the requirements, recording information such as test equipment identification (e.g., serial number), non-compliant indicators, exceeding values, and tolerance limits (e.g., Equipment 1 - Non-compliant indicator: GR&R - Exceeding value: 45% - Tolerance limit: 30%), facilitating quick location of problematic equipment by staff.

[0075] Specifically, the reliability index results of all test equipment are obtained, and the preset tolerance limits are retrieved. Each indicator of each equipment is compared and judged one by one. If one or more indicators of the equipment exceed the tolerance limit, the equipment is determined to be unqualified. The unqualified equipment is marked, and the marking results are integrated into the equipment evaluation report or visualization platform. It can be presented in the form of color marking (such as red highlighting) or separate list to ensure that staff can quickly identify the problematic equipment and provide clear guidance for subsequent maintenance, calibration or replacement.

[0076] In a specific example, the reliability metrics of each test device are summarized, and the tolerance limits for the corresponding scenario are retrieved (e.g., automotive electronics scenario requirements: GR&R < 30%, CpK ≥ 1.33, and standard deviation ≤ 0.01grav). A multi-dimensional comparison is performed to check if any individual metric exceeds the limit (assuming device 1 has an out-of-limit item). Then, considering the specific requirements of the scenario (e.g., automotive electronics requires all metrics to meet the standard, not just some), device 1 is determined to be a non-compliant test device, and it is labeled: the labeling content includes "test device identification, non-compliant metric, out-of-limit value, and tolerance limit," and can also include the degree of exceeding the limit (e.g., GR&R exceeding the limit by 12 percentage points), and the associated test group (e.g., data anomalies in the device 1-site 5-cycle 2 group). The labeling results are then synchronized to the visualization platform (e.g., the site corresponding to the device is highlighted in red on the heatmap).

[0077] By labeling test equipment that fails to meet reliability indicators and set tolerance limits, the information of substandard equipment can be made explicit, highlighting risks. Compared with traditional manual data comparison, this reduces the risk identification omission rate and avoids test data failure caused by hidden defects. The labeling results can clearly point to specific problematic equipment and indicators (such as a certain equipment exceeding the standard deviation). Staff can directly check the signal link or calibration status of the equipment without the need for a full-domain inspection, shortening the troubleshooting time and improving efficiency. The labeling records (including the judgment basis and processing time) can serve as compliance evidence, meeting the equipment evaluation standardization requirements of quality systems such as IATF 16949, and can also form a traceability chain, facilitating the return of data sources. By labeling and blocking substandard equipment in advance, it can be prevented from participating in mass production testing, preventing MEMS device test data distortion and misjudgment due to equipment problems, and reducing batch rework costs and delivery delays.

[0078] The technical solution of this invention involves acquiring test data files generated by multiple test devices and their subordinate test stations during parallel testing of batch MEMS devices. From these files, structured test data sequences corresponding to each dimension are obtained through parsing according to at least one set parsing dimension. An event isolation window is used to extract valid data from the test data sequences to form valid data sequences. These valid data sequences are then grouped according to the test device, test station, and test cycle number in the test configuration to determine multiple test groups. The test devices and test stations are the hardware carriers that affect the test results, and the test cycle number reflects the repeated test process under the same hardware combination. Grouping according to these three dimensions can determine the complete test scenario for a specific device, specific station, and specific cycle, avoiding grouping confusion caused by missing dimensions. For example, grouping only by device would confuse the same device. Grouping data from different sites by site alone ignores repeated test information from different cycles within the same site, ensuring that data within each group comes from a consistent hardware environment and test cycle, establishing a clear scenario for subsequent analysis. This grouping method adapts to the repeatability and reproducibility analysis requirements in measurement system analysis, quickly extracting sequences from different cycles within the same site for repeatability calculation and sequences from the same device across different sites for reproducibility calculation, without the need for additional data correlation analysis. Simultaneously, this grouping method eliminates issues such as missed cycle numbers and mismatched device / site pairings that easily occur with manual grouping, making it particularly suitable for high-capacity testing scenarios with multiple devices and sites. Furthermore, the grouping results directly support subsequent scenario-based data filtering, such as extracting all cycle data from a specific device and site without secondary processing, improving the efficiency of subsequent scenario-based analysis and metric calculation. Reliability metrics are calculated based on the effective data sequences of each test group, enabling a comprehensive multi-dimensional evaluation of test system performance and ensuring the testing quality of batch MEMS devices.

[0079] Example 3

[0080] Figure 3 This is a flowchart illustrating another method for processing MEMS device test data according to Embodiment 3 of the present invention. This embodiment is a refinement of the method for processing MEMS device test data in the above embodiments. Accordingly, as... Figure 3 As shown, the method includes:

[0081] S310. Obtain the test data file. The test data file contains test values ​​generated by parallel testing of the set attributes of a batch of MEMS devices through multiple test stations of multiple test devices.

[0082] S320. From the test data file, according to at least one set parsing dimension, obtain the structured test data sequence corresponding to each dimension. The set parsing dimension includes at least one of the following: test equipment, test site, MEMS device, set attribute, test time, and test configuration.

[0083] S330. Use an event isolation window to extract valid data from the test data sequence to form a valid data sequence.

[0084] S340. Determine the test group based on the dimension corresponding to each valid data sequence.

[0085] S350. Calculate the reliability index of the test equipment based on the valid data sequences of each test group. The reliability index includes: CpK, standard deviation, repeatability, and reproducibility.

[0086] S360. Based on reliability metrics, generate at least one of the following visualization results and display it visually: a time-domain plot of MEMS test data, a histogram of peak response across multiple test devices, a trend graph of CpK values ​​over time, or a heat map overlaid on a load board or socket board layout diagram that correlates test results with the location of physical test sites.

[0087] In this embodiment of the invention, the time-domain graph of MEMS detection data can be specifically understood as a line graph or curve with time as the horizontal axis and MEMS device detection data (such as acceleration values ​​or angular velocity values) as the vertical axis, used to display the dynamic changes of data over test time (such as the time history of impact response). The histogram of peak responses across multiple test devices can be specifically understood as: statistically analyzing the distribution frequency of peak response data (such as maximum acceleration values) during MEMS device testing by multiple test devices, with the horizontal axis representing the peak interval and the vertical axis representing the number of devices in that interval, reflecting the consistency of peak responses among devices. The trend graph of CpK values ​​over time can be specifically understood as a trend line graph with time as the horizontal axis and CpK values ​​as the vertical axis, used to display the changing trend of the CpK values ​​of the test devices at different test stages (such as different dates or different batches), and to determine the stability of device performance. A heatmap can be understood as an image that correlates test results (such as GR&R values, failure rates, or CpK values) with the physical test site locations, and visually presents this information on a load board or socket board layout diagram using color gradients (e.g., red for high values ​​and green for low values). It is used to expose systemic problems related to the site location (e.g., abnormal performance of sites in a certain area). The load board or socket board layout diagram can be understood as a drawing that reflects the physical structure of the test hardware, marking the spatial location of each test site (socket) (e.g., row and column coordinates), and serves as the underlying carrier of the heatmap.

[0088] Specifically, from the calculated reliability metrics (such as CpK, peak response, and GR&R) and raw test data, the information required for visualization (such as time points and corresponding values ​​of time-domain data, peak response values ​​of each device, CpK values ​​at different time points, test results and location coordinates of each site, etc.) is extracted. The data is then transformed according to different visualization type rules: time-domain data is mapped to a time-value line chart, peak responses of multiple devices are statistically analyzed into interval frequency distribution histograms, CpK values ​​are correlated with time to generate trend line charts, site test results are mapped to layout diagrams by location and assigned color gradients to generate heatmaps, and the generated graphs are centrally displayed using visualization tools (such as data analysis software or dedicated testing platforms).

[0089] S370. The extracted valid data sequence, calculated reliability indicators, and generated visualization results are compiled into a test report conforming to MSA in at least one standardized format and exported. The report is automatically formatted to meet the quality audit requirements of semiconductor manufacturing.

[0090] Specifically, the report summarizes the metadata, reliability indicators, and visualization results of all valid data sequences, and formats them according to the structured MSA report framework. This can include modules such as a report cover (e.g., project name and report date), data sourcing (e.g., sources and screening process of valid data), indicator evaluation (e.g., numerical values ​​of each indicator, comparison with tolerances, and judgment results), visualization (e.g., embedded visualizations and corresponding analysis explanations), and conclusions and recommendations (e.g., whether the equipment meets standards and optimization directions). Based on requirements, a standardized format (e.g., PDF (Portable Document Format), Excel (spreadsheet software), or HTML (HyperText Markup Language)) is selected for conversion. For PDF format, the graphics and text must be neatly formatted and uneditable; for Excel format, data and indicators must be organized into separate tables and formulas must be viewable; for HTML format, it must be compatible with web page display and ensure that graphics load correctly. Next, the verification report is checked to ensure it meets semiconductor quality audit requirements, such as whether the data is traceable to the original test data file, whether the indicator judgments conform to industry standards (e.g., AIAG (Automotive Industry Action Group) MSE), whether the visualization results accurately reflect the data characteristics, whether the report includes a signature and date, and whether the records are complete. After verification, the report is exported. The report can be saved locally or uploaded to the quality audit platform for subsequent audit review or test optimization reference.

[0091] Furthermore, based on the above embodiments, the method for processing MEMS device test data may further include: mapping the test data to physical locations on a load board or socket board, and generating a heat map of CpK values ​​or failure rates across the test fixture.

[0092] In this embodiment of the invention, the test fixture can be specifically understood as a load board or a socket board. The test fixture covers multiple load boards or socket boards, or covers all test stations on a single load board or socket board, ensuring comprehensive data coverage rather than being limited to local stations.

[0093] Specifically, the calculated reliability metrics can also include the failure rate of each test site. Accordingly, obtain the physical layout diagram of the load board or socket board (including the coordinates of each test site, such as row and column numbers), and simultaneously organize the CpK values ​​or failure rate data of all test sites, establishing a correlation table between the site's physical coordinates and the corresponding data. Based on color mapping rules (e.g., green for CpK values ​​≥ 1.33, yellow for 1.0 ≤ CpK < 1.33, and red for CpK < 1.0; green for failure rate < 5%, yellow for 5% ≤ failure rate < 15%, and red for failure rate ≥ 15%), match the CpK value or failure rate of each site to the corresponding color according to the color rules, and fill it into the corresponding coordinates of the site in the physical layout diagram. This achieves a visual conversion of data, color, and location, generating a heatmap covering all test sites (across test fixtures), and labeling the color legend (indicating the numerical range corresponding to the color) with the layout diagram coordinates.

[0094] By mapping test data to the physical locations of load boards or socket boards and generating CpK values ​​or failure rate heatmaps across test fixtures, and using color gradients to correlate data distribution with the physical layout, common defects in different areas can be visually exposed. This addresses the problem of overlooked hidden issues caused by the fragmentation of traditional data and location information, improving the identification rate of space-related issues. Heatmaps can accurately pinpoint abnormal areas, narrowing the investigation scope by combining physical layout features, shortening hardware fault diagnosis time, and improving troubleshooting efficiency. Cross-fixture heatmaps can also compare performance differences across multiple load boards, such as failure rate differences at edge sites, providing a basis for optimizing fixture structures, such as adjusting probe pressure and setting benchmark calibration sites. Heatmaps can serve as visual evidence for semiconductor quality audits, showing changes in spatial data before and after rectification, meeting industry standards for traceability and verifiability. They can also be used to verify the testing validity of batch devices by correlating location data, providing support for quality accountability and adapting to the high-precision requirements of multi-site parallel testing in semiconductors.

[0095] Furthermore, based on the above embodiments, before exporting the test report, the method may further include: verifying the completeness of the MSA report by comparing it with a checklist exported from a preset standard.

[0096] In this embodiment of the invention, the preset standard can be specifically understood as: industry-standard quality management and measurement system analysis specifications, such as AIAG MSA 4th Edition (Automotive Industry Action Group's Measurement System Analysis Guide) and IATF 16949 (Automotive Production Parts Quality Management System Standard), which have clear requirements for the content, format, and verification items of the MSA report. The checklist can be specifically understood as: a structured checklist extracted from the preset standard clauses, containing the elements that an MSA report must include (such as data sources, indicator calculation methods, tolerance standards, visualization results, and conclusion judgments), used to verify item by item whether the report conforms to the standard.

[0097] Specifically, the system includes built-in checklists based on preset standards. For example, for AIAG MSA version 4, the checklist includes items such as "whether the measurement object and specification limits are clearly defined (e.g., the specification limit for MEMS accelerometers is ±0.1 grav)," "whether GR&R calculation uses analysis of variance (ANOVA)," and "whether repeatability and reproducibility are distinguished." For IATF 16949, the checklist can add audit-related items such as "whether the calibration status of the test equipment is recorded," "whether the handling records for non-compliant equipment are complete," and "whether the report includes the signatures of the compiler and reviewer." Before exporting the test report, the content of the MSA report to be exported is compared with each item in the checklist to check for any unmet requirements. If any is missing, the system automatically prompts for the content to be supplemented and the relevant standard clauses. After supplementing the report content according to the prompts, the system verifies again until all check items pass, confirming that the MSA report meets the completeness requirements, and then exports the test report.

[0098] By extracting the requirements from the standards to form verification items, and comparing the report content item by item, the report can be forced to fully align with industry standards, thereby improving the report compliance rate. The checklist can systematically identify information gaps, making the data traceable and verifiable, thus improving the credibility of the report. The report can fully reflect the system risk points, and the maintenance plan based on this can intercept problematic equipment in advance, ensuring the stability of MEMS device testing.

[0099] In a specific example, a MEMS device test data processing system may include: a dedicated test data parser module, an event isolation module, a data orchestration engine, a statistical analysis module, a visualization module, a simulation module, and a reporting module. The dedicated test data parser module receives non-standardized test data files generated by the test equipment. By identifying the test configuration (such as sensor type and firmware version) and applying specific parsing rules, it extracts multi-axis sensor time-series data (such as X, Y, and Z-axis acceleration and angular velocity), device serial number, test site identifier, physical location of the load board or socket board, and environmental data. This transforms the heterogeneous data into a standardized internal model, resolving the problem of the original data format being chaotic. The event isolation module, through a dynamic threshold algorithm for MEMS device characteristic calibration, calculates the baseline noise level based on the pre-excitation data, then sets a threshold based on the expected device response. It automatically detects and isolates only the effective time window containing the main excitation response, excluding irrelevant noise data and providing a clean data source for subsequent statistical analysis. The data orchestration engine automatically parses test equipment, test site, and test cycle information from metadata, constructing a GR&R matrix (Part × Operator × Trial format) conforming to the AIAG MSA standard. It supports three simulation modes: best-case (selecting the combination of equipment or sites with the highest data consistency), worst-case (selecting the combination with the greatest fluctuation), and random sampling, solving the problems of error-prone and time-consuming manual combination selection. The statistical analysis module calculates reliability indicators such as CpK, standard deviation, and GR&R based solely on valid data within the isolation window. It can also combine calibration data to apply correction factors, ensuring the accuracy of indicator calculations. The visualization module generates time-domain plots (showing sensor response trajectories and isolation windows), histograms (presenting peak response distribution across devices), CpK trend charts (reflecting indicator changes over time), and spatial heatmaps (mapping CpK values ​​or failure rates to the physical layout of load boards or socket boards), intuitively exposing equipment performance fluctuations or site-related defects. The simulation module conducts hypothetical scenario tests without altering the physical data through best-case, worst-case analysis, and Monte Carlo random sampling, evaluating the robustness of the test system. The reporting module compiles the extracted metadata, calculated statistical indicators, and generated visualization results into a structured MSA report conforming to AIAG MSA 4th Edition and IATF 16949 standards in standardized PDF, Excel, or HTML formats. Before exporting, the report's completeness is verified against a standard checklist to ensure compliance with semiconductor quality audit requirements.

[0100] Figure 4 This is a time-domain graph of MEMS detection data applicable to embodiments of the present invention, such as... Figure 4As shown, with time (milliseconds) on the horizontal axis and acceleration (g) on ​​the vertical axis, the dynamic response of the MEMS device's acceleration along the X, Y, and Z axes is displayed. The colored curves correspond to the acceleration data for each of the three axes, while the green area represents the isolation analysis window (12.5-35 milliseconds). This window is automatically identified by the system's event isolation module, excluding baseline noise before 12.5 milliseconds and invalid data after 35 milliseconds, retaining only the valid time series containing the main stimulus responses. This provides clean time-domain data support for subsequent statistical analysis (such as calculating CpK and GR&R), and intuitively presents the acceleration variation pattern of the MEMS device within the valid test period.

[0101] Figure 5 This is a heat map, applicable to embodiments of the present invention, overlaid on a load board or socket board layout diagram that associates test results with the location of physical test sites, such as... Figure 5 As shown, a heatmap correlates test results (CpK values) with the locations of physical test sites. Using the site layout of the load board or socket board as the base map, each colored block represents a test site. The color gradient ranges from green (CpK ≥ 2.0) to red (CpK < 0.67), distinguishing the process capability levels of different sites: green sites demonstrate excellent CpK performance, yellow sites require optimization, and red sites fail to meet performance standards. This heatmap allows for a visual understanding of the spatial distribution patterns of site performance (e.g., sites concentrated in red in a certain area), quickly locating hardware defects on the load board (e.g., probe wear or poor line contact), and providing a spatial dimension for decision-making regarding hardware maintenance and performance optimization of the test system.

[0102] Figure 6 This is a schematic diagram of a test grouping based on repeatability and reproducibility indicators applicable to an embodiment of the present invention, as shown below. Figure 6 As shown, this table is a GR&R matrix generated by the system's data orchestration engine, following the AIAG MSA standard's "Component × Operation Station (Test Site) × Test" structure. The Component column contains the device ID (Identifier) ​​and description of different MEMS devices (such as accelerometers A, B, C, and D). The Operation Station (Test Site) column is grouped by Site ID (Sites 12, 35, and 58) and number of tests (1-3). The values ​​in the table represent the test data for each device at its corresponding site and test. Based on this data, the system independently calculates the repeatability (consistency of multiple tests within the same site) and reproducibility (consistency between different sites) of each "Component × Site × Test" combination, thereby selecting the best-case, worst-case, or random sampling combinations to provide structured data support for evaluating the GR&R performance of the test equipment.

[0103] The system improves the efficiency of MSA report generation, shortens the new product introduction cycle, and reduces labor costs through inter-module collaborative operation. A dedicated test data parser eliminates data extraction errors caused by format heterogeneity, an event isolation module eliminates noise interference, and a data orchestration engine avoids manual combination and selection errors, ensuring that GR&R and CpK calculations are based on statistically valid data and improving result repeatability. Standardized reports automatically adapt to industry standards such as AIAG MSA and IATF 16949, accompanied by complete audit trails (such as software version, timestamps, and data sources), meeting the quality audit requirements of high-reliability fields such as automotive electronics and industrial semiconductors. Spatial heatmaps intuitively present the correlation between the physical location and performance of test sites, enabling rapid location of hardware defects such as load board probe wear and abnormal regional temperature control. Combined with scenario testing by the simulation module, it can predict the system's stability under extreme conditions in advance, reducing the misjudgment rate of mass production test data and providing technical support for the efficiency, accuracy, and compliance of MEMS device testing.

[0104] The technical solution of this invention involves acquiring test data files generated by multiple test devices and their subordinate test stations during parallel testing of batch MEMS devices; parsing these files according to at least one set parsing dimension to obtain structured test data sequences corresponding to each dimension; extracting valid data from the test data sequences using an event isolation window to form valid data sequences; determining test groups based on the dimensions corresponding to the valid data sequences; calculating reliability indicators based on the valid data sequences of each test group; comprehensively evaluating the performance of the test system from multiple dimensions to ensure the test quality of batch MEMS devices; generating at least one visualization result based on the reliability indicators and displaying it visually; compiling the extracted valid data sequences, the calculated reliability indicators, and the generated visualization results into a test report conforming to MSA in at least one standardized format and exporting it; transforming the reliability indicators, which are abstract numerical values, into intuitive graphics through visualization results such as time-domain waveforms, trend graphs, and spatial heatmaps, which helps to quickly capture key information and improve the efficiency of quality problem identification; compiling the test report conforming to MSA in a standardized format avoids the situation of format chaos and missing elements when manually compiling reports, reduces report rework costs, adapts to different scenario requirements, and improves the efficiency of information synchronization in cross-team collaboration.

[0105] Example 4

[0106] Figure 7 This is a schematic diagram of a MEMS device test data processing device provided in Embodiment 4 of the present invention. Figure 7 As shown, the device includes: a file acquisition module 710, a test parsing module 720, a window extraction module 730, a test grouping module 740, and a metric calculation module 750, wherein:

[0107] The file acquisition module 710 is used to acquire a test data file; wherein the test data file includes test data generated by parallel testing of set attributes of batch MEMS devices through multiple test stations of multiple test devices; the test parsing module 720 is used to parse and acquire structured test data sequences corresponding to each dimension from the test data file according to at least one set parsing dimension; wherein the set parsing dimension includes at least one of the following: test device, test station, MEMS device, set attribute, test time, and test configuration; the window extraction module 730 is used to extract valid data from the test data sequence using an event isolation window to form a valid data sequence; the test grouping module 740 is used to determine test groups according to the dimensions corresponding to each valid data sequence; the index calculation module 750 is used to calculate the reliability index of the test device according to the valid data sequence of each test group; wherein the reliability index includes: CpK, standard deviation, repeatability, and reproducibility.

[0108] The technical solution of this invention, by acquiring test data files generated by multiple test devices and their multiple test stations for parallel testing of batch MEMS devices, can cover the data requirements of high-capacity test scenarios at once, avoiding data fragmentation. By parsing the file according to at least one set parsing dimension, structured test data sequences corresponding to each dimension can be obtained, thus determining the data correlation between different dimensions. Using an event isolation window to extract valid data from the test data sequences to form valid data sequences can eliminate redundant and interfering data, ensuring that subsequent analysis is based on valid data from real responses, improving the accuracy of MEMS device test data processing. Determining test groups based on the dimensions corresponding to the valid data sequences matches the calculation logic of reliability indicators. Calculating reliability indicators based on the valid data sequences of each test group allows for a comprehensive evaluation of the test system performance from multiple dimensions, including data dispersion, process compliance with specifications, and test system measurement reliability. The automated processing flow improves the efficiency of MEMS device test data processing, ensuring timely detection of problems in the testing process and guaranteeing the test quality of batch MEMS devices.

[0109] Based on the above embodiments, the test parsing module 720 is specifically used for: identifying the test configuration of the test equipment according to the header data and / or file description data of the test data file; determining the corresponding target parsing rule from the parsing rule set according to the test configuration; wherein the target parsing rule includes parsing configurations for test equipment, test site, MEMS device, set attributes, test time and test configuration; and parsing the test data file using the target parsing rule to obtain structured test data sequences corresponding to each dimension.

[0110] Based on the above embodiments, the parsing rule set includes at least one of the following: a header mapping template, column sorting rules, and unit conversion logic of at least two test programs executed by the test equipment.

[0111] Based on the above embodiments, the MEMS device is a multi-axis sensor, the test data is an acceleration value, and the set resolution dimension includes: device serial number, test site identifier, load board or socket board position, and sampling rate.

[0112] Based on the above embodiments, the window extraction module 730 is specifically used for: acquiring the pre-excitation data of the test data in the test data sequence and calculating the corresponding baseline value; setting a dynamic threshold around the baseline value according to the expected MEMS device test data; wherein the dynamic threshold is determined based on the type of the MEMS device and the test environment conditions; sequentially detecting the relationship between each test data and the dynamic threshold from the test data sequence, and when test data reaching the dynamic threshold is detected from the test data sequence, taking the test time corresponding to the test data as the starting point of the event isolation window; performing signal attenuation analysis and energy integration analysis on the test data sequence to determine the end point of the valid data in the test data, which is taken as the end point of the event isolation window; and acquiring the data falling into the event isolation window from the test data sequence as valid data.

[0113] Based on the above embodiments, the test grouping module 740 is specifically used to: group according to the test equipment, test site, and test cycle number in the test configuration corresponding to each valid data sequence, so as to determine multiple test groups.

[0114] Based on the above embodiments, the calculation index module 750 is specifically used for at least one of the following: selecting the test group with the highest consistency of valid data sequences from the valid data sequences of each test group, calculating repeatability and reproducibility as the best-case index; selecting the test group with the lowest consistency of valid data sequences from the valid data sequences of each test group, calculating repeatability and reproducibility as the worst-case index; randomly selecting test groups from the valid data sequences of each test group to form a set number of test group combinations, calculating repeatability and reproducibility as the average-case index.

[0115] Furthermore, based on the above embodiments, the MEMS device test data processing device may further include: a labeling module, wherein: the labeling module is used to calculate the reliability index of the test equipment according to the valid data sequence of each test group, and then label the test equipment that does not meet the requirements according to the reliability index and the set tolerance limit.

[0116] Furthermore, based on the above embodiments, the MEMS device test data processing apparatus may further include: a visualization module and a compilation and export module, wherein: the visualization module is used to calculate the reliability index of the test equipment according to the effective data sequence of each test group, and then generate at least one of the following visualization results based on the reliability index and display them visually: a time-domain plot of MEMS test data, a histogram of peak response across multiple test equipment, a trend graph of CpK value over time, or a heat map overlaid on a load board or socket board layout diagram that associates the test results with the physical test site location; the compilation and export module is used to compile the extracted effective data sequence, the calculated reliability index, and the generated visualization results into a test report conforming to MSA in at least one standardized format and export it; wherein the report is automatically formatted to meet the quality audit requirements of semiconductor manufacturing.

[0117] Furthermore, based on the above embodiments, the MEMS device test data processing apparatus may further include: a mapping module, wherein: the mapping module is used to map the test data to the physical location on the load board or socket board, and generate a heat map of CpK value or failure rate across the test fixture.

[0118] Furthermore, based on the above embodiments, the MEMS device test data processing device may further include: a verification module, wherein: the verification module is used to verify the integrity of the MSA report by comparing it with a checklist exported from a preset standard before exporting the test report.

[0119] The MEMS device test data processing apparatus provided in the embodiments of the present invention can execute the MEMS device test data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0120] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0121] Example 5

[0122] Figure 8A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0123] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0125] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for processing MEMS device test data, namely: acquiring a test data file; wherein the test data file includes test values ​​generated by parallel testing of set attributes of a batch of MEMS devices through multiple test stations of multiple test devices; from the test data file, according to at least one set parsing dimension, respectively parsing and acquiring structured test data sequences corresponding to each dimension; wherein the set parsing dimension includes at least one of the following: test device, test station, MEMS device, set attribute, test time, and test configuration; extracting valid data from the test data sequence using an event isolation window to form a valid data sequence; determining test groups according to the dimensions corresponding to each valid data sequence; calculating the reliability index of the test device according to the valid data sequence of each test group; wherein the reliability index includes: CpK, standard deviation, repeatability, and reproducibility.

[0126] In some embodiments, the method for processing MEMS device test data may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the MEMS device test data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the MEMS device test data processing method by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0132] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0133] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing MEMS device test data, characterized in that, include: Obtain test data files; the test data files contain test values ​​generated by parallel testing of the set attributes of batch microelectromechanical MEMS devices through multiple test stations of multiple test devices. From the test data file, structured test data sequences corresponding to each dimension are obtained by parsing according to at least one set parsing dimension; wherein, the set parsing dimension includes at least one of the following: test equipment, test site, MEMS device, set attribute, test time and test configuration; Valid data is extracted from the test data sequence using an event isolation window to form a valid data sequence. The test groups are determined based on the dimensions corresponding to each valid data sequence; Based on the valid data sequences of each test group, the reliability index of the test equipment is calculated; wherein, the reliability index includes: process capability index CpK, standard deviation, and repeatability and reproducibility.

2. The method according to claim 1, characterized in that, From the test data file, according to at least one set parsing dimension, the structured test data sequence corresponding to each dimension is parsed and obtained, including: Identify the test configuration of the test equipment based on the header data and / or file description data of the test data file; Based on the test configuration, the corresponding target parsing rule is determined from the parsing rule set; wherein, the target parsing rule includes the parsing configuration of test equipment, test site, MEMS device, set attributes, test time and test configuration; The target parsing rules are used to parse the test data file to obtain structured test data sequences corresponding to each dimension.

3. The method according to claim 1, characterized in that, The parsing rule set includes at least one of the following: a header mapping template, column sorting rules, and unit conversion logic of at least two test programs executed by the test equipment.

4. The method according to claim 1, characterized in that, The MEMS device is a multi-axis sensor, the test data is acceleration value, and the set resolution dimensions include: device serial number, test site identifier, load board or socket board position, and sampling rate.

5. The method according to claim 1, characterized in that, The event isolation window is used to extract valid data from the test data sequence to form a valid data sequence, including: Obtain the pre-excitation data of the test data in the test data sequence and calculate the corresponding baseline value; Based on the expected MEMS device test data, a dynamic threshold is set around the baseline value; wherein the dynamic threshold is determined based on the type of MEMS device and the test environment conditions; The relationship between each test data and the dynamic threshold is detected sequentially from the test data sequence. When test data that reaches the dynamic threshold is detected from the test data sequence, the test time corresponding to the test data is taken as the starting point of the event isolation window. Signal attenuation analysis and energy integration analysis are performed on the test data sequence to determine the end point of valid data in the test data, which is used as the end point of the event isolation window; Data falling into the event isolation window from the test data sequence is taken as valid data.

6. The method according to claim 1, characterized in that, Based on the dimensions corresponding to each valid data sequence, the test groups are determined, including: The data sequences are grouped according to the test equipment, test site, and test cycle number in the test configuration corresponding to each valid data sequence to determine multiple test groups.

7. The method according to claim 1, characterized in that, Based on the valid data sequences of each of the test groups, calculate the repeatability and reproducibility indices of the test equipment, including at least one of the following: From the valid data sequences of each test group, select the test group with the highest consistency of valid data sequences, calculate repeatability and reproducibility, and use them as the best-case indicators. From the valid data sequences of each test group, select the test group with the lowest consistency of valid data sequences, calculate repeatability and reproducibility, and use them as worst-case indicators. Test groups are randomly selected from the valid data sequences of each test group to form a set number of test group combinations. Repeatability and reproducibility are calculated as average performance indicators.

8. The method according to claim 1, characterized in that, After calculating the reliability index of the test equipment based on the valid data sequence of each test group, the method further includes: Based on reliability indicators and set tolerance limits, test equipment that does not meet the requirements is marked.

9. The method according to claim 1, characterized in that, After calculating the reliability index of the test equipment based on the valid data sequence of each test group, the method further includes: Based on reliability metrics, generate at least one of the following visualization results and display it: a time-domain plot of MEMS test data, a histogram of peak response across multiple test devices, a trend graph of CpK values ​​over time, or a heat map overlaid on a load board or socket board layout diagram that correlates test results with the location of physical test sites. The extracted valid data sequences, calculated reliability indicators, and generated visualization results are compiled into a test report conforming to Structured Measurement System Analysis (MSA) in at least one standardized format and exported; wherein the report is automatically formatted to meet the quality audit requirements of semiconductor manufacturing.

10. The method according to claim 9, characterized in that, Also includes: The test data is mapped to physical locations on the load board or socket board, and a heatmap of CpK values ​​or failure rates across the test fixture is generated.

11. The method according to claim 9, characterized in that, Before exporting the test report, the following is also included: Verify the completeness of the MSA report by comparing it with a checklist derived from a preset standard.

12. A device for processing MEMS device test data, characterized in that, include: The file acquisition module is used to acquire test data files. The test data files contain test data, which are test values ​​generated by parallel testing of the set attributes of a batch of MEMS devices through multiple test stations of multiple test devices. The test parsing module is used to parse and obtain the structured test data sequence corresponding to each dimension from the test data file according to at least one set parsing dimension; wherein, the set parsing dimension includes at least one of the following: test equipment, test site, MEMS device, set attribute, test time and test configuration; The window extraction module is used to extract valid data from the test data sequence using an event isolation window to form a valid data sequence. The test grouping module is used to determine test groups based on the dimensions corresponding to each valid data sequence; The index calculation module is used to calculate the reliability index of the test equipment based on the valid data sequence of each test group; wherein the reliability index includes: CpK, standard deviation, repeatability and reproducibility.

13. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for processing MEMS device test data according to any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for processing MEMS device test data according to any one of claims 1-11.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a method for processing MEMS device test data according to any one of claims 1-11.

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