Chip high and low temperature test method based on multi-point acquisition

Abstract

The invention discloses a chip high and low temperature test method based on multi-point acquisition, and the method specifically comprises the following steps: 1, carrying out the real-time data analysis of the temperature of a chip through a dynamic temperature control array, and adjusting the working state of a temperature control unit; 2, constructing a three-dimensional temperature monitoring network, deploying a plurality of high-precision temperature sensors in a three-dimensional space on the surface of the chip and around the chip, capturing the spatial distribution and change of the temperature in real time, and importing three-dimensional temperature data into visual software to generate a real-time three-dimensional temperature distribution diagram; through intelligent control and self-adaptive adjustment, precise control of local temperature is realized, the problem of temperature instability is effectively solved, the accuracy and reliability of testing are improved, meanwhile, an omnibearing temperature monitoring system is constructed, spatial distribution and change of temperature can be captured in real time, and the accuracy and reliability of testing are improved. Powerful support is provided for identifying and correcting temperature nonuniformity, and temperature measurement errors caused by direct contact between the temperature sensor and the chip are avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of chip high and low temperature testing, and in particular relates to a chip high and low temperature testing method based on multi-point acquisition. Background Art

[0002] In the chip temperature detection, multiple acquisition points are used to comprehensively and accurately monitor the temperature changes of the chip in high and low temperature environments, and then the performance and reliability of the chip are evaluated; specifically, this method uses advanced temperature monitoring technology to set multiple acquisition points at different locations of the chip. By real-time monitoring of the temperature data of these acquisition points, the temperature distribution of the chip in extreme temperature environments can be fully understood. This method not only improves the accuracy and reliability of temperature monitoring, but also more accurately reflects the temperature changes of the chip in actual use.

[0003] In the existing test methods, if the temperature stability is poor during the test process, it will lead to inconsistency and unreliability of the test data. Slight temperature fluctuations may have a significant impact on the performance evaluation of the chip, and the uneven temperature distribution on the chip surface will also affect the accuracy of the test results. In actual applications, since temperature changes may cause signal drift or increased noise, there may be errors in the data acquisition process. Therefore, there are great limitations in actual use and there is room for improvement. Summary of the invention

[0004] The purpose of the present invention is to provide a chip high and low temperature test method based on multi-point acquisition to solve the problems of temperature instability, temperature non-uniformity and inaccurate data acquisition mentioned in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a chip high and low temperature test method based on multi-point acquisition, specifically comprising the following steps:

[0006] Step 1: Use a dynamic temperature control array to perform real-time data analysis on the chip temperature and adjust the working state of the temperature control unit;

[0007] Step 2: Build a three-dimensional temperature monitoring network, deploy multiple high-precision temperature sensors on the chip surface and in the surrounding three-dimensional space, capture the spatial distribution and changes of temperature in real time, and import the three-dimensional temperature data into the visualization software to generate a real-time three-dimensional temperature distribution map. By observing and analyzing the temperature distribution map, the area of ​​temperature non-uniformity can be intuitively identified, and corresponding measures can be taken to correct it;

[0008] Step 3: Use non-contact temperature measurement technology to measure the chip surface temperature non-contactly;

[0009] Step 4: Real-time data fusion and verification, fusing the data from multiple temperature sensors and infrared temperature measurement data; using data fusion algorithms, comprehensively considering data from different sources to improve the accuracy and reliability of the overall data;

[0010] Step 5: Establish a real-time feedback mechanism. During the test process, establish a real-time temperature data feedback mechanism; based on the real-time feedback temperature data, dynamically adjust the temperature control array and temperature measurement network to adapt to changes in the test environment, and use the self-learning ability of the machine learning algorithm to continuously learn and analyze the temperature data during the test process.

[0011] As a preferred technical solution in the present invention, the dynamic temperature control array in step one is composed of multiple small independent temperature control units, each temperature control unit performs independent heating or cooling operations according to the real-time temperature of the surrounding environment to achieve precise control of local temperature.

[0012] As a preferred technical solution in the present invention, the independent temperature control unit is integrated with a machine learning algorithm to intelligently control the temperature control array. The machine learning algorithm predicts future temperature trends by analyzing temperature data in real time, and adjusts the working state of the temperature control unit in advance to prevent temperature instability.

[0013] As a preferred technical solution of the present invention, in step 2, the component three-dimensional temperature monitoring network steps are as follows:

[0014] S1. Analyze needs, determine monitoring scope and accuracy, and determine data collection frequency;

[0015] S2. Select the corresponding temperature sensor and adjust the temperature sensor data and distribution position according to the monitoring range;

[0016] S3, build a data acquisition system to convert the sensor output signal into a digital signal;

[0017] S4. Establish a data processing and analysis system, develop data processing software, process and analyze the collected temperature data, such as data smoothing, filtering, and denoising, to achieve data visualization.

[0018] As a preferred technical solution in the present invention, in step 4, the data fusion algorithm includes a weighted average method, a decision tree method, and a machine learning algorithm.

[0019] As a preferred technical solution of the present invention, in step 5, the method for establishing the real-time feedback mechanism includes:

[0020] S1. Define needs and goals: Clarify the purpose of the feedback mechanism, including the expected feedback type, frequency, and target audience, to ensure that the feedback mechanism can meet actual needs;

[0021] S2. Select feedback channels and technologies: Based on organizational characteristics and audience preferences, select appropriate feedback channels (such as instant messaging, online platforms, face-to-face communication, etc.) and technical tools (such as performance management systems, data analysis software, etc.) to quickly deliver and receive feedback;

[0022] S3. Set feedback process and standards: Establish a clear feedback process, including feedback collection, analysis, delivery and response, and set feedback evaluation standards and time requirements to ensure the timeliness and effectiveness of feedback;

[0023] S4. Continuous monitoring and optimization: Establish a monitoring mechanism, regularly evaluate the operating effect of the feedback mechanism, collect user feedback and opinions, and make necessary adjustments and optimizations based on the evaluation results to ensure that the feedback mechanism can continue to meet needs and continuously improve.

[0024] As a preferred technical solution in the present invention, in step five, the temperature control array and the temperature measurement network are dynamically adjusted, the data is analyzed by an intelligent algorithm, the temperature abnormality area is automatically identified, and the adjustment instruction of the temperature control array is triggered. At the same time, the layout of the temperature measurement network is optimized to ensure the accuracy of the data and the comprehensiveness of the coverage, so as to realize the precise control and monitoring of the temperature.

[0025] As a preferred technical solution in the present invention, the intelligent algorithm includes a PID control algorithm, a fuzzy control algorithm, a neural network control algorithm, a predictive control algorithm, and a Fuzzy-PID control algorithm.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] Through intelligent control and adaptive adjustment, precise control of local temperature is achieved, the problem of temperature instability is effectively solved, and the accuracy and reliability of the test are improved. At the same time, a comprehensive temperature monitoring system is built, which can capture the spatial distribution and changes of temperature in real time, providing strong support for identifying and correcting temperature non-uniformity, and avoiding the temperature measurement error caused by direct contact between the temperature sensor and the chip, improving the accuracy of temperature measurement. At the same time, combined with laser ranging technology, the reliability of the temperature measurement results is further ensured, and the accuracy and reliability of the overall data are improved by integrating data from multiple sources, and performing verification and correction. This provides a solid foundation for subsequent test analysis and performance evaluation, and establishes a real-time temperature data feedback mechanism, and dynamically adjusts and optimizes according to the feedback data; this adaptive capability enables the test method to flexibly respond to changes in the test environment and improves the efficiency and accuracy of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The present invention provides a technical solution: a chip high and low temperature test method based on multi-point acquisition, which specifically includes the following steps:

[0031] Step 1: Use a dynamic temperature control array to perform real-time data analysis on the chip temperature and adjust the working state of the temperature control unit;

[0032] Step 2: Build a three-dimensional temperature monitoring network, deploy multiple high-precision temperature sensors on the chip surface and in the surrounding three-dimensional space, capture the spatial distribution and changes of temperature in real time, and import the three-dimensional temperature data into the visualization software to generate a real-time three-dimensional temperature distribution map. By observing and analyzing the temperature distribution map, the area of ​​temperature non-uniformity can be intuitively identified, and corresponding measures can be taken to correct it;

[0033] Step 3: Use non-contact temperature measurement technology to measure the chip surface temperature non-contactly;

[0034] Step 4: Real-time data fusion and verification, fusing the data from multiple temperature sensors and infrared temperature measurement data; using data fusion algorithms, comprehensively considering data from different sources to improve the accuracy and reliability of the overall data;

[0035] Step 5: Establish a real-time feedback mechanism. During the test process, establish a real-time temperature data feedback mechanism; based on the real-time feedback temperature data, dynamically adjust the temperature control array and temperature measurement network to adapt to changes in the test environment, and use the self-learning ability of the machine learning algorithm to continuously learn and analyze the temperature data during the test process.

[0036] In this embodiment, the dynamic temperature control array in step one is composed of a plurality of small independent temperature control units, each of which performs independent heating or cooling operations according to the real-time temperature of the surrounding environment to achieve precise control of local temperature.

[0037] In this embodiment, a machine learning algorithm is integrated in the independent temperature control unit to perform intelligent control of the temperature control array. The machine learning algorithm predicts future temperature trends by analyzing temperature data in real time, and adjusts the working state of the temperature control unit in advance to prevent temperature instability.

[0038] In this embodiment, in step 2, the component three-dimensional temperature monitoring network steps are as follows:

[0039] S1. Analyze needs, determine monitoring scope and accuracy, and determine data collection frequency;

[0040] S2. Select the corresponding temperature sensor and adjust the temperature sensor data and distribution position according to the monitoring range;

[0041] S3, build a data acquisition system to convert the sensor output signal into a digital signal;

[0042] S4. Establish a data processing and analysis system, develop data processing software, process and analyze the collected temperature data, such as data smoothing, filtering, and denoising, to achieve data visualization.

[0043] In this embodiment, in step 4, the data fusion algorithm includes a weighted average method, a decision tree method, and a machine learning algorithm.

[0044] In this embodiment, in step 5, the method for establishing the real-time feedback mechanism includes:

[0045] S1. Define needs and goals: Clarify the purpose of the feedback mechanism, including the expected feedback type, frequency, and target audience, to ensure that the feedback mechanism can meet actual needs;

[0046] S2. Select feedback channels and technologies: Based on organizational characteristics and audience preferences, select appropriate feedback channels (such as instant messaging, online platforms, face-to-face communication, etc.) and technical tools (such as performance management systems, data analysis software, etc.) to quickly deliver and receive feedback;

[0047] S3. Set feedback process and standards: Establish a clear feedback process, including feedback collection, analysis, delivery and response, and set feedback evaluation standards and time requirements to ensure the timeliness and effectiveness of feedback;

[0048] S4. Continuous monitoring and optimization: Establish a monitoring mechanism, regularly evaluate the operating effect of the feedback mechanism, collect user feedback and opinions, and make necessary adjustments and optimizations based on the evaluation results to ensure that the feedback mechanism can continue to meet needs and continuously improve.

[0049] In this embodiment, in step five, the temperature control array and the temperature measurement network are dynamically adjusted, the data is analyzed by an intelligent algorithm, the temperature abnormality area is automatically identified, and the adjustment instruction of the temperature control array is triggered. At the same time, the layout of the temperature measurement network is optimized to ensure the accuracy of the data and the comprehensiveness of the coverage, so as to achieve precise control and monitoring of the temperature.

[0050] In this embodiment, the intelligent algorithms include PID control algorithm, fuzzy control algorithm, neural network control algorithm, predictive control algorithm, and Fuzzy-PID control algorithm.

[0051] Although embodiments of the present invention have been shown and described (see the above detailed description for details), it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A chip high and low temperature test method based on multi-point acquisition, characterized in that: The specific steps include: Step 1: Use a dynamic temperature control array to perform real-time data analysis on the chip temperature and adjust the working state of the temperature control unit; Step 2: Build a three-dimensional temperature monitoring network, deploy multiple high-precision temperature sensors on the chip surface and in the surrounding three-dimensional space, capture the spatial distribution and changes of temperature in real time, and import the three-dimensional temperature data into the visualization software to generate a real-time three-dimensional temperature distribution map; Step 3: Use non-contact temperature measurement technology to measure the chip surface temperature non-contactly; Step 4: Real-time data fusion and verification, fusing the data from multiple temperature sensors and infrared temperature measurement data; using data fusion algorithms, comprehensively considering data from different sources to improve the accuracy and reliability of the overall data; Step 5: Establish a real-time feedback mechanism. During the test process, establish a real-time temperature data feedback mechanism; based on the real-time feedback temperature data, dynamically adjust the temperature control array and temperature measurement network to adapt to changes in the test environment, and use the self-learning ability of the machine learning algorithm to continuously learn and analyze the temperature data during the test process.

2. A chip high and low temperature test method based on multi-point acquisition according to claim 1, characterized in that: The dynamic temperature control array in step 1 is composed of a plurality of small independent temperature control units, each of which performs independent heating or cooling operations according to the real-time temperature of the surrounding environment to achieve precise control of local temperature.

3. A chip high and low temperature test method based on multi-point acquisition according to claim 2, characterized in that: The independent temperature control unit is integrated with a machine learning algorithm to intelligently control the temperature control array. The machine learning algorithm predicts future temperature trends by analyzing temperature data in real time, and adjusts the working state of the temperature control unit in advance to prevent temperature instability.

4. The chip high and low temperature test method based on multi-point acquisition according to claim 1, characterized in that: In step 2, the steps of the component three-dimensional temperature monitoring network are as follows: S1. Analyze needs, determine monitoring scope and accuracy, and determine data collection frequency; S2. Select the corresponding temperature sensor and adjust the temperature sensor data and distribution position according to the monitoring range; S3, build a data acquisition system to convert the sensor output signal into a digital signal; S4. Establish a data processing and analysis system, develop data processing software, process and analyze the collected temperature data, and realize data visualization.

5. The chip high and low temperature test method based on multi-point acquisition according to claim 1, characterized in that: In the step 4, the data fusion algorithm includes a weighted average method, a decision tree method, and a machine learning algorithm.

6. The chip high and low temperature test method based on multi-point acquisition according to claim 1, characterized in that: In step 5, the method for establishing the real-time feedback mechanism includes: S1. Define needs and goals: Clarify the purpose of the feedback mechanism, including the expected feedback type, frequency, and target audience, to ensure that the feedback mechanism can meet actual needs; S2. Select feedback channels and technologies: Based on organizational characteristics and audience preferences, select appropriate feedback channels and technical tools to quickly deliver and receive feedback; S3. Set feedback process and standards: Establish a clear feedback process, including feedback collection, analysis, delivery and response, and set feedback evaluation standards and time requirements to ensure the timeliness and effectiveness of feedback; S4. Continuous monitoring and optimization: Establish a monitoring mechanism, regularly evaluate the operating effect of the feedback mechanism, collect user feedback and opinions, and make necessary adjustments and optimizations based on the evaluation results to ensure that the feedback mechanism can continue to meet needs and continuously improve.

7. The chip high and low temperature test method based on multi-point acquisition according to claim 1 is characterized in that: In step five, the temperature control array and the temperature measurement network are dynamically adjusted, the data is analyzed by an intelligent algorithm, the temperature abnormality area is automatically identified, and the adjustment instructions of the temperature control array are triggered. At the same time, the layout of the temperature measurement network is optimized to ensure the accuracy of the data and the comprehensiveness of the coverage, so as to achieve precise control and monitoring of the temperature.

8. A chip high and low temperature test method based on multi-point acquisition according to claim 7, characterized in that: The intelligent algorithms include PID control algorithm, fuzzy control algorithm, neural network control algorithm, predictive control algorithm, and Fuzzy-PID control algorithm.

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