Resonator chip testing method and device, electronic equipment and storage medium

Abstract

The application provides a resonator chip test method and device, electronic equipment and storage medium, and relates to the technical field of semiconductor chip testing. The contact resistance value between the probe and the chip electrode is obtained, the current test signal frequency and the cumulative test number of the probe are obtained, the test error is calculated by using a preset dynamic error calibration model according to the contact resistance value, the current test signal frequency and the cumulative test number, the test error is deducted from the current test signal frequency, and the calibrated test result is obtained, so that the effective calibration of the resonator chip test result is realized, and the precision and reliability of high-frequency test in the automatic probe test system are improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor chip testing technology, and more specifically, to a method, apparatus, electronic device, and storage medium for testing resonator chips. Background Technology

[0002] In resonator chip manufacturing, to improve the performance and applicability of the resonator chip, the P electrode and N electrode are placed on the same side surface, such as... Figure 4 As shown, the testing of this resonator chip can be directly performed using probe point testing, meeting the needs of large-scale production. Therefore, automated probe stations are widely used for rapid testing of its high-frequency performance. However, in high-frequency testing environments, the contact resistance between the probe and the chip electrodes has a significant impact on the accuracy of the test results. During long-term and high-intensity automated testing, probe tip wear is inevitable. Probe wear leads to a reduction in the actual contact area between the probe and the chip electrodes, resulting in uneven contact pressure distribution and increased contact resistance. This increased contact resistance severely interferes with the transmission of test signals, reducing the accuracy of the test results. Therefore, it is crucial to reduce the impact of increased contact resistance caused by probe wear and other factors on the test results to ensure the accuracy and long-term reliability of high-frequency test results.

[0003] There is currently no effective technical solution to the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a resonator chip testing method and related equipment to minimize the impact of increased contact resistance caused by probe wear and other factors on the test results, and to ensure the accuracy and long-term reliability of high-frequency test results.

[0005] This application provides a resonator chip testing method for an automated probe testing system, including the following steps: S1. Obtain the contact resistance value between the probe and the chip electrode, and obtain the current test signal frequency and the cumulative number of tests of the probe; S2. Calculate the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency, and the cumulative number of tests; S3. Subtract the test error from the current test signal frequency to obtain the calibrated test result.

[0006] The above settings enable effective calibration of the resonator chip test results, thereby improving the accuracy and reliability of high-frequency testing in the automated probe testing system.

[0007] Optionally, step S2 includes: Obtain the preset dynamic error calibration model, which includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; The contact resistance value is input into the contact resistance calibration sub-model to obtain the first error data; The current test signal frequency is input into the frequency calibration sub-model to obtain the second error data; The cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data; The test error is calculated based on the first error data, the second error data, and the third error data.

[0008] By setting up the above, each sub-model is calibrated for a specific source of error, thereby making the overall error calibration more refined.

[0009] Optionally, the step of inputting the cumulative number of tests into the probe test count calibration sub-model to obtain the third error data includes: Obtain the pressure parameters of the probe, and adjust the cumulative number of tests according to the pressure parameters to obtain the adjusted cumulative number of tests; The adjusted cumulative number of tests is input into the probe test number calibration sub-model to obtain the third error data.

[0010] Optionally, the step of obtaining the pressure parameter of the probe, adjusting the cumulative number of tests based on the pressure parameter, and obtaining the adjusted cumulative number of tests includes: Obtain a preset pressure threshold and determine whether the pressure parameter of the probe is less than the preset pressure threshold; If the pressure parameter is less than the preset pressure threshold, the pressure of the probe is determined to be abnormal, and a pressure adjustment prompt message is output. If the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold to obtain the adjusted cumulative number of tests.

[0011] Optionally, the step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain the first error data includes: Obtain the temperature parameters of the test environment and determine whether the temperature parameters exceed the preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality warning message will be output. If the temperature parameter does not exceed the preset temperature range, the contact resistance value is corrected according to the temperature parameter to obtain the corrected contact resistance value. The corrected contact resistance value is then input into the contact resistance calibration sub-model to obtain the first error data.

[0012] By adding temperature parameter consideration and correction steps before inputting the contact resistance calibration sub-model, the error caused by temperature fluctuations in the test environment is reduced, ensuring the quality of input data for subsequent test error calculation and thus improving the accuracy of test results.

[0013] Optionally, the step of inputting the current test signal frequency into the frequency calibration sub-model to obtain the second error data includes: The frequency drift data of the test signal source is acquired in real time, and the current test signal frequency is corrected according to the frequency drift data to obtain the corrected current test signal frequency; The corrected current test signal frequency is input into the frequency calibration sub-model to obtain the second error data.

[0014] Optionally, the step of acquiring the frequency drift data of the test signal source in real time, correcting the current test signal frequency based on the frequency drift data, and obtaining the corrected current test signal frequency includes: Determine whether the frequency drift in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure message will be output. If the frequency drift does not exceed the frequency drift threshold, then the frequency correction value is calculated based on the frequency drift and the frequency correction step size, and the current test signal frequency is corrected based on the frequency correction value to obtain the corrected test signal frequency.

[0015] Secondly, this application provides a resonator chip testing apparatus for an automated probe testing system, comprising: The acquisition module is used to acquire the contact resistance value between the probe and the chip electrode, acquire the current test signal frequency and the cumulative number of tests of the probe; The calculation module is used to calculate the test error based on the contact resistance value, the current test signal frequency, and the cumulative number of tests using a preset dynamic error calibration model. The calibration module is used to subtract the test error from the current test signal frequency to obtain the calibrated test result.

[0016] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of a resonator chip testing method as described in any of the preceding claims are performed.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of a resonator chip testing method as described in any of the preceding claims.

[0018] As can be seen from the above, the resonator chip testing method provided in this application obtains the contact resistance value between the probe and the chip electrode, the current test signal frequency and the cumulative number of tests of the probe, calculates the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency and the cumulative number of tests, subtracts the test error from the current test signal frequency, and obtains the calibrated test result. This achieves effective calibration of the resonator chip test result, thereby improving the accuracy and reliability of high-frequency testing in the automated probe testing system.

[0019] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0020] Figure 1 This is a flowchart of a resonator chip testing method provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of the resonator chip testing device provided in an embodiment of this application.

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of the structure of a resonator chip in the prior art.

[0024] Labeling explanations: 21. Acquisition module; 22. Calculation module; 23. Calibration module; 3. Electronic equipment; 301. Processor; 302. Memory; 303. Communication bus. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Please refer to Figures 1-3 This application provides a resonator chip testing method, apparatus, electronic device, and storage medium to minimize the impact of increased contact resistance caused by probe wear on test results, thereby ensuring the accuracy and long-term reliability of high-frequency test results.

[0028] This application provides a method for testing resonator chips, including the following steps: S1. Obtain the contact resistance value between the probe and the chip electrode, and obtain the current test signal frequency and the cumulative number of tests of the probe; S2. Calculate the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency, and the cumulative number of tests; S3. Subtract the test error from the current test signal frequency to obtain the calibrated test result.

[0029] Specifically, this invention provides a resonator chip testing method to address the technical problem of contact resistance affecting test accuracy in automated probe testing systems. Firstly, higher contact resistance leads to greater test error; higher frequency test signals have a more significant impact on test error due to increased contact resistance. As the cumulative number of probe tests increases, probe wear intensifies, and the impact of contact resistance on test error dynamically changes. Therefore, the contact resistance between the probe and chip electrodes, the current test signal frequency, and the cumulative number of probe tests are all key factors affecting test error. Next, based on the acquired contact resistance value, current test signal frequency, and cumulative number of tests, a preset dynamic error calibration model is used to calculate the test error. Since this dynamic error calibration model comprehensively considers the impact of the aforementioned key factors on the test results, it more accurately assesses the test error. Finally, the calculated test error is subtracted from the current test signal frequency to obtain the calibrated test result. By subtracting the error, the impact of contact resistance on the test results can be effectively reduced, improving the accuracy of the test results. Therefore, this application achieves effective calibration of resonator chip test results by acquiring parameters affecting test error in real time and using a dynamic error calibration model for error compensation, thereby improving the accuracy and reliability of high-frequency testing in automated probe testing systems.

[0030] In step S3, the test error (5 MHz) is subtracted from the current test signal frequency (100MHz). The calculation process is: 100MHz-5 MHz=95MHz. Thus, the calibrated test result is 95MHz.

[0031] In some implementations, step S2 includes: Obtain the preset dynamic error calibration model, which includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; The contact resistance value is input into the contact resistance calibration sub-model to obtain the first error data; The current test signal frequency is input into the frequency calibration sub-model to obtain the second error data; The cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data; The test error is calculated based on the first error data, the second error data, and the third error data.

[0032] Specifically, this embodiment aims to refine the dynamic error calibration model to calculate test errors in a more accurate and operable manner. For the three key factors affecting test error—contact resistance value, current test signal frequency, and cumulative test count—corresponding sub-models are used for processing. The contact resistance value is input into the contact resistance calibration sub-model to quantify the impact of contact resistance on test error, obtaining the first error data. The current test signal frequency is input into the frequency calibration sub-model to quantify the impact of the signal frequency itself on test error, obtaining the second error data. The cumulative test count is input into the probe test count calibration sub-model to quantify the impact of factors such as probe wear on test error as the number of tests increases, obtaining the third error data. Then, the first, second, and third error data output from these three sub-models are combined for calculation to obtain the final test error. Because each sub-model is calibrated for a specific error source, the overall error calibration is more refined.

[0033] The contact resistance calibration sub-model establishes a mapping relationship between resistance value and error by experimentally measuring the actual error under different contact resistance values. For example, a polynomial regression fitting formula can be used: ; In the formula, This is the first error data. This is the contact resistance value. The first preset coefficient, This is the second preset coefficient. This is the third preset coefficient; The frequency calibration sub-model constructs a frequency-error lookup table or a piecewise linear model by comparing theoretical values ​​with actual measured values ​​at a standard frequency. For example: ; In the formula, This is the second error data. As the reference frequency, The current test signal frequency, This is the drift coefficient.

[0034] Among them, the probe test number calibration sub-model establishes a mapping relationship between the number of tests and the corresponding error by recording the wear state of the probe under different test numbers and the corresponding error over a long period of time. For example, an exponential decay model is used: ; In the formula, This is the third error data. The first fitting parameter (the limit error after the probe is completely worn out, which can be calibrated experimentally). The second fitting parameter (probe wear rate, which can be calibrated experimentally) is used. The number of tests.

[0035] The test error is obtained through weighted calculation, and the weighting formula is as follows: ; In the formula, For testing error, As the first preset weight, As the second preset weight, This is the third preset weight.

[0036] In some implementations, the step of inputting the cumulative number of tests into the probe test count calibration sub-model to obtain the third error data includes: Obtain the pressure parameters of the probe, adjust the cumulative number of tests based on the pressure parameters, and obtain the adjusted cumulative number of tests; The adjusted cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data.

[0037] Specifically, this approach takes into account the influence of pressure parameters on probe wear, enabling the probe test count calibration sub-model to more accurately reflect the actual wear of the probe, thereby improving the accuracy of test error calibration. Due to the introduction of pressure parameters, the cumulative test count is no longer a simple count, but a correction value related to the actual working state of the probe. This makes the obtained third error data more reliable, ultimately improving the accuracy of the test results.

[0038] In some implementations, the steps of obtaining the probe's pressure parameters, adjusting the cumulative number of tests based on the pressure parameters, and obtaining the adjusted cumulative number of tests include: Obtain a preset pressure threshold and determine whether the probe's pressure parameter is less than the preset pressure threshold. If the pressure parameter is less than the preset pressure threshold, the probe pressure is determined to be abnormal, and a pressure adjustment prompt message is output. If the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold, and the adjusted cumulative number of tests is obtained.

[0039] Specifically, when the pressure parameter is less than the preset pressure threshold (set according to actual needs, no specific limit is set here), it indicates that the probe pressure is too low, which may indicate abnormalities such as poor contact. In this case, the probe pressure is determined to be abnormal, and a pressure adjustment prompt is output to remind the operator that the probe pressure is abnormal and needs adjustment to avoid inaccurate calculations of subsequent test errors. When the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative test count is adjusted according to the ratio of the pressure parameter to the preset pressure threshold. (For example, if the current probe pressure parameter is 12 gf (grams), and the system's preset pressure threshold is 10 gf, since 12 gf is greater than or equal to 10 gf, the system determines that the probe pressure is normal. At this time, the system obtains the original cumulative test count for the probe, for example, 10,000 times. Based on the ratio of the pressure parameter to the preset pressure threshold (12 gf / 10 gf = 1.2), the system calculates the adjusted cumulative test count: 10,000 times * 1.2 = ... (12,000 times) to obtain the adjusted cumulative number of tests, ensuring that under normal probe pressure, the cumulative number of tests can be corrected according to the pressure parameters, thereby improving the accuracy of subsequent test error calculations.

[0040] In some implementations, the step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain the first error data includes: Obtain the temperature parameters of the test environment and determine whether the temperature parameters exceed the preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality warning message will be output. If the temperature parameter does not exceed the preset temperature range, the contact resistance value is corrected according to the temperature parameter to obtain the corrected contact resistance value. The corrected contact resistance value is then input into the contact resistance calibration sub-model to obtain the first error data.

[0041] Specifically, to address the impact of temperature variations in the testing environment on the accuracy of contact resistance values, this solution first acquires the temperature parameters of the testing environment and determines whether these parameters exceed a preset normal temperature range. If the temperature exceeds the preset normal range, the system outputs a temperature anomaly warning message, alerting the operator that there may be a problem with the current testing environment. If the temperature is within the normal range, the contact resistance value is corrected based on the acquired temperature parameters (for example, the preset temperature range is set to 20℃ to 25℃. When the sensor acquires a temperature of 26℃, the system determines that this temperature exceeds the preset range and displays a "Test environment temperature abnormal, please check" message on the user interface. If the sensor acquires a temperature of 23℃, which is within the preset range, the system corrects the contact resistance value according to a preset temperature correction formula, for example...). In the formula, This is the corrected contact resistance value. This is the current contact resistance value. The current temperature. For preset reference temperature, A temperature coefficient is preset to obtain a contact resistance value that takes temperature into account. This corrected contact resistance value is then input into the contact resistance calibration sub-model to calculate the first error data. By adding a temperature parameter consideration and correction step before inputting the data into the contact resistance calibration sub-model, errors caused by temperature fluctuations in the test environment are reduced, ensuring the quality of the input data for subsequent test error calculations and thus improving the accuracy of the test results.

[0042] In some implementations, the step of inputting the current test signal frequency into the frequency calibration sub-model to obtain the second error data includes: The frequency drift data of the test signal source is acquired in real time, and the current test signal frequency is corrected based on the frequency drift data to obtain the corrected current test signal frequency. The corrected frequency of the current test signal is input into the frequency calibration sub-model to obtain the second error data.

[0043] Specifically, to address the issue of inaccurate test results caused by frequency drift of the test signal source, this solution adds a step to acquire frequency drift data in real time. The frequency drift data is then used to correct the current test signal frequency, resulting in the corrected current test signal frequency, thus ensuring the accuracy of the current test signal frequency input to the frequency calibration sub-model.

[0044] In some implementations, the steps of acquiring frequency drift data of the test signal source in real time, correcting the current test signal frequency based on the frequency drift data, and obtaining the corrected current test signal frequency include: Determine whether the frequency drift in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure message will be output. If the frequency drift does not exceed the frequency drift threshold, the frequency correction value is calculated based on the frequency drift and the frequency correction step size. The current test signal frequency is then corrected based on the frequency correction value to obtain the corrected test signal frequency.

[0045] Specifically, if the frequency drift exceeds the threshold, frequency calibration is deemed a failure and a warning message is given. The advantage of this approach is that excessive frequency drift indicates a potential anomaly in the test signal source. Performing frequency calibration in this case might introduce even greater errors, reducing the accuracy of the test results. Frequency calibration is only performed when the frequency drift is within an acceptable range (not exceeding the frequency drift threshold), and a frequency correction step size (set according to actual needs, no specific limit is specified here) is introduced. Frequency correction is performed in steps (e.g., if the drift is +3 MHz (the measured frequency is 3 Hz higher than expected), and the correction step size is 1 MHz / step (the minimum frequency change per adjustment is 1 MHz), then the calculated frequency correction value is -3 MHz (reverse compensation for drift is required). Based on the calculated frequency correction value, the current test signal frequency is corrected. For example, the current test signal frequency (e.g., 100 MHz) is added to the frequency correction value (e.g., the frequency calibration value above -3 MHz, i.e., adding -3 MHz). (Hz), to obtain the corrected test signal frequency (e.g., 97MHz)), ensuring the stability and accuracy of the frequency correction process.

[0046] In some implementations, the step of calculating the test error based on the first error data, the second error data, and the third error data includes: Obtain a preset weight allocation scheme, which includes the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data; The test error is obtained by calculating a weighted average of the first error data, the second error data, and the third error data based on the first weight, the second weight, and the third weight.

[0047] This scheme obtains a preset weighting scheme, which defines a first weight for the first error data, a second weight for the second error data, and a third weight for the third error data. Then, based on the weight values ​​defined in the weighting scheme, a weighted average is calculated for the first, second, and third error data to obtain the test error. Through weighting and weighted averaging, the scheme fully considers the influence of different error data on the test results when calculating the test error, thus making the calculated test error more accurately reflect the actual error situation.

[0048] In some implementations, the step of obtaining a preset weight allocation scheme, which includes a first weight for the first error data, a second weight for the second error data, and a third weight for the third error data, includes: Obtain probe type parameters, and select a target weight allocation scheme from multiple preset weight allocation schemes based on probe type parameters. The target weight allocation scheme includes the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data.

[0049] Since the weighting scheme is preset, meaning the weights of each error data point are determined before testing, different probe types may have different characteristics. For example, different types of probes may differ in contact resistance stability, frequency response characteristics, and wear rates. If a uniform preset weighting scheme is used, the error impact of different probe types may not be fully considered, resulting in low accuracy of error calibration. To address this issue, this application proposes selecting a weighting scheme based on probe type parameters. Specifically, multiple weighting schemes are preset, each corresponding to one or more probe types. During testing, the type parameters of the currently used probe are first obtained. Then, based on these parameters, a target weighting scheme matching the current probe type is selected from the preset schemes. The target weighting scheme defines the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data, which are used for subsequent weighted average calculations to obtain the test error.

[0050] Secondly, this application provides a resonator chip testing apparatus for an automated probe testing system, comprising: The acquisition module 21 is used to acquire the contact resistance value between the probe and the chip electrode, the current test signal frequency, and the cumulative number of tests of the probe; The calculation module 22 is used to calculate the test error based on the contact resistance value, the current test signal frequency, and the cumulative number of tests using a preset dynamic error calibration model. The calibration module 23 is used to subtract the test error from the current test signal frequency to obtain the calibrated test result.

[0051] Specifically, this invention provides a resonator chip testing method to address the technical problem of contact resistance affecting test accuracy in automated probe testing systems. Firstly, higher contact resistance leads to greater test error; higher frequency test signals have a more significant impact on test error due to increased contact resistance. As the cumulative number of probe tests increases, probe wear intensifies, and the impact of contact resistance on test error dynamically changes. Therefore, the contact resistance between the probe and chip electrodes, the current test signal frequency, and the cumulative number of probe tests are all key factors affecting test error. Next, based on the acquired contact resistance value, current test signal frequency, and cumulative number of tests, a preset dynamic error calibration model is used to calculate the test error. Since this dynamic error calibration model comprehensively considers the impact of the aforementioned key factors on the test results, it more accurately assesses the test error. Finally, the calculated test error is subtracted from the current test signal frequency to obtain the calibrated test result. By subtracting the error, the impact of contact resistance on the test results can be effectively reduced, improving the accuracy of the test results. Therefore, this application achieves effective calibration of resonator chip test results by acquiring parameters affecting test error in real time and using a dynamic error calibration model for error compensation, thereby improving the accuracy and reliability of high-frequency testing in automated probe testing systems.

[0052] When calibration module 23 performs the operation of subtracting the test error from the current test signal frequency to obtain the calibrated test result, it specifically performs the operation of subtracting the test error (5 MHz) from the current test signal frequency (100MHz). The calculation process is: 100MHz-5 MHz=95MHz. Therefore, the calibrated test result is 95MHz.

[0053] In some implementations, when the calculation module 22 calculates the test error based on the contact resistance value, the current test signal frequency, and the cumulative number of tests using a preset dynamic error calibration model, it specifically performs the following: Obtain the preset dynamic error calibration model, which includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; The contact resistance value is input into the contact resistance calibration sub-model to obtain the first error data; The current test signal frequency is input into the frequency calibration sub-model to obtain the second error data; The cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data; The test error is calculated based on the first error data, the second error data, and the third error data.

[0054] Specifically, this embodiment aims to refine the dynamic error calibration model to calculate test errors in a more accurate and operable manner. For the three key factors affecting test error—contact resistance value, current test signal frequency, and cumulative test count—corresponding sub-models are used for processing. The contact resistance value is input into the contact resistance calibration sub-model to quantify the impact of contact resistance on test error, obtaining the first error data. The current test signal frequency is input into the frequency calibration sub-model to quantify the impact of the signal frequency itself on test error, obtaining the second error data. The cumulative test count is input into the probe test count calibration sub-model to quantify the impact of factors such as probe wear on test error as the number of tests increases, obtaining the third error data. Then, the first, second, and third error data output from these three sub-models are combined for calculation to obtain the final test error. Because each sub-model is calibrated for a specific error source, the overall error calibration is more refined.

[0055] The contact resistance calibration sub-model establishes a mapping relationship between resistance value and error by experimentally measuring the actual error under different contact resistance values. For example, a polynomial regression fitting formula can be used: ; In the formula, This is the first error data. This is the contact resistance value. The first preset coefficient, This is the second preset coefficient. This is the third preset coefficient; The frequency calibration sub-model constructs a frequency-error lookup table or a piecewise linear model by comparing theoretical values ​​with actual measured values ​​at a standard frequency. For example: ; In the formula, This is the second error data. As the reference frequency, The current test signal frequency, This is the drift coefficient.

[0056] Among them, the probe test number calibration sub-model establishes a mapping relationship between the number of tests and the corresponding error by recording the wear state of the probe under different test numbers and the corresponding error over a long period of time. For example, an exponential decay model is used: ; In the formula, This is the third error data. The first fitting parameter (the limit error after the probe is completely worn out, which can be calibrated experimentally). The second fitting parameter (probe wear rate, which can be calibrated experimentally) is used. The number of tests.

[0057] The test error is obtained through weighted calculation, and the weighting formula is as follows: ; In the formula, For testing error, As the first preset weight, As the second preset weight, This is the third preset weight.

[0058] In some implementations, when the calculation module 22 inputs the cumulative number of tests into the probe test count calibration sub-model to obtain the third error data, it also performs the following: Obtain the pressure parameters of the probe, adjust the cumulative number of tests based on the pressure parameters, and obtain the adjusted cumulative number of tests; The adjusted cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data.

[0059] Specifically, this approach takes into account the influence of pressure parameters on probe wear, enabling the probe test count calibration sub-model to more accurately reflect the actual wear of the probe, thereby improving the accuracy of test error calibration. Due to the introduction of pressure parameters, the cumulative test count is no longer a simple count, but a correction value related to the actual working state of the probe. This makes the obtained third error data more reliable, ultimately improving the accuracy of the test results.

[0060] In some implementations, when the calculation module 22 acquires the probe's pressure parameters, adjusts the cumulative test count based on the pressure parameters, and obtains the adjusted cumulative test count, it also performs the following: Obtain a preset pressure threshold and determine whether the probe's pressure parameter is less than the preset pressure threshold. If the pressure parameter is less than the preset pressure threshold, the probe pressure is determined to be abnormal, and a pressure adjustment prompt message is output. If the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold, and the adjusted cumulative number of tests is obtained.

[0061] Specifically, when the pressure parameter is less than the preset pressure threshold (set according to actual needs, no specific limit is set here), it indicates that the probe pressure is too low, which may indicate abnormalities such as poor contact. In this case, the probe pressure is determined to be abnormal, and a pressure adjustment prompt is output to remind the operator that the probe pressure is abnormal and needs adjustment to avoid inaccurate calculations of subsequent test errors. When the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative test count is adjusted according to the ratio of the pressure parameter to the preset pressure threshold. (For example, if the current probe pressure parameter is 12 gf (grams), and the system's preset pressure threshold is 10 gf, since 12 gf is greater than or equal to 10 gf, the system determines that the probe pressure is normal. At this time, the system obtains the original cumulative test count for the probe, for example, 10,000 times. Based on the ratio of the pressure parameter to the preset pressure threshold (12 gf / 10 gf = 1.2), the system calculates the adjusted cumulative test count: 10,000 times * 1.2 = ... (12,000 times) to obtain the adjusted cumulative number of tests, ensuring that under normal probe pressure, the cumulative number of tests can be corrected according to the pressure parameters, thereby improving the accuracy of subsequent test error calculations.

[0062] In some implementations, when the calculation module 22 inputs the contact resistance value into the contact resistance calibration sub-model to obtain the first error data, it also performs the following: Obtain the temperature parameters of the test environment and determine whether the temperature parameters exceed the preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality warning message will be output. If the temperature parameter does not exceed the preset temperature range, the contact resistance value is corrected according to the temperature parameter to obtain the corrected contact resistance value. The corrected contact resistance value is then input into the contact resistance calibration sub-model to obtain the first error data.

[0063] Specifically, to address the impact of temperature variations in the testing environment on the accuracy of contact resistance values, this solution first acquires the temperature parameters of the testing environment and determines whether these parameters exceed a preset normal temperature range. If the temperature exceeds the preset normal range, the system outputs a temperature anomaly warning message, alerting the operator that there may be a problem with the current testing environment. If the temperature is within the normal range, the contact resistance value is corrected based on the acquired temperature parameters (for example, the preset temperature range is set to 20℃ to 25℃. When the sensor acquires a temperature of 26℃, the system determines that this temperature exceeds the preset range and displays a "Test environment temperature abnormal, please check" message on the user interface. If the sensor acquires a temperature of 23℃, which is within the preset range, the system corrects the contact resistance value according to a preset temperature correction formula, for example...). In the formula, This is the corrected contact resistance value. This is the current contact resistance value. The current temperature. For preset reference temperature, A temperature coefficient is preset to obtain a contact resistance value that takes temperature into account. This corrected contact resistance value is then input into the contact resistance calibration sub-model to calculate the first error data. By adding a temperature parameter consideration and correction step before inputting the data into the contact resistance calibration sub-model, errors caused by temperature fluctuations in the test environment are reduced, ensuring the quality of the input data for subsequent test error calculations and thus improving the accuracy of the test results.

[0064] In some implementations, when the calculation module 22 inputs the current test signal frequency into the frequency calibration sub-model to obtain the second error data, it also performs the following: The frequency drift data of the test signal source is acquired in real time, and the current test signal frequency is corrected based on the frequency drift data to obtain the corrected current test signal frequency. The corrected frequency of the current test signal is input into the frequency calibration sub-model to obtain the second error data.

[0065] Specifically, to address the issue of inaccurate test results caused by frequency drift of the test signal source, this solution adds a step to acquire frequency drift data in real time. The frequency drift data is then used to correct the current test signal frequency, resulting in the corrected current test signal frequency, thus ensuring the accuracy of the current test signal frequency input to the frequency calibration sub-model.

[0066] In some implementations, when the calculation module 22 acquires the frequency drift data of the test signal source in real time, corrects the current test signal frequency based on the frequency drift data, and obtains the corrected current test signal frequency, it also performs the following: Determine whether the frequency drift in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure message will be output. If the frequency drift does not exceed the frequency drift threshold, the frequency correction value is calculated based on the frequency drift and the frequency correction step size. The current test signal frequency is then corrected based on the frequency correction value to obtain the corrected test signal frequency.

[0067] Specifically, if the frequency drift exceeds the threshold, frequency calibration is deemed a failure and a warning message is given. The advantage of this approach is that excessive frequency drift indicates a potential anomaly in the test signal source. Performing frequency calibration in this case might introduce even greater errors, reducing the accuracy of the test results. Frequency calibration is only performed when the frequency drift is within an acceptable range (not exceeding the frequency drift threshold), and a frequency correction step size (set according to actual needs, no specific limit is specified here) is introduced for step-by-step frequency correction. (For example, if the drift is +3MHz (the measured frequency is 3 Hz higher than expected), and the correction step size is 1MHz / step (the minimum frequency change per adjustment is 1MHz), the calculated frequency correction value is -3MHz (reverse compensation for drift is required). Based on the calculated frequency correction value, the current test signal frequency is corrected. For example, the current test signal frequency (e.g., 100MHz) is added to the frequency correction value (e.g., the frequency calibration value above -3MHz, i.e., adding -3MHz). (Hz), to obtain the corrected test signal frequency (e.g., 97MHz)), ensuring the stability and accuracy of the frequency correction process.

[0068] In some implementations, when the calculation module 22 calculates the test error based on the first error data, the second error data, and the third error data, it also performs the following: Obtain a preset weight allocation scheme, which includes the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data; The test error is obtained by calculating a weighted average of the first error data, the second error data, and the third error data based on the first weight, the second weight, and the third weight.

[0069] This scheme obtains a preset weighting scheme, which defines a first weight for the first error data, a second weight for the second error data, and a third weight for the third error data. Then, based on the weight values ​​defined in the weighting scheme, a weighted average is calculated for the first, second, and third error data to obtain the test error. Through weighting and weighted averaging, the scheme fully considers the influence of different error data on the test results when calculating the test error, thus making the calculated test error more accurately reflect the actual error situation.

[0070] In some implementations, when the calculation module 22 obtains a preset weight allocation scheme, which includes a first weight for the first error data, a second weight for the second error data, and a third weight for the third error data, it also performs the following: Obtain probe type parameters, and select a target weight allocation scheme from multiple preset weight allocation schemes based on probe type parameters. The target weight allocation scheme includes the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data.

[0071] Since the weighting scheme is preset, meaning the weights of each error data point are determined before testing, different probe types may have different characteristics. For example, different types of probes may differ in contact resistance stability, frequency response characteristics, and wear rates. If a uniform preset weighting scheme is used, the error impact of different probe types may not be fully considered, resulting in low accuracy of error calibration. To address this issue, this application proposes selecting a weighting scheme based on probe type parameters. Specifically, multiple weighting schemes are preset, each corresponding to one or more probe types. During testing, the type parameters of the currently used probe are first obtained. Then, based on these parameters, a target weighting scheme matching the current probe type is selected from the preset schemes. The target weighting scheme defines the first weight of the first error data, the second weight of the second error data, and the third weight of the third error data, which are used for subsequent weighted average calculations to obtain the test error.

[0072] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 3 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other via a communication bus 303 and / or other connection mechanisms (not shown). The memory 302 stores computer-readable instructions executable by the processor 301. When the electronic device is running, the processor 301 executes these computer-readable instructions to perform the method in any optional implementation of the above embodiments, thereby achieving the following functions: obtaining the contact resistance value between the probe and the chip electrode; obtaining the current test signal frequency and the cumulative number of tests of the probe; calculating the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency, and the cumulative number of tests; and subtracting the test error from the current test signal frequency to obtain the calibrated test result.

[0073] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments to achieve the following functions: obtaining the contact resistance value between the probe and the chip electrode; obtaining the current test signal frequency and the cumulative number of tests of the probe; calculating the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency, and the cumulative number of tests; and subtracting the test error from the current test signal frequency to obtain the calibrated test result. The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0074] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0077] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0078] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing resonator chips, used in an automated probe testing system, characterized in that, Including the following steps: S1. Obtain the contact resistance value between the probe and the chip electrode, and obtain the current test signal frequency and the cumulative number of tests of the probe; S2. Calculate the test error using a preset dynamic error calibration model based on the contact resistance value, the current test signal frequency, and the cumulative number of tests; S3. Subtract the test error from the current test signal frequency to obtain the calibrated test result; Step S2 includes: Obtain the preset dynamic error calibration model, which includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; The contact resistance value is input into the contact resistance calibration sub-model to obtain the first error data; The current test signal frequency is input into the frequency calibration sub-model to obtain the second error data; The cumulative number of tests is input into the probe test count calibration sub-model to obtain the third error data; The test error is calculated based on the first error data, the second error data, and the third error data; The step of inputting the cumulative number of tests into the probe test number calibration sub-model to obtain the third error data includes: Obtain the pressure parameters of the probe, and adjust the cumulative number of tests according to the pressure parameters to obtain the adjusted cumulative number of tests; The adjusted cumulative number of tests is input into the probe test number calibration sub-model to obtain the third error data; The step of obtaining the pressure parameter of the probe and adjusting the cumulative number of tests based on the pressure parameter to obtain the adjusted cumulative number of tests includes: Obtain a preset pressure threshold and determine whether the pressure parameter of the probe is less than the preset pressure threshold; If the pressure parameter is less than the preset pressure threshold, the pressure of the probe is determined to be abnormal, and a pressure adjustment prompt message is output. If the pressure parameter is greater than or equal to the preset pressure threshold, the cumulative number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold to obtain the adjusted cumulative number of tests. The step of inputting the current test signal frequency into the frequency calibration sub-model to obtain the second error data includes: The frequency drift data of the test signal source is acquired in real time, and the current test signal frequency is corrected according to the frequency drift data to obtain the corrected current test signal frequency; The corrected frequency of the current test signal is input into the frequency calibration sub-model to obtain the second error data; The step of acquiring frequency drift data of the test signal source in real time, correcting the current test signal frequency based on the frequency drift data, and obtaining the corrected current test signal frequency includes: Determine whether the frequency drift in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure message will be output. If the frequency drift does not exceed the frequency drift threshold, then the frequency correction value is calculated based on the frequency drift and the frequency correction step size, and the current test signal frequency is corrected based on the frequency correction value to obtain the corrected test signal frequency.

2. The resonator chip testing method according to claim 1, characterized in that, The step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain the first error data includes: Obtain the temperature parameters of the test environment and determine whether the temperature parameters exceed the preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality warning message will be output. If the temperature parameter does not exceed the preset temperature range, the contact resistance value is corrected according to the temperature parameter to obtain the corrected contact resistance value. The corrected contact resistance value is then input into the contact resistance calibration sub-model to obtain the first error data.

3. A resonator chip testing apparatus employing the resonator chip testing method as described in any one of claims 1-2, used in an automated probe testing system, characterized in that, include: The acquisition module is used to acquire the contact resistance value between the probe and the chip electrode, acquire the current test signal frequency and the cumulative number of tests of the probe; The calculation module is used to calculate the test error based on the contact resistance value, the current test signal frequency, and the cumulative number of tests using a preset dynamic error calibration model. The calibration module is used to subtract the test error from the current test signal frequency to obtain the calibrated test result.

4. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps of a resonator chip testing method as described in any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the steps in the resonator chip testing method as described in any one of claims 1-2.

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