Chip test calibration method and chip tester

Abstract

The invention relates to the technical field of integrated circuit test and calibration, and discloses a chip test and calibration method and a chip tester. The chip test calibration method is applied to a chip test module and specifically comprises the following steps that S101, a starting signal sent by a user terminal is received, environment initialization operation is executed, and after initialization is completed, a system automatically enters a standby mode and waits for an external trigger signal or a user instruction; and S102, synchronously capturing input and output signal waveforms of the tested chip at a preset sampling frequency through a high-precision current sensor and a voltage sampling circuit. Through a dynamic parameter acquisition and real-time compensation technology, a voltage reference error is effectively reduced, the temperature control precision is superior to + / -0.3 DEG C, signal distortion caused by environmental interference is effectively inhibited, a multi-dimensional calibration model is combined with time domain, frequency domain and statistical characteristic analysis, nonlinear errors and system drift can be dynamically corrected, and the system performance is improved. And the attenuation error of a test signal transmission path is lower than 0.02 dB.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated circuit testing and calibration, and in particular to a chip testing and calibration method and a chip tester. Background Art

[0002] Chip testing is an indispensable part of the chip manufacturing process. Traditional chip testers rely on a fixed laboratory environment and cannot adapt to temperature and humidity fluctuations, electromagnetic interference, and mechanical vibrations in industrial sites, resulting in severe distortion of test signals. Existing calibration methods use static compensation parameters and cannot dynamically respond to environmental changes.

[0003] Moreover, the chip testing process is highly dependent on manual operation. Test case selection, error analysis, and adjustment decisions all require engineers' experience and judgment, resulting in long testing cycles and poor consistency. For example, in the chip function verification stage, manual screening of boundary condition test cases takes more than 60% of the overall process and is difficult to cover complex working condition combinations. In terms of calibration data management, historical test records are stored in a scattered manner, lacking correlation analysis and model iteration capabilities, resulting in the need for repeated calibration of the same type of chips. In addition, traditional methods cannot achieve full life cycle data traceability, test reports are easily tampered with, and adjustment records are difficult to match with the actual chip, posing legal risks in quality traceability.

[0004] Based on the above, a closed-loop calibration system based on intelligent algorithms and blockchain has not yet been established in the existing technology, which limits the autonomous decision-making ability and credibility of the testing system. Summary of the Invention

[0005] The purpose of the present invention is to provide a chip test and calibration method and a chip tester, which effectively reduce the voltage reference error through dynamic parameter acquisition and real-time compensation technology, and achieve a temperature control accuracy better than ±0.3°C, effectively suppressing signal distortion caused by environmental interference, aiming to solve the problems in the prior art.

[0006] The present invention is implemented as follows: a chip test calibration method is applied to a chip test module, specifically comprising the following steps:

[0007] S101: receiving a start signal sent by a user terminal and performing an environment initialization operation. After the initialization is completed, the system automatically enters a standby mode and waits for an external trigger signal or user instruction;

[0008] S102: Using a high-precision current sensor and voltage sampling circuit, the input and output signal waveforms of the chip under test are synchronously captured at a preset sampling frequency. Data from the temperature sensor and ambient humidity sensor are also integrated. Based on the real-time acquired signal characteristics, an adaptive filtering algorithm is used to eliminate high-frequency noise interference. Nonlinear correction is performed on the raw data using pre-stored chip characteristic curves to complete the acquisition of dynamic parameters for the calibration model.

[0009] S103: Based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical features is established. During the model construction process, a machine learning algorithm is introduced to train the historical calibration data to optimize the weight distribution of the compensation parameters. The model is verified through Monte Carlo simulation to generate a dynamically updateable calibration coefficient matrix, which is stored in a non-volatile memory.

[0010] S104: Compare the real-time test data with the theoretical values ​​of the calibration model item by item, identify the source of the error and perform hierarchical processing. During the correction process, the calibration coefficient matrix is ​​synchronously updated, and the correction record is uploaded to the cloud knowledge base via wireless communication for subsequent iterative optimization of the model. A redundant check mechanism is used to verify the error of key parameters to ensure that the relative error between the corrected output signal and the theoretical value is less than a preset error threshold;

[0011] S105: After calibration is completed, boundary condition test cases, extreme load test modes and aging acceleration test scenarios are loaded to verify the performance consistency of the chip under different working conditions. After verification, an encrypted test report containing original data, calibration parameters and adjustment records is generated and output to the user terminal via QR code or NFC.

[0012] Furthermore, in S101, a start signal sent by a user terminal is received and an environment initialization operation is performed, including:

[0013] Load the preset calibration parameter database, activate the embedded temperature compensation module, and configure the multi-channel data acquisition interface;

[0014] The built-in hardware self-test unit verifies the tester's power supply stability, signal generator accuracy, and ADC / DAC modules item by item and generates a self-test report.

[0015] If the verification fails, a graded alarm mechanism is triggered, which includes the LED status indicator changing color and wireless communication sending an error code to the cloud server.

[0016] Furthermore, the startup signal includes a self-test request and detection target chip type data;

[0017] The self-test request is used to complete the self-test startup of the chip test module;

[0018] The detection target chip type data calls a matching test protocol template according to the target chip type, and displays the initialization progress through a human-computer interaction interface to ensure that the test environment meets the preset threshold voltage reference error of ±0.05% and the preset threshold temperature control accuracy of ±0.1°C.

[0019] Furthermore, after initialization is complete, the system automatically enters standby mode, including:

[0020] For chips that do not meet the standards, laser trimming is activated, and the fuse programming strategy is dynamically adjusted based on the error analysis results to modify the bias voltage or gain coefficient of the internal register;

[0021] After the adjustment is completed, the closed-loop calibration process is performed again until the key indicators of the chip meet the preset acceptance criteria;

[0022] The key indicators include power consumption, timing margin and signal-to-noise ratio.

[0023] Furthermore, in S102, the original data is subjected to nonlinear correction using the pre-stored chip characteristic curve to complete the acquisition of dynamic parameters of the calibration model. The nonlinear correction includes the temperature drift effect:

[0024] A dynamic compensation mechanism is activated to address the temperature drift effect. The internal thermostat is adjusted by a PID controller to stabilize the operating temperature of the chip under test within the set value ±0.5°C.

[0025] By comparing historical calibration data with current acquisition values ​​in real time, the output amplitude and phase offset of the signal generator are dynamically adjusted to ensure that the attenuation error of the test signal in the transmission path is less than 0.02dB.

[0026] Furthermore, in S103, based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical features is established, including:

[0027] Time domain model, using the least squares method to fit the compensation function of signal rise time, overshoot rate and steady-state error;

[0028] Frequency domain model, using FFT to analyze harmonic distortion components and generate a frequency response curve correction coefficient table;

[0029] Statistical model, variance analysis is performed on the results of multiple consecutive tests, and the confidence interval threshold is calculated after eliminating abnormal data points.

[0030] Furthermore, in S104, the real-time test data is compared with the theoretical values ​​of the calibration model item by item, the error sources are identified and a classification process is performed, wherein the classification process includes:

[0031] Level 1 error: if the deviation is less than 1%, the preset compensation parameters will be automatically called for linear correction.

[0032] Secondary error: if the deviation of the secondary error is 1% ≤ deviation < 5%, the multi-parameter joint optimization algorithm is triggered to recalculate the signal gain, offset and filter cutoff frequency;

[0033] Level 3 error: if the deviation of the level 3 error is ≥5%, the test process will be interrupted, the hardware diagnosis mode will be started, the faulty module will be located, and a replacement of spare parts will be prompted.

[0034] Furthermore, in S105, after calibration, boundary condition test cases, extreme load test modes, and aging acceleration test scenarios are loaded to verify the performance consistency of the chip under different working conditions, including:

[0035] In extreme load testing, a programmable electronic load module is used to simulate a full range of load transitions from no-load to short-circuit, monitor the transient response characteristics of the chip, and use digital twin technology to build a virtual test environment to predict potential failure modes.

[0036] Arrhenius accelerated aging test was conducted by increasing the ambient temperature to 125°C and applying 1.5 times the rated voltage for 72 hours to achieve a 5-year service life.

[0037] During the verification process, if parameter drift is found to exceed the tolerance, the self-healing mechanism is automatically triggered. The self-healing mechanism first attempts software compensation. If this fails, the backup circuit module is activated to replace the faulty unit. The final test report integrates 3D thermal imaging images, signal spectrum waterfall diagrams, and video logs of the adjustment process.

[0038] Compared with the prior art, the chip test calibration method and chip tester provided by the present invention have the following beneficial effects:

[0039] 1. Through dynamic parameter acquisition and real-time compensation technology, voltage reference errors are effectively reduced, temperature control accuracy is better than ±0.3°C, and signal distortion caused by environmental interference is effectively suppressed. The multi-dimensional calibration model combines time domain, frequency domain, and statistical feature analysis to dynamically correct nonlinear errors and system drift, reducing the attenuation error of the test signal transmission path to less than 0.02dB and harmonic distortion by more than 40%. The introduction of quantized standard sources and atomic clock technology improves the voltage and time reference accuracy to 0.1ppm and 1e-12 / hour respectively. In addition, the integration of non-contact stress detection and X-ray CT technology can detect packaging defects in advance and compensate for the influence of mechanical deformation, ensuring that the calibrated chip maintains stable performance in the temperature range of -40°C to 125°C and under 50G vibration conditions.

[0040] 2. An intelligent calibration strategy based on deep reinforcement learning and meta-learning can autonomously optimize test paths, skip redundant steps, shorten overall calibration time, and support the rapid adaptation of new chips. Digital twin technology, combined with accelerated aging test models, can predict chip lifespan and failure modes in a virtual environment, guiding on-site maintenance decisions. Blockchain technology assigns a unique digital identity to each chip, recording data from wafer testing to terminal use, ensuring that adjustment records are tamper-proof and traceable, and improving quality traceability efficiency by 90%. At the same time, the reconfigurable RF front-end supports full-band testing from DC to 40 GHz and, combined with 3D near-field scanning technology, meets the complex radiation characteristic calibration requirements of 5G Massive MIMO chips. The application of a federated learning framework and homomorphic encryption technology enables collaborative optimization of multi-terminal data, continuously improving the generalization capability of the calibration model while protecting privacy, and enhancing system scalability.

[0041] A chip tester includes a chip test module, a storage device, and a processor. The storage device is used to store a computer program. The processor runs the computer program to enable the chip test module to perform the chip test calibration method described above.

[0042] Specifically, the chip testing module includes:

[0043] The control unit integrates a multi-core processor and an FPGA programmable logic unit. It is used to perform test environment initialization, dynamic parameter acquisition, and calibration model calculations. It can perform full-link diagnosis of the tester's internal functional modules and display fault codes and repair suggestions in real time through status indicators and the touch screen.

[0044] The signal acquisition and generation unit consists of an ADC / DAC array equipped with a programmable gain amplifier (PGA) and an anti-aliasing filter;

[0045] The environmental control unit achieves a temperature control accuracy of ±0.1°C through a PID controller, integrates an electromagnetic shielding cavity and active noise cancellation circuit to suppress external interference, and has a built-in non-contact stress detection unit that uses a femtosecond laser and laser interferometer to measure the mechanical stress distribution inside the chip package;

[0046] A calibration and adjustment unit, which runs a machine learning algorithm to build a 3D calibration model and performs hierarchical error correction;

[0047] The communication and data management unit is used to achieve real-time upload of test data and remote command reception. The built-in blockchain node generates a unique digital identity for each chip and records data throughout its life cycle. The deployment of a federated learning framework supports multi-terminal collaborative optimization and calibration models. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1A schematic flow chart of a chip testing and calibration method proposed by the present invention;

[0049] Figure 2 This is a schematic block diagram of the process of receiving a start signal sent by a user terminal and performing an environment initialization operation in a chip testing and calibration method proposed by the present invention;

[0050] Figure 3 This is a structural schematic diagram of a chip test module in a chip tester proposed by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] The implementation of the present invention is described in detail below with reference to specific embodiments.

[0053] The same or similar numbers in the drawings of this embodiment correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "up", "down", "left", "right", etc. indicate directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0054] Reference Figure 1-2 As shown, a chip test calibration method is applied to a chip test module and specifically includes the following steps:

[0055] S101: receiving a start signal sent by a user terminal and performing an environment initialization operation. After the initialization is completed, the system automatically enters a standby mode and waits for an external trigger signal or user instruction;

[0056] Receiving a startup signal sent by a user terminal and performing an environment initialization operation include:

[0057] Load the preset calibration parameter database, activate the embedded temperature compensation module, and configure the multi-channel data acquisition interface;

[0058] The built-in hardware self-test unit verifies the tester's power supply stability, signal generator accuracy, and ADC / DAC modules item by item and generates a self-test report.

[0059] If the verification fails, a hierarchical alarm mechanism is triggered, including the LED status indicator changing color and wireless communication sending an error code to the cloud server;

[0060] S102: Using a high-precision current sensor and voltage sampling circuit, the input and output signal waveforms of the chip under test are synchronously captured at a preset sampling frequency. Data from the temperature sensor and ambient humidity sensor are also integrated. Based on the real-time acquired signal characteristics, an adaptive filtering algorithm is used to eliminate high-frequency noise interference. Nonlinear correction is performed on the raw data using pre-stored chip characteristic curves to complete the acquisition of dynamic parameters for the calibration model.

[0061] The pre-stored chip characteristic curve is used to perform nonlinear correction on the original data to complete the dynamic parameter acquisition of the calibration model. The nonlinear correction includes the temperature drift effect:

[0062] A dynamic compensation mechanism is activated to address the temperature drift effect. The internal thermostat is adjusted by a PID controller to stabilize the operating temperature of the chip under test within the set value ±0.5°C.

[0063] By comparing historical calibration data with current acquisition values ​​in real time, the output amplitude and phase offset of the signal generator are dynamically adjusted to ensure that the attenuation error of the test signal in the transmission path is less than 0.02dB;

[0064] S103: Based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical features is established. During the model construction process, a machine learning algorithm is introduced to train the historical calibration data to optimize the weight distribution of the compensation parameters. The model is verified through Monte Carlo simulation to generate a dynamically updateable calibration coefficient matrix, which is stored in a non-volatile memory.

[0065] Based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical characteristics is established, including:

[0066] Time domain model, using the least squares method to fit the compensation function of signal rise time, overshoot rate and steady-state error;

[0067] Frequency domain model, using FFT to analyze harmonic distortion components and generate a frequency response curve correction coefficient table;

[0068] Statistical model, perform variance analysis on multiple consecutive test results, calculate the confidence interval threshold after eliminating abnormal data points;

[0069] S104: Compare the real-time test data with the theoretical values ​​of the calibration model item by item, identify the source of the error and perform hierarchical processing. During the correction process, the calibration coefficient matrix is ​​synchronously updated, and the correction record is uploaded to the cloud knowledge base via wireless communication for subsequent iterative optimization of the model. A redundant check mechanism is used to verify the error of key parameters to ensure that the relative error between the corrected output signal and the theoretical value is less than a preset error threshold;

[0070] The real-time test data is compared item by item with the theoretical value of the calibration model to identify the source of the error and perform a hierarchical process. The hierarchical process includes:

[0071] Level 1 error: Level 1 error is: deviation <1%, automatically calling the preset compensation parameters for linear correction;

[0072] Secondary error: When the deviation of the secondary error is 1% ≤ deviation < 5%, the multi-parameter joint optimization algorithm is triggered to recalculate the signal gain, offset and filter cutoff frequency;

[0073] Level 3 error: If the deviation of level 3 error is ≥5%, the test process will be interrupted, the hardware diagnosis mode will be started, the faulty module will be located, and a replacement part will be prompted;

[0074] S105: After calibration, boundary condition test cases, extreme load test modes, and accelerated aging test scenarios are applied to verify the chip's performance consistency under different operating conditions. After verification, an encrypted test report containing raw data, calibration parameters, and adjustment records is generated and output to the user terminal via a QR code or NFC. Dynamic parameter acquisition and real-time compensation effectively reduce voltage reference errors, achieve temperature control accuracy better than ±0.3°C, and effectively suppress signal distortion caused by environmental interference. A multi-dimensional calibration model, combining time-domain, frequency-domain, and statistical feature analysis, dynamically corrects nonlinear errors and system drift, reducing test signal transmission path attenuation error to less than 0.02dB and harmonic distortion by over 40%. The introduction of quantized standard sources and atomic clock technology improves voltage and time reference accuracy to 0.1ppm and 1e-12 / hour, respectively. Furthermore, the integration of non-contact stress detection and X-ray CT technology enables early detection of packaging defects and compensation for mechanical deformation, ensuring stable performance of the calibrated chip over a temperature range of -40°C to 125°C and under 50G vibration conditions.

[0075] Specifically, blockchain technology gives each chip a unique digital identity, recording the entire process data from wafer testing to terminal use, ensuring that the adjustment records cannot be tampered with and are traceable. At the same time, the reconfigurable RF front-end supports full-band testing from DC to 40GHz, and combined with three-dimensional near-field scanning technology, it realizes multi-terminal data collaborative optimization. While protecting privacy, it continuously improves the generalization capability of the calibration model and enhances system scalability.

[0076] In S101 of this embodiment, the start signal includes a self-test request and detection target chip type data;

[0077] The self-test request is used to complete the self-test startup of the chip test module;

[0078] The target chip type data is detected and the matching test protocol template is called according to the target chip type. The initialization progress is displayed through the human-computer interaction interface to ensure that the test environment meets the preset threshold voltage reference error of ±0.05% and the preset threshold temperature control accuracy of ±0.1°C.

[0079] In this embodiment, after initialization is completed, the system automatically enters standby mode, including:

[0080] For chips that do not meet the standards, laser trimming is activated, and the fuse programming strategy is dynamically adjusted based on the error analysis results to modify the bias voltage or gain coefficient of the internal register;

[0081] After the adjustment is completed, the closed-loop calibration process is performed again until the key indicators of the chip meet the preset acceptance criteria;

[0082] Key metrics include power consumption, timing margin, and signal-to-noise ratio.

[0083] In S105 of this embodiment, after calibration, boundary condition test cases, extreme load test mode, and aging acceleration test scenarios are loaded to verify the performance consistency of the chip under different working conditions, including:

[0084] In extreme load testing, a programmable electronic load module is used to simulate a full range of load transitions from no-load to short-circuit, monitor the transient response characteristics of the chip, and use digital twin technology to build a virtual test environment to predict potential failure modes.

[0085] Arrhenius accelerated aging test was conducted by increasing the ambient temperature to 125°C and applying 1.5 times the rated voltage for 72 hours to achieve a 5-year service life.

[0086] During the verification process, if parameter drift is found to exceed the tolerance, the self-healing mechanism is automatically triggered. The self-healing mechanism first attempts software compensation. If it fails, the backup circuit module is activated to replace the faulty unit. The final test report integrates 3D thermal imaging images, signal spectrum waterfall diagrams and video logs of the adjustment process.

[0087] This technical solution is based on an intelligent calibration strategy based on deep reinforcement learning and meta-learning. It can autonomously optimize test paths, skip redundant steps, shorten overall calibration time, and support rapid adaptation of new chips. Digital twin technology, combined with accelerated aging test models, can predict chip life and failure modes in a virtual environment, guiding on-site maintenance decisions. Blockchain technology gives each chip a unique digital identity, recording data from wafer testing to terminal use, ensuring that adjustment records cannot be tampered with and are traceable, and improving quality traceability efficiency by 90%. At the same time, the reconfigurable RF front-end supports full-band testing from DC to 40GHz, and combined with three-dimensional near-field scanning technology, it meets the complex radiation characteristic calibration requirements of 5G Massive MIMO chips. The application of the federated learning framework and homomorphic encryption technology realizes collaborative optimization of multi-terminal data, continuously improves the generalization capability of the calibration model while protecting privacy, and enhances system scalability.

[0088] Reference Figure 3 As shown, a chip tester includes a chip test module, a storage device and a processor. The storage device is used to store computer programs. The processor runs the computer programs to enable the chip test module to execute the above-mentioned chip test calibration method. The intelligent calibration strategy based on deep reinforcement learning and meta-learning can autonomously optimize the test path, skip redundant steps, shorten the overall calibration time, and support the rapid adaptation of new chips. Digital twin technology combined with aging accelerated test model can predict chip life and failure mode in a virtual environment, and guide on-site maintenance decisions.

[0089] In this embodiment, the chip test module includes: a control unit, which integrates a multi-core processor and an FPGA programmable logic unit, and is used to perform test environment initialization, dynamic parameter acquisition and calibration model calculations. It can perform full-link diagnosis of the internal functional modules of the tester, and display fault codes and repair suggestions in real time through status indicators and touch screens; a signal acquisition and generation unit, which is composed of an ADC / DAC array and is equipped with a programmable gain amplifier (PGA) and an anti-aliasing filter. Through dynamic parameter acquisition and real-time compensation technology, the voltage reference error is effectively reduced, and the temperature control accuracy is better than ±0.3°C, which effectively suppresses signal distortion caused by environmental interference; an environmental control unit, which achieves a temperature control accuracy of ±0.1°C through a PID controller, and integrates an electromagnetic shielding cavity and an active noise cancellation circuit to suppress external interference. It has a built-in non-contact stress detection unit, which uses a femtosecond laser and a laser interferometer to measure the mechanical stress distribution inside the chip package; calibration and The adjustment unit is used to run machine learning algorithms to build a three-dimensional calibration model and perform hierarchical error correction. The multi-dimensional calibration model combines time domain, frequency domain, and statistical feature analysis to dynamically correct nonlinear errors and system drift, reducing the attenuation error of the test signal transmission path to less than 0.02dB and harmonic distortion to more than 40%. The communication and data management unit is used to achieve real-time upload of test data and remote command reception. The built-in blockchain node generates a unique digital identity for each chip and records data throughout its life cycle. A federated learning framework is deployed to support multi-terminal collaborative optimization of the calibration model. The introduction of quantized standard sources and atomic clock technology improves the voltage and time reference accuracy to 0.1ppm and 1e-12 / hour. In addition, the integration of non-contact stress detection and X-ray CT technology can detect packaging defects in advance and compensate for the impact of mechanical deformation, so that the calibrated chip still maintains stable performance in the temperature range of -40°C to 125°C and under 50G vibration conditions.

[0090] In this embodiment, the entire operation process can be controlled by a computer to provide signal feedback to implement the steps in sequence. These are all conventional knowledge of current automated control and will not be described in detail in this embodiment.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A chip testing and calibration method, characterized in that: Applied to chip test module, specifically including the following steps: S101: receiving a start signal sent by a user terminal and performing an environment initialization operation. After the initialization is completed, the system automatically enters a standby mode and waits for an external trigger signal or user instruction; S102: Using a high-precision current sensor and voltage sampling circuit, the input and output signal waveforms of the chip under test are synchronously captured at a preset sampling frequency. Data from the temperature sensor and ambient humidity sensor are also integrated. Based on the real-time acquired signal characteristics, an adaptive filtering algorithm is used to eliminate high-frequency noise interference. Nonlinear correction is performed on the raw data using pre-stored chip characteristic curves to complete the acquisition of dynamic parameters for the calibration model. S103: Based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical features is established. During the model construction process, a machine learning algorithm is introduced to train the historical calibration data to optimize the weight distribution of the compensation parameters. The model is verified through Monte Carlo simulation to generate a dynamically updateable calibration coefficient matrix, which is stored in a non-volatile memory. S104: Compare the real-time test data with the theoretical values ​​of the calibration model item by item, identify the source of the error and perform hierarchical processing. During the correction process, the calibration coefficient matrix is ​​synchronously updated, and the correction record is uploaded to the cloud knowledge base via wireless communication for subsequent iterative optimization of the model. A redundant check mechanism is used to verify the error of key parameters to ensure that the relative error between the corrected output signal and the theoretical value is less than a preset error threshold; S105: After calibration is completed, boundary condition test cases, extreme load test modes and aging acceleration test scenarios are loaded to verify the performance consistency of the chip under different working conditions. After verification, an encrypted test report containing original data, calibration parameters and adjustment records is generated and output to the user terminal via QR code or NFC.

2. A chip testing and calibration method according to claim 1, characterized in that: In S101, a start signal sent by a user terminal is received and an environment initialization operation is performed, including: Load the preset calibration parameter database, activate the embedded temperature compensation module, and configure the multi-channel data acquisition interface; The built-in hardware self-test unit verifies the tester's power supply stability, signal generator accuracy, and ADC / DAC modules item by item and generates a self-test report. If the verification fails, a graded alarm mechanism is triggered, which includes the LED status indicator changing color and wireless communication sending an error code to the cloud server.

3. A chip testing and calibration method according to claim 2, characterized in that: The start signal includes a self-test request and detection target chip type data; The self-test request is used to complete the self-test startup of the chip test module; The detection target chip type data calls a matching test protocol template according to the target chip type, and displays the initialization progress through a human-computer interaction interface to ensure that the test environment meets the preset threshold voltage reference error of ±0.05% and the preset threshold temperature control accuracy of ±0.1°C.

4. A chip testing and calibration method according to claim 3, characterized in that: After initialization is complete, the system automatically enters standby mode, including: For chips that do not meet the standards, laser trimming is activated, and the fuse programming strategy is dynamically adjusted based on the error analysis results to modify the bias voltage or gain coefficient of the internal register; After the adjustment is completed, the closed-loop calibration process is performed again until the key indicators of the chip meet the preset acceptance criteria; The key indicators include power consumption, timing margin and signal-to-noise ratio.

5. A chip testing and calibration method as claimed in claim 4, characterized in that: In S102, the original data is subjected to nonlinear correction using the pre-stored chip characteristic curve to complete the acquisition of dynamic parameters of the calibration model. The nonlinear correction includes the temperature drift effect: A dynamic compensation mechanism is activated to address the temperature drift effect. The internal thermostat is adjusted by a PID controller to stabilize the operating temperature of the chip under test within the set value ±0.5°C. By comparing historical calibration data with current acquisition values ​​in real time, the output amplitude and phase offset of the signal generator are dynamically adjusted to ensure that the attenuation error of the test signal in the transmission path is less than 0.02dB.

6. A chip testing and calibration method according to claim 5, characterized in that: In S103, based on the obtained dynamic parameters of the calibration model, a three-dimensional calibration model including time domain, frequency domain and statistical features is established, including: Time domain model, using the least squares method to fit the compensation function of signal rise time, overshoot rate and steady-state error; Frequency domain model, using FFT to analyze harmonic distortion components and generate a frequency response curve correction coefficient table; Statistical model, variance analysis is performed on the results of multiple consecutive tests, and the confidence interval threshold is calculated after eliminating abnormal data points.

7. A chip testing and calibration method according to claim 6, characterized in that: In S104, the real-time test data is compared with the theoretical values ​​of the calibration model item by item, the error sources are identified and a classification process is performed, wherein the classification process includes: Level 1 error: if the deviation is less than 1%, the preset compensation parameters will be automatically called for linear correction. Secondary error: if the deviation of the secondary error is 1% ≤ deviation < 5%, the multi-parameter joint optimization algorithm is triggered to recalculate the signal gain, offset and filter cutoff frequency; Level 3 error: if the deviation of the level 3 error is ≥5%, the test process will be interrupted, the hardware diagnosis mode will be started, the faulty module will be located, and a replacement of spare parts will be prompted.

8. A chip testing and calibration method according to claim 7, characterized in that: In S105, after calibration is completed, boundary condition test cases, extreme load test modes, and aging acceleration test scenarios are loaded to verify the performance consistency of the chip under different working conditions, including: In extreme load testing, a programmable electronic load module is used to simulate a full range of load transitions from no-load to short-circuit, monitor the transient response characteristics of the chip, and use digital twin technology to build a virtual test environment to predict potential failure modes. Arrhenius accelerated aging test was conducted by increasing the ambient temperature to 125°C and applying 1.5 times the rated voltage for 72 hours to achieve a 5-year service life. During the verification process, if parameter drift is found to exceed the tolerance, the self-healing mechanism is automatically triggered. The self-healing mechanism first attempts software compensation. If this fails, the backup circuit module is activated to replace the faulty unit. The final test report integrates 3D thermal imaging images, signal spectrum waterfall diagrams, and video logs of the adjustment process.

9. A chip tester, characterized in that: The method comprises a chip test module, a storage device and a processor, wherein the storage device is used to store a computer program, and the processor runs the computer program to enable the chip test module to perform the chip test calibration method according to any one of claims 1 to 8.

10. A chip tester according to claim 9, characterized in that: The chip testing module includes: The control unit integrates a multi-core processor and an FPGA programmable logic unit. It is used to perform test environment initialization, dynamic parameter acquisition, and calibration model calculations. It can perform full-link diagnosis of the tester's internal functional modules and display fault codes and repair suggestions in real time through status indicators and the touch screen. The signal acquisition and generation unit consists of an ADC / DAC array equipped with a programmable gain amplifier (PGA) and an anti-aliasing filter; The environmental control unit achieves a temperature control accuracy of ±0.1°C through a PID controller, integrates an electromagnetic shielding cavity and active noise cancellation circuit to suppress external interference, and has a built-in non-contact stress detection unit that uses a femtosecond laser and laser interferometer to measure the mechanical stress distribution inside the chip package; A calibration and adjustment unit, which runs a machine learning algorithm to build a 3D calibration model and performs hierarchical error correction; The communication and data management unit is used to achieve real-time upload of test data and remote command reception. The built-in blockchain node generates a unique digital identity for each chip and records data throughout its life cycle. The deployment of a federated learning framework supports multi-terminal collaborative optimization and calibration models.

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