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

Abstract

The invention provides a resonator chip testing method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductor chip testing. The method comprises the steps of obtaining a contact resistance value between a probe and a chip electrode, obtaining a current test signal frequency and an accumulated test frequency of the probe, calculating a test error by using a preset dynamic error calibration model according to the contact resistance value, the current test signal frequency and the accumulated test frequency, and deducting the test error from the current test signal frequency. According to the invention, effective calibration of the test result of the resonator chip is realized, so that the precision and reliability of high-frequency test in an automatic probe test system are improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor chip testing technology, and in particular to a resonator chip testing method, device, electronic device and storage medium. Background Art

[0002] In the manufacture of resonator chips, in order to improve the performance and applicability of the resonator chip, the P electrode and the N electrode are set on the same side surface, such as Figure 4 As shown in the figure, the test of this resonator chip can be directly carried out by probe point testing, which can meet 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 test environments, the contact resistance between the probe and the chip electrode 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 will lead to a reduction in the actual contact area between the probe and the chip electrode, uneven distribution of contact pressure, and thus increase contact resistance. The increase in contact resistance will seriously interfere with the transmission of the test signal and reduce the accuracy of the test results. Therefore, how 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 test results, thereby ensuring the accuracy and long-term reliability of high-frequency test results.

[0005] The present application provides a resonator chip testing method for an automated probe testing system, comprising the steps of: S1. Obtain the contact resistance value between the probe and the chip electrode, 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. Deduct the test error from the current test signal frequency to obtain a calibrated test result.

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

[0007] Optionally, step S2 includes: Obtaining the preset dynamic error calibration model, wherein the dynamic error calibration model includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; Inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data; Inputting the current test signal frequency into the frequency calibration sub-model to obtain second error data; Inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data; The test error is obtained by calculation according to the first error data, the second error data and the third error data.

[0008] Through the above settings, each sub-model is calibrated for a specific error source, making the overall error calibration more refined.

[0009] Optionally, the step of inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data includes: Acquiring a pressure parameter of the probe, and adjusting the cumulative number of tests according to the pressure parameter to obtain the adjusted cumulative number of tests; The adjusted cumulative test times are input into the probe test times calibration sub-model to obtain third error data.

[0010] Optionally, the step of obtaining the pressure parameter of the probe, adjusting the cumulative number of tests according to the pressure parameter, and obtaining the adjusted cumulative number of tests includes: Obtaining a preset pressure threshold, and determining 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, it is determined that the pressure of the probe is abnormal, and a pressure adjustment prompt message is output; If the pressure parameter is greater than or equal to the preset pressure threshold, the accumulated number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold to obtain the adjusted accumulated number of tests.

[0011] Optionally, the step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data includes: Obtaining temperature parameters of the test environment, and determining whether the temperature parameters exceed a preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality prompt message is 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, and the corrected contact resistance value is input into the contact resistance calibration sub-model to obtain first error data.

[0012] By adding temperature parameter considerations 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 calculations, thereby improving the accuracy of the test results.

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

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

[0015] In a second aspect, the present application provides a resonator chip testing device for an automated probe testing system, comprising: An acquisition module is used to obtain 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; a calculation module, configured to calculate a test error using a preset dynamic error calibration model according to the contact resistance value, the current test signal frequency, and the accumulated number of tests; The calibration module is used to deduct the test error from the current test signal frequency to obtain a calibrated test result.

[0016] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in a resonator chip testing method as described in any one of the foregoing items are executed.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in a resonator chip testing method as described in any one of the above items are executed.

[0018] From the above, it can be seen that the present application provides a resonator chip testing method, which obtains the contact resistance value between the probe and the chip electrode, obtains the current test signal frequency and the cumulative number of tests of the probe, 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, deducts the test error from the current test signal frequency, and obtains the calibrated test result, thereby realizing effective calibration of the resonator chip test results, thereby improving the accuracy and reliability of high-frequency testing in the automated probe testing system.

[0019] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a resonator chip testing method provided in an embodiment of the present application.

[0021] Figure 2 A schematic structural diagram of the resonator chip testing device provided in an embodiment of the present application.

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

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

[0024] Description of reference numerals: 21, acquisition module; 22, calculation module; 23, calibration module; 3, electronic device; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0027] Please refer to Figure 1-Figure 3 The present application provides a resonator chip testing method, device, 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] The present application provides a resonator chip testing method, comprising the steps of: S1. Obtain the contact resistance value between the probe and the chip electrode, obtain the current test signal frequency and the cumulative number of probe tests; 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. Deduct the test error from the current test signal frequency to obtain a calibrated test result.

[0029] Specifically, the present invention provides a resonator chip testing method for solving the technical problem that contact resistance affects test accuracy in an automated probe test system. First, the higher the contact resistance value, the greater the test error; the higher the high-frequency test signal frequency, the more significant the impact of contact resistance on test error. As the number of probe cumulative tests increases, probe wear increases, and the impact of contact resistance on test error also changes dynamically. Therefore, the contact resistance value between the probe and the chip electrode, the current test signal frequency, and the cumulative number of probe tests are all key factors affecting the test error. Then, according to the obtained contact resistance value, the current test signal frequency, and the cumulative number of tests, the test error is calculated using a preset dynamic error calibration model. Since the dynamic error calibration model comprehensively considers the impact of the above key factors on the test results, the test error is more accurately evaluated. Finally, the calculated test error is deducted from the current test signal frequency to obtain the calibrated test result. By deducting the error, the impact of contact resistance on the test result can be effectively reduced, and the accuracy of the test result is improved. Therefore, the present application achieves effective calibration of the resonator chip test result by acquiring the parameters that affect the test error in real time and using the dynamic error calibration model for error compensation, thereby improving the accuracy and reliability of high-frequency testing in the automated probe test system.

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

[0031] In some embodiments, step S2 includes: Obtaining a preset dynamic error calibration model, the dynamic error calibration model including a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; Inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data; Inputting the current test signal frequency into the frequency calibration sub-model to obtain second error data; Inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data; A 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 and calculate the test error in a more accurate and operational manner. The three key factors affecting the test error: contact resistance value, current test signal frequency and cumulative test times are processed using corresponding sub-models respectively. The contact resistance value is input into the contact resistance calibration sub-model to quantify the influence of contact resistance on the test error and obtain the first error data; the current test signal frequency is input into the frequency calibration sub-model to quantify the influence of the signal frequency itself on the test error and obtain the second error data. The cumulative number of tests is input into the probe test number calibration sub-model to quantify the influence of factors such as probe wear on the test error as the number of tests increases and obtain the third error data. The first error data, second error data and third error data output by these three sub-models are then comprehensively calculated to obtain the final test error. Since each sub-model is calibrated for a specific error source, the overall error calibration is more refined.

[0033] The contact resistance calibration sub-model measures the actual error under different contact resistance values ​​through experiments and establishes a mapping relationship between resistance value and error. For example, a polynomial regression fitting formula can be used: ; Where, is the first error data, is the contact resistance value, is the first preset coefficient, is the second preset coefficient, is the third preset coefficient; The frequency calibration sub-model compares the theoretical value with the actual measured value at the standard frequency to construct a frequency-error lookup table or a piecewise linear model, for example: ; Where, is the second error data, is the reference frequency, is the current test signal frequency, is the drift coefficient.

[0034] The probe test times calibration sub-model establishes a mapping relationship between the test times and the corresponding errors by long-term recording of the probe wear state and the corresponding errors at different test times. For example, an exponential decay model is used: ; Where, is the third error data, is the first fitting parameter (the limit error after the probe is completely worn out, which can be calibrated through experiments), is the second fitting parameter (probe wear rate, which can be calibrated experimentally), is the number of tests.

[0035] Among them, the test error is obtained by weighted calculation, and the weighted formula is as follows: ; Where, is the test error, is the first preset weight, is the second preset weight, The third preset weight.

[0036] In some embodiments, the step of inputting the cumulative number of tests into the probe test number calibration sub-model to obtain third error data includes: Obtaining a pressure parameter of the probe, adjusting the cumulative number of tests according to the pressure parameter, and obtaining an adjusted cumulative number of tests; The adjusted cumulative test times are input into the probe test times calibration sub-model to obtain third error data.

[0037] Specifically, in this way, the influence of pressure parameters on probe wear is taken into account, so that the probe test number calibration sub-model can 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 number of tests is no longer a simple count, but a correction value related to the actual working state of the probe. The third error data obtained in this way is more reliable, and ultimately improves the accuracy of the test results.

[0038] In some embodiments, the steps of obtaining a pressure parameter of the probe, adjusting the cumulative number of tests according to the pressure parameter, and obtaining the adjusted cumulative number of tests include: 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 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 to obtain an adjusted cumulative number of tests.

[0039] Specifically, when the pressure parameter is less than a preset pressure threshold (set according to actual needs and not specifically limited here), it means that the pressure of the probe is too low, and there may be an abnormality such as poor contact. At this time, the probe pressure is determined to be abnormal, and a pressure adjustment prompt message is output to remind the operator that the probe pressure is abnormal and needs to be adjusted to avoid inaccurate calculation of subsequent test errors; when 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 (the current probe pressure parameter is 12 gf (gram-force), and the system 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 number of tests 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 number of tests: 10,000 times * 1.2 = 12000 times), and obtain the adjusted cumulative test times to ensure that when the probe pressure is normal, the cumulative test times can be corrected according to the pressure parameters, thereby improving the accuracy of subsequent test error calculations.

[0040] In some embodiments, the step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain 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 prompt 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 a corrected contact resistance value, and the corrected contact resistance value is input into the contact resistance calibration sub-model to obtain first error data.

[0041] Specifically, in order to solve the impact of temperature changes in the test environment on the accuracy of the contact resistance value. This solution first obtains the temperature parameters of the test environment and determines whether this temperature parameter exceeds the preset normal temperature range. If the temperature exceeds the preset normal range, the system will output a temperature abnormality prompt message to alert the operator that there may be a problem with the current test environment. If the temperature is within the normal range, the contact resistance value is corrected according to the obtained temperature parameters (for example, the preset temperature range is set to 20°C to 25°C. When the sensor detects a temperature of 26°C, the system determines that the temperature exceeds the preset range and displays a prompt message on the operation interface that "The test environment temperature is abnormal, please check". If the sensor detects a temperature of 23°C, which is within the preset range, the system uses the preset temperature correction formula, such as , where is the corrected contact resistance value, is the current contact resistance value, is the current temperature, is the preset reference temperature, is the preset temperature coefficient), obtaining a contact resistance value that accounts for temperature. This corrected contact resistance value is then input into the contact resistance calibration sub-model to calculate the first error data. By adding temperature parameter consideration and correction steps before inputting into the contact resistance calibration sub-model, the error caused by test environment temperature fluctuations is reduced, ensuring the quality of input data for subsequent test error calculations and thus improving the accuracy of test results.

[0042] In some embodiments, the step of inputting the current test signal frequency into the frequency calibration sub-model to obtain the second error data includes: Acquire frequency drift data of the test signal source in real time, correct the current test signal frequency according to the frequency drift data, and 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 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 of acquiring frequency drift data in real time. This frequency drift data is used to correct the current test signal frequency, obtaining the corrected current test signal frequency and ensuring the accuracy of the current test signal frequency input into the frequency calibration sub-model.

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

[0045] Specifically, if the frequency drift exceeds the threshold, the frequency calibration is determined to have failed and a prompt message is given. The advantage of this is that when the frequency drift is too large, it indicates that there may be an abnormality in the test signal source. If frequency calibration is still performed at this time, it may introduce a larger error, which in turn reduces the accuracy of the test results. Only when the frequency drift is within an acceptable range (does not exceed the frequency drift threshold), frequency calibration is performed, and a frequency correction step is introduced (set according to actual needs, no specific restrictions are made here), and frequency correction is performed in a step-by-step manner (for example, if the drift is +3 MHz (the measured frequency is 3 Hz higher than expected), the correction step is 1 MHz / step (the minimum frequency change for each adjustment is 1 MHz), then the calculated frequency correction value is -3 MHz (reverse compensation for the drift is required). According to the calculated frequency correction value, the current test signal frequency is corrected. For example, the current test signal frequency (for example, 100 MHz) is added to the frequency correction value (for example, the frequency calibration value -3 MHz above, that is, -3 MHz is added). Hz), and obtain the corrected test signal frequency (for example, 97 MHz) to ensure the stability and accuracy of the frequency correction process.

[0046] In some embodiments, the step of calculating the test error based on the first error data, the second error data, and the third error data includes: Obtaining a preset weight distribution scheme, the weight distribution scheme including 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; A weighted average calculation is performed on the first error data, the second error data, and the third error data according to the first weight, the second weight, and the third weight to obtain a test error.

[0047] This solution obtains a preset weight distribution scheme that 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. The solution then performs a weighted average calculation on the first error data, the second error data, and the third error data based on the weight values ​​defined in the weight distribution scheme to obtain a test error. Through weight distribution and weighted average calculation, the impact of different error data on the test results can be fully considered when calculating the test error, thereby ensuring that the calculated test error can more accurately reflect the actual error situation.

[0048] In some embodiments, the step of obtaining a preset weight distribution scheme, wherein the weight distribution scheme 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: Probe type parameters are obtained, and a target weight distribution scheme is selected from a plurality of preset weight distribution schemes according to the probe type parameters. The target weight distribution scheme includes a first weight of the first error data, a second weight of the second error data, and a third weight of the third error data.

[0049] Since the weight distribution scheme is preset, that is, the weights of each error data have been determined before the test. However, different probe types may have different characteristics. For example, the stability of the contact resistance, frequency response characteristics and wear rate of different types of probes may be different. If a unified preset weight distribution scheme is adopted, the error impact caused by different probe types may not be fully considered, resulting in low accuracy of error calibration. In order to solve this problem, the present application proposes to select a weight distribution scheme based on the probe type parameters. Specifically, a plurality of weight distribution schemes are pre-set, and each weight distribution scheme corresponds to one or more probe types. When performing a test, the type parameters of the probe currently in use are first obtained, and then, based on the probe type parameters, a target weight distribution scheme that matches the current probe type is selected from the preset multiple weight distribution schemes. The target weight distribution 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 calculation to obtain the test error.

[0050] In a second aspect, the present application provides a resonator chip testing device for an automated probe testing system, comprising: An acquisition module 21 is used to obtain the contact resistance value between the probe and the chip electrode, the current test signal frequency, and the cumulative number of probe tests; A calculation module 22 is used to 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; The calibration module 23 is configured to deduct a test error from the current test signal frequency to obtain a calibrated test result.

[0051] Specifically, the present invention provides a resonator chip testing method for solving the technical problem that contact resistance affects test accuracy in an automated probe test system. First, the higher the contact resistance value, the greater the test error; the higher the high-frequency test signal frequency, the more significant the impact of contact resistance on test error. As the number of probe cumulative tests increases, probe wear increases, and the impact of contact resistance on test error also changes dynamically. Therefore, the contact resistance value between the probe and the chip electrode, the current test signal frequency, and the cumulative number of probe tests are all key factors affecting the test error. Then, according to the obtained contact resistance value, the current test signal frequency, and the cumulative number of tests, the test error is calculated using a preset dynamic error calibration model. Since the dynamic error calibration model comprehensively considers the impact of the above key factors on the test results, the test error is more accurately evaluated. Finally, the calculated test error is deducted from the current test signal frequency to obtain the calibrated test result. By deducting the error, the impact of contact resistance on the test result can be effectively reduced, and the accuracy of the test result is improved. Therefore, the present application achieves effective calibration of the resonator chip test result by acquiring the parameters that affect the test error in real time and using the dynamic error calibration model for error compensation, thereby improving the accuracy and reliability of high-frequency testing in the automated probe test system.

[0052] The calibration module 23 executes the subtraction of the test error from the current test signal frequency to obtain the calibrated test result. Specifically, the test error (5 MHz) is subtracted from the current test signal frequency (100 MHz). The calculation process is: 100 MHz - 5 MHz = 95 MHz. Therefore, the calibrated test result is 95 MHz.

[0053] In some embodiments, when 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, the calculation module 22 specifically performs the following steps: Obtaining a preset dynamic error calibration model, the dynamic error calibration model including a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; Inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data; Inputting the current test signal frequency into the frequency calibration sub-model to obtain second error data; Inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data; A 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 and calculate the test error in a more accurate and operational manner. The three key factors affecting the test error: contact resistance value, current test signal frequency and cumulative test times are processed using corresponding sub-models respectively. The contact resistance value is input into the contact resistance calibration sub-model to quantify the influence of contact resistance on the test error and obtain the first error data; the current test signal frequency is input into the frequency calibration sub-model to quantify the influence of the signal frequency itself on the test error and obtain the second error data. The cumulative number of tests is input into the probe test number calibration sub-model to quantify the influence of factors such as probe wear on the test error as the number of tests increases and obtain the third error data. The first error data, second error data and third error data output by these three sub-models are then comprehensively calculated to obtain the final test error. Since each sub-model is calibrated for a specific error source, the overall error calibration is more refined.

[0055] The contact resistance calibration sub-model measures the actual error under different contact resistance values ​​through experiments and establishes a mapping relationship between resistance value and error. For example, a polynomial regression fitting formula can be used: ; Where, is the first error data, is the contact resistance value, is the first preset coefficient, is the second preset coefficient, is the third preset coefficient; The frequency calibration sub-model compares the theoretical value with the actual measured value at the standard frequency to construct a frequency-error lookup table or a piecewise linear model, for example: ; Where, is the second error data, is the reference frequency, is the current test signal frequency, is the drift coefficient.

[0056] The probe test times calibration sub-model establishes a mapping relationship between the test times and the corresponding errors by long-term recording of the probe wear state and the corresponding errors at different test times. For example, an exponential decay model is used: ; Where, is the third error data, is the first fitting parameter (the limit error after the probe is completely worn out, which can be calibrated through experiments), is the second fitting parameter (probe wear rate, which can be calibrated experimentally), is the number of tests.

[0057] Among them, the test error is obtained by weighted calculation, and the weighted formula is as follows: ; Where, is the test error, is the first preset weight, is the second preset weight, The third preset weight.

[0058] In some embodiments, when the calculation module 22 inputs the accumulated test times into the probe test times calibration sub-model to obtain the third error data, it further executes: Obtaining a pressure parameter of the probe, adjusting the cumulative number of tests according to the pressure parameter, and obtaining an adjusted cumulative number of tests; The adjusted cumulative test times are input into the probe test times calibration sub-model to obtain third error data.

[0059] Specifically, in this way, the influence of pressure parameters on probe wear is taken into account, so that the probe test number calibration sub-model can 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 number of tests is no longer a simple count, but a correction value related to the actual working state of the probe. The third error data obtained in this way is more reliable, and ultimately improves the accuracy of the test results.

[0060] In some embodiments, when the calculation module 22 obtains the pressure parameter of the probe and adjusts the cumulative number of tests according to the pressure parameter to obtain the adjusted cumulative number of tests, it further executes: 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 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 to obtain an adjusted cumulative number of tests.

[0061] Specifically, when the pressure parameter is less than a preset pressure threshold (set according to actual needs and not specifically limited here), it means that the pressure of the probe is too low, and there may be an abnormality such as poor contact. At this time, the probe pressure is determined to be abnormal, and a pressure adjustment prompt message is output to remind the operator that the probe pressure is abnormal and needs to be adjusted to avoid inaccurate calculation of subsequent test errors; when 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 (the current probe pressure parameter is 12 gf (gram-force), and the system 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 number of tests 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 number of tests: 10,000 times * 1.2 = 12000 times), and obtain the adjusted cumulative test times to ensure that when the probe pressure is normal, the cumulative test times can be corrected according to the pressure parameters, thereby improving the accuracy of subsequent test error calculations.

[0062] In some embodiments, when the calculation module 22 inputs the contact resistance value into the contact resistance calibration sub-model to obtain the first error data, it further executes: 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 prompt 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 a corrected contact resistance value, and the corrected contact resistance value is input into the contact resistance calibration sub-model to obtain first error data.

[0063] Specifically, in order to solve the impact of temperature changes in the test environment on the accuracy of the contact resistance value. This solution first obtains the temperature parameters of the test environment and determines whether this temperature parameter exceeds the preset normal temperature range. If the temperature exceeds the preset normal range, the system will output a temperature abnormality prompt message to alert the operator that there may be a problem with the current test environment. If the temperature is within the normal range, the contact resistance value is corrected according to the obtained temperature parameters (for example, the preset temperature range is set to 20°C to 25°C. When the sensor detects a temperature of 26°C, the system determines that the temperature exceeds the preset range and displays a prompt message on the operation interface that "The test environment temperature is abnormal, please check". If the sensor detects a temperature of 23°C, which is within the preset range, the system uses the preset temperature correction formula, such as , where is the corrected contact resistance value, is the current contact resistance value, is the current temperature, is the preset reference temperature, is the preset temperature coefficient), obtaining a contact resistance value that accounts for temperature. This corrected contact resistance value is then input into the contact resistance calibration sub-model to calculate the first error data. By adding temperature parameter consideration and correction steps before inputting into the contact resistance calibration sub-model, the error caused by test environment temperature fluctuations is reduced, ensuring the quality of input data for subsequent test error calculations and thus improving the accuracy of test results.

[0064] In some embodiments, when the calculation module 22 inputs the current test signal frequency into the frequency calibration sub-model to obtain the second error data, it further executes: Acquire frequency drift data of the test signal source in real time, correct the current test signal frequency according to the frequency drift data, and 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 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 of acquiring frequency drift data in real time. This frequency drift data is used to correct the current test signal frequency, obtaining the corrected current test signal frequency and ensuring the accuracy of the current test signal frequency input into the frequency calibration sub-model.

[0066] In some embodiments, when the calculation module 22 acquires the frequency drift data of the test signal source in real time and corrects the current test signal frequency according to the frequency drift data to obtain the corrected current test signal frequency, it further executes: Determining whether the frequency drift amount in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure prompt message will be output; If the frequency drift does not exceed the frequency drift threshold, a frequency correction value is calculated according to the frequency drift and the frequency correction step size, and the current test signal frequency is corrected according to the frequency correction value to obtain a corrected test signal frequency.

[0067] Specifically, if the frequency drift exceeds the threshold, the frequency calibration is determined to have failed and a prompt message is given. The advantage of this is that when the frequency drift is too large, it indicates that there may be an abnormality in the test signal source. If frequency calibration is still performed at this time, it may introduce a larger error, which in turn reduces the accuracy of the test results. Only when the frequency drift is within an acceptable range (does not exceed the frequency drift threshold), frequency calibration is performed, and a frequency correction step is introduced (set according to actual needs, no specific restrictions are made here), and frequency correction is performed in a step-by-step manner (for example, if the drift is +3MHz (the measured frequency is 3 Hz higher than expected), the correction step is 1MHz / step (the minimum frequency change for each adjustment is 1MHz), then the calculated frequency correction value is -3 MHz (reverse compensation for the drift is required). According to the calculated frequency correction value, the current test signal frequency is corrected. For example, the current test signal frequency (for example, 100MHz) is added to the frequency correction value (for example, the frequency calibration value -3MHz above, that is, -3MHz is added). Hz), and obtain the corrected test signal frequency (for example, 97 MHz) to ensure the stability and accuracy of the frequency correction process.

[0068] In some embodiments, when calculating the test error based on the first error data, the second error data, and the third error data, the calculation module 22 further performs: Obtaining a preset weight distribution scheme, the weight distribution scheme including 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; A weighted average calculation is performed on the first error data, the second error data, and the third error data according to the first weight, the second weight, and the third weight to obtain a test error.

[0069] This solution obtains a preset weight distribution scheme that 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. The solution then performs a weighted average calculation on the first error data, the second error data, and the third error data based on the weight values ​​defined in the weight distribution scheme to obtain a test error. Through weight distribution and weighted average calculation, the impact of different error data on the test results can be fully considered when calculating the test error, thereby ensuring that the calculated test error can more accurately reflect the actual error situation.

[0070] In some embodiments, when the calculation module 22 obtains a preset weight distribution scheme, the weight distribution scheme including a first weight of the first error data, a second weight of the second error data, and a third weight of the third error data, the calculation module 22 further performs: Probe type parameters are obtained, and a target weight distribution scheme is selected from a plurality of preset weight distribution schemes according to the probe type parameters. The target weight distribution scheme includes a first weight of the first error data, a second weight of the second error data, and a third weight of the third error data.

[0071] Since the weight distribution scheme is preset, that is, the weights of each error data have been determined before the test. However, different probe types may have different characteristics. For example, the stability of the contact resistance, frequency response characteristics and wear rate of different types of probes may be different. If a unified preset weight distribution scheme is adopted, the error impact caused by different probe types may not be fully considered, resulting in low accuracy of error calibration. In order to solve this problem, the present application proposes to select a weight distribution scheme based on the probe type parameters. Specifically, a plurality of weight distribution schemes are pre-set, and each weight distribution scheme corresponds to one or more probe types. When performing a test, the type parameters of the probe currently in use are first obtained, and then, based on the probe type parameters, a target weight distribution scheme that matches the current probe type is selected from the preset multiple weight distribution schemes. The target weight distribution 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 calculation to obtain the test error.

[0072] Please refer to Figure 3 , Figure 3 This is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The present application provides an electronic device 3, including: 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 forms of 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 the computer-readable instructions to execute the method in any optional implementation of the above embodiment to achieve the following functions: obtain the contact resistance value between the probe and the chip electrode, obtain the current test signal frequency and the cumulative number of tests of the probe; 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; deduct the test error from the current test signal frequency to obtain a calibrated test result.

[0073] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed 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; deducting the test error from the current test signal frequency to obtain a calibrated test result. Among them, 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 read-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 devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0075] In addition, the units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] Furthermore, the functional modules in each embodiment of the present 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, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0078] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A resonator chip testing method for an automated probe testing system, characterized in that: Including steps: S1. Obtain the contact resistance value between the probe and the chip electrode, 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. Deduct the test error from the current test signal frequency to obtain a calibrated test result.

2. The resonator chip testing method according to claim 1, characterized in that: Step S2 includes: Obtaining the preset dynamic error calibration model, wherein the dynamic error calibration model includes a contact resistance calibration sub-model, a frequency calibration sub-model, and a probe test number calibration sub-model; Inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data; Inputting the current test signal frequency into the frequency calibration sub-model to obtain second error data; Inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data; The test error is obtained by calculation according to the first error data, the second error data and the third error data.

3. The resonator chip testing method according to claim 2, characterized in that: The step of inputting the accumulated test times into the probe test times calibration sub-model to obtain third error data comprises: Acquiring a pressure parameter of the probe, and adjusting the cumulative number of tests according to the pressure parameter to obtain the adjusted cumulative number of tests; The adjusted cumulative test times are input into the probe test times calibration sub-model to obtain third error data.

4. The resonator chip testing method according to claim 3, characterized in that: The steps of obtaining the pressure parameter of the probe, adjusting the cumulative number of tests according to the pressure parameter, and obtaining the adjusted cumulative number of tests include: Obtaining a preset pressure threshold, and determining 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, it is determined that the pressure of the probe is abnormal, and a pressure adjustment prompt message is output; If the pressure parameter is greater than or equal to the preset pressure threshold, the accumulated number of tests is adjusted according to the ratio of the pressure parameter to the preset pressure threshold to obtain the adjusted accumulated number of tests.

5. The resonator chip testing method according to claim 2, wherein: The step of inputting the contact resistance value into the contact resistance calibration sub-model to obtain first error data includes: Obtaining temperature parameters of the test environment, and determining whether the temperature parameters exceed a preset temperature range; If the temperature parameter exceeds the preset temperature range, a temperature abnormality prompt message is 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, and the corrected contact resistance value is input into the contact resistance calibration sub-model to obtain first error data.

6. The resonator chip testing method according to claim 2, characterized in that: The step of inputting the current test signal frequency into the frequency calibration sub-model to obtain second error data includes: Acquire frequency drift data of a test signal source in real time, and correct the current test signal frequency according to the frequency drift data to obtain a corrected current test signal frequency; The corrected frequency of the current test signal is input into the frequency calibration sub-model to obtain second error data.

7. The resonator chip testing method according to claim 6, characterized in that: The step of acquiring frequency drift data of a test signal source in real time, and correcting the current test signal frequency according to the frequency drift data to obtain the corrected current test signal frequency comprises: Determining whether the frequency drift amount in the frequency drift data exceeds a preset frequency drift threshold; If the frequency drift exceeds the frequency drift threshold, a frequency calibration failure prompt message is output; If the frequency drift does not exceed the frequency drift threshold, a frequency correction value is calculated according to the frequency drift and the frequency correction step size, and the current test signal frequency is corrected according to the frequency correction value to obtain the corrected test signal frequency.

8. A resonator chip testing device for an automated probe testing system, characterized in that: include: An acquisition module is used to obtain 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; a calculation module, configured to calculate a test error using a preset dynamic error calibration model according to the contact resistance value, the current test signal frequency, and the accumulated number of tests; The calibration module is used to deduct the test error from the current test signal frequency to obtain a calibrated test result.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the resonator chip testing method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the resonator chip testing method according to any one of claims 1 to 7 are executed.

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