Lightweight temperature control method based on semiconductor test system

Abstract

The invention relates to the technical field of temperature control testing, in particular to a light-weight temperature control method based on a semiconductor testing system. The method comprises the following steps: obtaining the position of a semiconductor chip test area; performing chip temperature distribution data acquisition on the semiconductor chip test area position to obtain real-time temperature distribution data of the semiconductor chip; performing heat conduction model construction on the real-time temperature distribution data of the semiconductor chip to generate a high-dimensional heat conduction model; performing chip temperature scene division on the real-time temperature distribution data of the semiconductor chip by using a high-dimensional heat conduction model to generate a first chip temperature scene and a second chip temperature scene; and performing multi-point temperature control distribution optimization on the first chip temperature scene and the second chip temperature scene to generate optimized temperature control distribution data. According to the invention, through high-precision data acquisition, multi-region optimization, adaptive thermal compensation, scene test and intelligent management, the efficiency and comprehensiveness of light-weight temperature control of the semiconductor test system are improved.

Description

Technical Field

[0001] The present invention relates to the field of temperature control testing technology, and in particular to a lightweight temperature control method based on a semiconductor testing system. Background Art

[0002] Traditional temperature control methods mainly rely on large-scale constant temperature equipment to achieve temperature control by heating or cooling. However, this method usually has problems such as large equipment size, high energy consumption, and slow adjustment speed, which makes it difficult to meet the modern semiconductor industry's needs for lightweight, precision, and high efficiency. With the continuous advancement of semiconductor technology, lightweight temperature control methods have gradually emerged. Since the 1990s, miniaturized and modular temperature control devices have entered the market, which have achieved faster temperature regulation by integrating micro-refrigeration elements and efficient heat transfer materials. In the 21st century, the introduction of thermoelectric cooling technology (such as the Peltier effect) and microfluidic technology has provided more flexible solutions for lightweight temperature control methods. These technologies enable temperature control systems to achieve local precise temperature control and adapt to complex test environments. In recent years, with the popularization of artificial intelligence and intelligent control algorithms, temperature control technology has become more intelligent. Temperature prediction and adaptive control technology based on real-time data enable semiconductor test systems to maintain high stability and efficiency under complex operating conditions. However, current traditional temperature control methods usually adopt a unified control strategy for the entire chip area, ignoring the differences in temperature requirements in different areas. At the same time, it is difficult to make dynamic adjustments based on real-time temperature changes, resulting in low efficiency and comprehensiveness of lightweight temperature control in semiconductor test systems. Summary of the invention

[0003] Based on this, it is necessary to provide a lightweight temperature control method based on a semiconductor testing system to solve at least one of the above technical problems.

[0004] To achieve the above object, a lightweight temperature control method based on a semiconductor test system is provided, the method comprising the following steps: Step S1: obtaining the location of the semiconductor chip test area; collecting chip temperature distribution data at the location of the semiconductor chip test area to obtain real-time temperature distribution data of the semiconductor chip; constructing a heat conduction model for the real-time temperature distribution data of the semiconductor chip to generate a high-dimensional heat conduction model; Step S2: using a high-dimensional heat conduction model to divide the real-time temperature distribution data of the semiconductor chip into chip temperature scenarios, generating a first chip temperature scenario and a second chip temperature scenario; performing multi-point temperature control distribution optimization on the first chip temperature scenario and the second chip temperature scenario to generate optimized temperature control distribution data; performing chip adaptive thermal compensation feedback on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data, thereby generating chip thermal compensation feedback data; Step S3: Perform scenario-based testing on the optimized temperature control distribution data based on the chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; perform optimal temperature control curve screening on the semiconductor chip scenario temperature control test data to obtain the semiconductor chip scenario temperature control optimal curve; Step S4: construct a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scene to generate a semiconductor chip scene temperature control strategy; perform temperature control management on the semiconductor chip scene temperature control strategy to generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

[0005] The present invention achieves accurate positioning of key test points of the chip by acquiring the position of the chip test area, avoiding invalid temperature control operations. Through real-time temperature distribution data collection, thermal state monitoring of the entire chip range is provided to avoid temperature control errors caused by single-point data. High-dimensional modeling is used to improve the analytical ability of heat conduction behavior, providing a scientific basis for subsequent temperature control optimization. By dividing the chip temperature scene, the temperature control requirements of different regions are distinguished to avoid the inefficiency caused by global unified temperature control. Based on multi-point distribution optimization, the spatial resolution of the temperature control operation is refined and the temperature control effect is improved. The thermal compensation feedback mechanism responds to temperature changes in real time and enhances the stability of the system in a rapid thermal fluctuation environment. By testing and optimizing data in actual scenarios, the feasibility and effectiveness of the temperature control strategy are ensured. The temperature control curve with the best adaptability is selected to minimize energy consumption and improve temperature control accuracy. The strategy decision based on the optimized test data is more scientific and reasonable, which improves the intelligence level of the entire system. The strategy formulated based on the optimal temperature control curve is global and systematic, which can ensure the standardization and uniformity of temperature control execution. Through intelligent temperature control management, the need for manual intervention is reduced and the system operation process is simplified. The execution of the optimization strategy significantly reduces the energy consumption of the equipment and improves the resource utilization of the test system. The temperature control management data finally generated has both execution efficiency and data accuracy, laying a solid foundation for the implementation of lightweight temperature control. Therefore, the present invention improves the efficiency and comprehensiveness of lightweight temperature control of semiconductor test systems through high-precision data acquisition, multi-zone optimization, adaptive thermal compensation, scenario-based testing and intelligent management.

[0006] Preferably, step S1 comprises the following steps: Step S11: obtaining the location of the semiconductor chip test area based on the semiconductor test system; Step S12: deploying temperature sensors at the semiconductor chip test area, and collecting semiconductor chip temperature distribution data at the semiconductor chip test area based on the deployed temperature sensors to obtain real-time temperature distribution data of the semiconductor chip; Step S13: determining key thermodynamic parameters based on the material properties and structural design of the semiconductor chip to obtain the thermodynamic parameters of the semiconductor chip; performing chip spatial temperature distribution analysis on the thermodynamic parameters of the semiconductor chip using the real-time temperature distribution data of the semiconductor chip to generate a field distribution diagram of the thermodynamic parameters of the semiconductor chip; Step S14: construct a high-level heat conduction model based on the thermodynamic parameter field distribution map of the semiconductor chip to generate a preliminary high-dimensional heat conduction model; calibrate the preliminary high-dimensional heat conduction model to generate high-dimensional heat conduction model calibration data; optimize the preliminary high-dimensional heat conduction model through the high-dimensional heat conduction model calibration data to generate a high-dimensional heat conduction model.

[0007] The present invention can collect the temperature distribution data of the chip in real time and accurately reflect the temperature condition of the chip by deploying temperature sensors in the semiconductor chip test area. This provides key temperature data support for subsequent thermodynamic analysis and ensures the accuracy of the analysis. According to the material properties and structural design of the semiconductor chip, the key thermodynamic parameters related to heat conduction can be accurately calculated. This analysis helps to deeply understand the thermal behavior of the chip and avoid performance degradation or damage due to overheating. The real-time temperature distribution data is used to perform spatial distribution analysis on the thermodynamic parameters of the chip to generate an accurate thermodynamic parameter field distribution map. This process helps to reveal the temperature differences in different parts of the chip and assists in optimizing the design and material selection. By constructing a high-level heat conduction model, the heat conduction process inside the chip can be simulated, and the model can be further calibrated and optimized. The optimized high-dimensional heat conduction model can more accurately predict the temperature distribution of the chip under different working conditions and improve the thermal management efficiency of the chip.

[0008] Preferably, constructing a high-dimensional heat conduction model based on the thermodynamic parameter field distribution map of the semiconductor chip includes: Based on the heat conduction equation, a mathematical model is established for the thermodynamic parameter field distribution diagram of the semiconductor chip to generate a thermal conduction mathematical model of the semiconductor chip. The formula for establishing the mathematical model is as follows: In the formula, The temperature at a point Over time The rate of change, Expressed as the divergence of the heat flux density, is represented as the gradient operator, Expressed as thermal conductivity, Expressed as temperature, Expressed as heat capacity, is represented as a heat source term; The dimensional boundary conditions are introduced into the semiconductor chip heat conduction mathematical model to generate the dimensional boundary conditions of the semiconductor chip heat conduction, wherein the dimensional boundary conditions include: the temperature value of the fixed chip boundary, the heat flux density of the given boundary, and the heat exchange of the environment to the chip boundary; The semiconductor chip heat conduction dimensional boundary conditions are used to discretize the semiconductor chip heat conduction mathematical model and generate a preliminary high-dimensional heat conduction model.

[0009] The present invention uses the heat conduction equation Mathematical modeling of the temperature distribution of semiconductor chips can accurately describe the heat conduction process within the chip. This equation combines thermal conductivity ,temperature , heat source term and heat capacity , providing a theoretical framework for analyzing the thermal behavior of chips. Introducing dimensional boundary conditions (such as fixed temperature, given heat flux density, ambient heat exchange, etc.) during the modeling process enables the model to more realistically reflect the heat exchange process between the chip and the external environment. This multi-dimensional analysis enhances the accuracy of the heat conduction model and can better adapt to the complex boundary conditions in practical applications. By combining the heat conduction equation with the dimensional boundary conditions, the thermal behavior of semiconductor chips under different working conditions can be predicted. This helps to optimize chip design, ensure its thermal management capabilities during operation, and avoid performance degradation or chip damage caused by overheating. Through discretization processing, a preliminary high-dimensional heat conduction model can be obtained, and the accuracy of the prediction can be further improved through model calibration and optimization. This high-dimensional model can not only capture the complex heat conduction process inside the chip, but also provide accurate temperature distribution predictions under a variety of working environments.

[0010] Preferably, step S2 comprises the following steps: Step S21: using a high-dimensional heat conduction model to dynamically predict chip thermal effects on the real-time temperature distribution data of the semiconductor chip, and generating chip thermal effect dynamic prediction data; performing time-step chip temperature change analysis on the chip thermal effect dynamic prediction data, and generating time-step chip temperature change data; Step S22: dividing the chip temperature change data of the time step into chip temperature scenarios based on a preset temperature change threshold value to generate a first chip temperature scenario and a second chip temperature scenario; performing chip temperature control demand analysis on the real-time temperature distribution data of the semiconductor chip based on the first chip temperature scenario and the second chip temperature scenario to generate chip temperature control demand data; Step S23: Optimizing the multi-point temperature control distribution of the first chip temperature scenario and the second chip temperature scenario according to the chip temperature control demand data to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data; Step S24: Perform chip adaptive thermal compensation on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data to generate chip thermal compensation data; perform compensation feedback on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data through the chip thermal compensation data, thereby generating chip thermal compensation feedback data.

[0011] The present invention uses a high-dimensional heat conduction model to dynamically predict the thermal effect of the real-time temperature distribution data of the semiconductor chip, which can monitor the temperature change of the chip in real time and predict the future thermal behavior. This process can provide an accurate temperature change trend and provide data support for subsequent temperature control and thermal management. By analyzing the chip temperature change of the chip thermal effect dynamic prediction data in time step, the temperature change of the chip at different time points can be accurately grasped. This analysis helps to identify the law of temperature fluctuations, timely discover potential overheating risks, and warn and adjust the chip. Through the threshold analysis of the temperature change data, the temperature change of the chip can be divided into different temperature scenes (such as the first chip temperature scene and the second chip temperature scene). This scene division helps to understand the temperature control requirements under different working conditions more carefully, and then provide a more targeted solution for the thermal management of the chip. Based on the chip temperature control demand data, the temperature control distribution under different temperature scenes is optimized. By optimizing the temperature control distribution data, temperature control can be adjusted in different areas to ensure that the temperature of each part of the chip is balanced, avoid overheating or uneven temperature problems, and improve the heat dissipation performance and overall stability of the chip. By adaptively compensating the chip's temperature control optimization data, it can dynamically adjust according to actual temperature changes, thereby achieving more accurate temperature control management. The introduction of the compensation feedback mechanism further improves the system's adaptability and temperature control effect, allowing the chip to maintain a stable temperature under different environments and workloads.

[0012] Preferably, step S23 includes the following steps: Step S231: measuring the temperature distribution of the first chip temperature scene and the second chip temperature scene according to the chip temperature control requirement data to generate initial temperature field data of the first scene and initial temperature field data of the second scene; performing data fitting on the initial temperature field data of the first scene and the initial temperature field data of the second scene to generate initial temperature field distribution data; Step S232: performing high temperature scene multi-point controller parameter configuration on the initial temperature field data of the first scene to generate first scene multi-point controller configuration parameters, wherein the high temperature scene multi-point controller parameter configuration includes power adjustment range and response time; Step S233: performing low-temperature scene multi-point controller parameter configuration on the initial temperature field data of the second scene to generate second scene multi-point controller configuration parameters, wherein the low-temperature scene multi-point controller parameter configuration includes cooling efficiency and steady-state maintenance capability; Step S234: Perform real-time temperature control signal distribution on the initial temperature field distribution data through the first scene multi-point controller configuration parameters and the second scene multi-point controller configuration parameters to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

[0013] The present invention can generate accurate initial temperature field distribution data by measuring the temperature distribution of the first chip temperature scene and the second chip temperature scene, and fitting the initial temperature field data. These data provide a basis for subsequent temperature control optimization, ensuring that the temperature control strategy can be adjusted according to the actual temperature distribution of the chip. By configuring multi-point controller parameters for high temperature and low temperature scenes respectively, it is ensured that the temperature control system of each scene can operate within the optimal range. The controller parameter configuration of the high temperature scene (such as power adjustment range and response time) can respond quickly to temperature changes and avoid overheating; while the controller parameter configuration of the low temperature scene (such as cooling efficiency and steady-state maintenance ability) can effectively maintain a low temperature and maintain stability, avoiding chip overcooling or uneven temperature. By generating optimized temperature control distribution data, accurate temperature control of various parts of the chip can be achieved. These optimized temperature control data can effectively adjust the temperature distribution of each temperature scene, ensure that different areas of the chip can be maintained within a suitable temperature range, and improve heat dissipation performance and overall thermal management efficiency. Through fine multi-point controller parameter configuration, the response speed and stability of the temperature control system can be significantly improved. In high-temperature scenarios, fast response and power regulation capabilities can effectively prevent the chip from overheating, while in low-temperature scenarios, the optimization of cooling efficiency and steady-state maintenance capabilities ensures that the chip can continue to work in a low-temperature state, avoiding unstable performance caused by excessive temperature fluctuations.

[0014] Preferably, distributing the real-time temperature control signal to the initial temperature field distribution data by configuring the first scene multi-point controller and the second scene multi-point controller includes the following: The control signal is generated by the initial temperature field distribution data through the configuration parameters of the first scene multi-point controller and the configuration parameters of the second scene multi-point controller to obtain the first scene control signal and the second scene control signal; Performing signal control area identification on the first scene control signal and the second scene control signal to obtain the first scene control signal area and the second scene control signal area; performing control signal area overlap on the first scene control signal area and the second scene control signal area to obtain a control boundary area signal; Based on the boundary conditions of the semiconductor chip's thermal conduction dimension, the control boundary area signal is adjusted to generate a control boundary area adjustment signal; according to the control boundary area adjustment signal, the first scene control signal area and the second scene control signal area are distributed to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

[0015] The present invention can generate control signals for different temperature scenes by applying the configuration parameters of the multi-point controller of the first scene and the second scene. These control signals are generated in real time according to the actual temperature field distribution data, ensuring that the temperature control system can accurately respond to the thermal effects of the chip under different temperature conditions. By performing regional identification and overlap of the control signals, it can be ensured that the temperature control signals of the two scenes will not conflict or interfere in the boundary area. The process of regional identification helps to determine which areas require specific temperature control adjustments, and the overlap optimization of the control signal area ensures the coordination of temperature control in various areas of the chip, avoiding thermal failure problems caused by inconsistent temperature control. By adjusting the signals of the boundary area based on the boundary conditions of the thermal conduction dimension of the semiconductor chip, refined management of the control signals can be achieved. In the boundary area, the temperature has a transitional change due to the heat conduction effect, so adjusting the signals of these areas helps to optimize the temperature control effect and avoid temperature instability due to excessive or insufficient temperature control. By adjusting and optimizing the distribution of the control signal area, accurate optimized temperature control distribution data can be generated in the end. These data ensure that the chip can achieve the best temperature control effect in two different temperature scenarios, ensure uniform and stable temperature distribution, and effectively prevent the chip from being damaged by thermal effects caused by excessively high or low temperatures. Through precise temperature control signal adjustment and signal processing in the interface area, the temperature control accuracy and stability of the system in complex temperature scenarios can be improved. The temperature control strategy for each scenario is optimized, which can not only cope with extreme conditions of high or low temperatures, but also ensure that the thermal effects of the chip during operation are effectively suppressed. Through signal adjustment in the interface area, the temperature fluctuations and thermal stress between the two temperature control areas are effectively reduced. This is crucial for the long-term stable operation of semiconductor chips, because excessive thermal stress can cause material fatigue or damage, affecting the performance and life of the chip. This process can not only optimize the temperature control distribution in static conditions, but also respond quickly to dynamic temperature changes. Through real-time signal distribution and interface signal adjustment, the system can adapt to different working environments and workload conditions, ensuring that the chip always maintains the best temperature control state under changing conditions.

[0016] Preferably, step S24 includes the following steps: Step S241: performing thermal behavior analysis on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data to generate chip thermal behavior data; Step S242: performing closed-loop control on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data through the chip thermal behavior data to generate chip scenario temperature control closed-loop control data; Step S243: using an adaptive thermal compensation formula to perform real-time thermal compensation on the chip scene temperature control closed-loop control data to generate chip thermal compensation data; Step S244: Compensate and feedback the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data using chip thermal compensation data, thereby generating chip thermal compensation feedback data.

[0017] The present invention can obtain detailed data about the thermal response of the chip under different temperature scenarios by performing thermal behavior analysis on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data. This helps to fully understand the thermal characteristics of the chip under dynamic temperature control, such as thermal response time, thermal fluctuations, and thermal effects of different areas of the chip. This analysis provides basic data for subsequent thermal compensation and optimization control. Based on the chip thermal behavior data, temperature control closed-loop control is performed. This step ensures that the temperature is maintained within a preset ideal range by adjusting the temperature control strategy in real time. Closed-loop control enables the system to respond to changes in the thermal state of the chip in real time and maintain a stable temperature environment by automatically adjusting the temperature control strategy. Closed-loop control improves the robustness and response speed of the system, especially when facing external environment or load fluctuations, it can quickly correct temperature deviations to avoid chip damage caused by overheating or temperature instability. The chip scene temperature control closed-loop control data is thermally compensated in real time using an adaptive thermal compensation formula. This means that the system can dynamically adjust the compensation parameters according to the specific circumstances of temperature changes during actual operation. This adaptive capability ensures the temperature control accuracy of the chip under different working conditions. Regardless of whether the load changes or the external environment changes, the temperature can be kept within a safe range to avoid negative impacts of temperature fluctuations on chip performance. Compensation feedback is performed through chip thermal compensation data to further optimize the temperature control optimization data for the first chip scenario and the temperature control optimization data for the second chip scenario. This step ensures that the feedback mechanism during the thermal compensation process can continuously optimize the temperature control distribution, further enhancing the system's adaptive capability. This feedback mechanism enables the system to continuously learn and adjust the temperature control strategy, gradually improving the temperature control effect, thereby extending the service life of the chip and improving the long-term stability of the system.

[0018] Preferably, the adaptive thermal compensation formula in step S243 is as follows: In the formula, Represented as the current time and location The compensation temperature, Represented as the current time and location The temperature error, Expressed as the compensation gain coefficient, Expressed as the temperature history integration coefficient, Represented as the current time and location The actual measured temperature, Expressed as the feedback gain coefficient.

[0019] This invention analyzes and integrates an adaptive thermal compensation formula. The purpose of this adaptive thermal compensation formula is to dynamically compensate for the temperature error of the temperature control system to ensure that the chip can maintain stable temperature control while maintaining high performance. It achieves adaptive adjustment through three main components (current error, historical error integration, and real-time feedback). The current temperature error term in the formula is: It directly reflects the temperature error of the chip at the current moment. The greater the temperature deviation, the greater the compensation signal. is the difference between the actual temperature and the target temperature, which determines the intensity of compensation required. This term enables the system to respond to temperature errors in real time, ensuring that the chip temperature can quickly approach the target value. To adjust the response strength, ensure that the system can cope with sudden temperature fluctuations and prevent overcompensation. Historical temperature error integral term: Represents the accumulation of temperature errors over a period of time. It considers the impact of historical errors on current compensation through integration. The historical integral term can reduce the reaction delay of the temperature control system, avoid overreaction to instantaneous errors, and smooth the entire temperature control process. This avoids oscillation or instability caused by too fast response of the system. Real-time temperature feedback term: Indicates that the compensation amount is adjusted based on the real-time temperature feedback signal. Feedback signal It is the actual temperature detected by the sensor, reflecting the real situation of the chip at a certain moment. The real-time feedback term is used to modify the temperature control strategy to ensure that the system dynamically adjusts the control signal according to the actual chip temperature to avoid the situation where the theoretical prediction does not match the actual situation. Feedback enables the temperature control system to cope with external changes or internal uncertainties. , the contribution of the feedback signal to the compensation can be flexibly adjusted to ensure more efficient and accurate compensation. When using the conventional adaptive thermal compensation formula in the field, it can be obtained at the current time and location By applying the adaptive thermal compensation formula provided by the present invention, the compensation temperature at the current time can be calculated more accurately. and location The formula uses the real-time temperature error to drive a fast compensation response, and uses historical errors and feedback to smooth the control process, ensuring that the temperature control process is both fast and accurate. The integral term of the historical temperature error avoids the system from overreacting to short-term fluctuations, thereby improving the stability of the system. ), which can self-adjust according to the real-time working status of the chip, changes in the external environment and other factors to ensure that the system always maintains optimal performance under different working conditions.

[0020] Preferably, step S3 comprises the following steps: Step S31: performing scenario-based testing on the optimized temperature control distribution data based on chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; Step S32: converting the semiconductor chip scene temperature control test data into a temperature control curve to generate a semiconductor chip scene temperature control curve; calibrating the semiconductor chip scene temperature control curve to generate a semiconductor chip scene temperature control calibration curve; Step S33: Screening the optimal temperature control curve for the semiconductor chip scene temperature control calibration curve, thereby obtaining the semiconductor chip scene temperature control optimal curve.

[0021] The present invention performs scenario testing on the optimized temperature control distribution data through chip thermal compensation feedback data, thereby generating semiconductor chip scenario temperature control test data. The beneficial effect of this step is to apply the optimized temperature control data to the actual test scenario to ensure its effectiveness under different environmental conditions and workloads. Through scenario testing, the accuracy and stability of the temperature control strategy can be verified in an environment closer to actual use, potential temperature control problems can be found and targeted adjustments can be made to ensure that the system can maintain efficient thermal management performance in actual applications. The semiconductor chip scenario temperature control test data is converted into a temperature control curve to generate a temperature control curve, which is then calibrated. The beneficial effect of this process is that through conversion and calibration, the temperature control curve is ensured to accurately reflect the thermal behavior of the chip under actual working conditions. The calibrated temperature control curve can provide a more accurate temperature control reference to avoid temperature control failure or instability caused by inaccurate initial curves. This process helps to improve the accuracy of temperature control and ensure that the chip can work within the optimal temperature range, thereby reducing the impact of overheating or temperature fluctuations on chip performance and life. By screening the optimal temperature control curve for the temperature control calibration curve, the optimal curve for semiconductor chip scenario temperature control is obtained. The beneficial effect of this step is to select the best temperature control strategy from multiple temperature control schemes to ensure that the chip always maintains the most suitable operating temperature range. This not only improves the efficiency of temperature control, but also further optimizes energy efficiency, reduces energy consumption, and ensures that the chip can maintain high performance and stability under various environmental conditions.

[0022] Preferably, step S4 comprises the following steps: Step S41: constructing a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scenario to generate a temperature control strategy for the semiconductor chip scenario; Step S42: Upload the semiconductor chip scene temperature control strategy to the cloud platform for automatic push, and generate automatic temperature control strategy deployment data; perform temperature control management on the automatic temperature control strategy deployment data, and generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

[0023] The present invention generates a temperature control strategy for a specific scenario by constructing a temperature control strategy based on an optimal temperature control curve. The beneficial effect of this process is to form a temperature control strategy for different working states through precise data drive, ensure that the temperature control strategy can respond to the thermal behavior of the chip in real time, and ensure that the chip maintains the optimal temperature under various workloads. According to actual needs, a customized temperature control solution is designed for the chip, and the temperature control strategy can be adjusted according to factors such as load and ambient temperature, thereby further improving the temperature control accuracy and reducing energy consumption. The construction of the temperature control strategy will enable each chip to achieve the best thermal management effect based on the actual environment and usage. The constructed semiconductor chip scene temperature control strategy is uploaded to the cloud platform for automatic deployment. The beneficial effect of this step is that, by utilizing the computing and distribution capabilities of the cloud platform, the latest temperature control strategy can be pushed to each test system in real time, achieving rapid policy updates and full network coverage, and ensuring that each chip or system always applies the latest temperature control strategy. The automated deployment and push of the temperature control strategy can accelerate the update and iteration of the chip temperature control system, so that when the environment changes or the load changes, the system can adjust the strategy in time to ensure that the chip temperature is always maintained within the optimal range. Manage the deployment data of automated temperature control strategies and generate semiconductor chip scenario temperature control management data. The beneficial effect of this process is that through the continuous management of temperature control strategies, the effect of strategy execution can be monitored and necessary adjustments can be made. The generated management data can help engineers monitor the performance and temperature status of the temperature control system and provide a basis for subsequent optimization work. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a lightweight temperature control method based on a semiconductor test system; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a lightweight temperature control method based on a semiconductor test system, the method comprising the following steps: Step S1: obtaining the location of the semiconductor chip test area; collecting chip temperature distribution data at the location of the semiconductor chip test area to obtain real-time temperature distribution data of the semiconductor chip; constructing a heat conduction model for the real-time temperature distribution data of the semiconductor chip to generate a high-dimensional heat conduction model; Step S2: using a high-dimensional heat conduction model to divide the real-time temperature distribution data of the semiconductor chip into chip temperature scenarios, generating a first chip temperature scenario and a second chip temperature scenario; performing multi-point temperature control distribution optimization on the first chip temperature scenario and the second chip temperature scenario to generate optimized temperature control distribution data; performing chip adaptive thermal compensation feedback on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data, thereby generating chip thermal compensation feedback data; Step S3: Perform scenario-based testing on the optimized temperature control distribution data based on the chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; perform optimal temperature control curve screening on the semiconductor chip scenario temperature control test data to obtain the semiconductor chip scenario temperature control optimal curve; Step S4: construct a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scene to generate a semiconductor chip scene temperature control strategy; perform temperature control management on the semiconductor chip scene temperature control strategy to generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

[0029] The present invention achieves accurate positioning of key test points of the chip by acquiring the position of the chip test area, avoiding invalid temperature control operations. Through real-time temperature distribution data collection, thermal state monitoring of the entire chip range is provided to avoid temperature control errors caused by single-point data. High-dimensional modeling is used to improve the analytical ability of heat conduction behavior, providing a scientific basis for subsequent temperature control optimization. By dividing the chip temperature scene, the temperature control requirements of different regions are distinguished to avoid the inefficiency caused by global unified temperature control. Based on multi-point distribution optimization, the spatial resolution of the temperature control operation is refined and the temperature control effect is improved. The thermal compensation feedback mechanism responds to temperature changes in real time and enhances the stability of the system in a rapid thermal fluctuation environment. By testing and optimizing data in actual scenarios, the feasibility and effectiveness of the temperature control strategy are ensured. The temperature control curve with the best adaptability is selected to minimize energy consumption and improve temperature control accuracy. The strategy decision based on the optimized test data is more scientific and reasonable, which improves the intelligence level of the entire system. The strategy formulated based on the optimal temperature control curve is global and systematic, which can ensure the standardization and uniformity of temperature control execution. Through intelligent temperature control management, the need for manual intervention is reduced and the system operation process is simplified. The execution of the optimization strategy significantly reduces the energy consumption of the equipment and improves the resource utilization of the test system. The temperature control management data finally generated has both execution efficiency and data accuracy, laying a solid foundation for the implementation of lightweight temperature control. Therefore, the present invention improves the efficiency and comprehensiveness of lightweight temperature control of semiconductor test systems through high-precision data acquisition, multi-zone optimization, adaptive thermal compensation, scenario-based testing and intelligent management.

[0030] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a lightweight temperature control method based on a semiconductor test system according to the present invention. In this example, the lightweight temperature control method based on a semiconductor test system includes the following steps: Step S1: obtaining the location of the semiconductor chip test area; collecting chip temperature distribution data at the location of the semiconductor chip test area to obtain real-time temperature distribution data of the semiconductor chip; constructing a heat conduction model for the real-time temperature distribution data of the semiconductor chip to generate a high-dimensional heat conduction model; In an embodiment of the present invention, the test area of ​​the chip is determined, including the core functional module, the peripheral circuit area, the package interface area, etc. These areas are the focus of thermal management. Use high-precision positioning tools, such as a micropositioner or a thermal imager, to accurately calibrate the physical position of the chip test area. Mark the test area position into the device database to provide a basis for subsequent data acquisition and analysis. The data marking format includes the two-dimensional plane coordinates and three-dimensional geometric position information of the test area. Arrange high-precision temperature sensors (such as thermocouples, infrared thermal imaging sensors) in the chip test area. The sensor layout density should meet the monitoring requirements of the high-heating area of ​​the chip, and it is recommended to arrange 1 sensor point per square millimeter. Ensure that the response time of the sensor is within 1 millisecond to meet the capture requirements of real-time temperature changes. Configure a high-frequency data acquisition system, and set the sampling frequency to 10 kHz to capture small fluctuations in temperature. The system must have data caching and real-time transmission functions to ensure that important information is not lost when the data flow is large. When the chip is in working state, start the data acquisition system to record the real-time temperature distribution data of the chip test area. According to Fourier's law of heat conduction, the basic equation of heat conduction of the chip is established, and the thermal physical parameters of the chip material (such as thermal conductivity and specific heat capacity of silicon) are used to parameterize the heat conduction model. Combined with the actual temperature distribution data, the heat source intensity and boundary conditions (such as natural convection and radiation) are calibrated. The heat conduction equation is discretized using the finite element method (FEM) to generate a spatially distributed grid model. The grid accuracy is recommended to be set to 0.1 microns. The time dimension is subdivided, and the time step is recommended to be set to 1 millisecond to achieve dynamic simulation. High-performance computing (HPC) is used to numerically solve the heat conduction model and calculate the dynamic temperature distribution of the chip. The calculation results are compared with the real-time temperature data to verify the model accuracy; the target error should be controlled within ±2°C. Generate high-dimensional heat conduction model data, including temperature distribution matrices in space and time.

[0031] Step S2: using a high-dimensional heat conduction model to divide the real-time temperature distribution data of the semiconductor chip into chip temperature scenarios, generating a first chip temperature scenario and a second chip temperature scenario; performing multi-point temperature control distribution optimization on the first chip temperature scenario and the second chip temperature scenario to generate optimized temperature control distribution data; performing chip adaptive thermal compensation feedback on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data, thereby generating chip thermal compensation feedback data; In an embodiment of the present invention, the initial scene division boundary conditions, such as the core area, the edge area, and the interface area, are defined according to the chip functional module or physical area division standard. The high-dimensional temperature data is layered by isothermal surface analysis technology to identify temperature gradients and key overheating areas: Si={(x,y,z)|T(x,y,z,t)∈[Timin,Timax]}; wherein Si represents the i-th temperature scene, and Timin and Timax are the temperature range thresholds of the scene, respectively. The analyzed temperature scene is divided into a first chip temperature scene and a second chip temperature scene, which represent the high heat zone and the low heat zone or the thermal behavior areas of different functional modules, respectively. According to the spatial distribution of each temperature scene, a multi-point temperature control signal is generated: a stronger heat dissipation signal is applied to S1 (high heat zone), and the signal strength can be defined as: PS1=k1⋅∇T+k2; wherein k1 and k2 are signal gain parameters. A hold or fine-tuning signal is applied to S2 (low heat zone). Perform overlapping analysis on the control signal areas of S1 and S2, calculate the boundary area signal, and optimize the boundary area temperature balance: Topt = (TS1 + TS2) ÷ 2. The optimization result is used as the basis for the distribution of the boundary area control signal. Output optimized temperature control distribution data, including the first chip temperature control optimization data and the second chip temperature control optimization data. Use the following adaptive thermal compensation formula to compensate the first chip temperature control optimization data and the second chip temperature control optimization data, and generate chip thermal compensation feedback data based on the compensation result. The feedback data includes the following elements: real-time compensation amount and adjusted temperature control distribution data. Re-input the chip thermal compensation feedback data into the temperature control system to form a closed-loop regulation and continuously optimize the chip's thermal management strategy.

[0032] Step S3: Perform scenario-based testing on the optimized temperature control distribution data based on the chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; perform optimal temperature control curve screening on the semiconductor chip scenario temperature control test data to obtain the semiconductor chip scenario temperature control optimal curve; In the embodiment of the present invention, the input data include chip thermal compensation feedback data and optimized temperature control distribution data.

[0033] Initialize the scenario-based test environment, including: Simulate chip operating conditions (voltage, frequency, load).

[0034] Set up thermal load test equipment and temperature sensor array. Perform temperature control distribution test on the chip under different operating conditions (such as high-performance mode, energy-saving mode): record the real-time temperature distribution of the chip under various conditions. Use the sensor array to collect chip temperature control data in real time and generate scenario-based temperature control test data. Analyze the collected temperature control test data: evaluate the temperature uniformity and stability of each area of ​​the chip, and mark the temperature fluctuation area and hot spots. Convert the scenario temperature control test data into a time-temperature curve (temperature control curve): Tcurve(t)={T1(t),T2(t),…,Tn(t)}; where Ti(t) represents the temperature change of the i-th key point of the chip over time. Smooth and calibrate the generated temperature control curve to eliminate noise and outliers: Tcalibrated(t)=Tcurve(t)−ΔTnoise; where ΔTnoise is the noise correction value. Based on the calibration results, generate a semiconductor chip scenario temperature control calibration curve. The temperature control calibration curve is screened according to the following indicators: whether the temperature fluctuation range of the curve is within the preset range (such as ±0.5°C), whether the response time required for the curve to reach a stable temperature is the shortest, and whether the energy consumption corresponding to the curve is the lowest. Each temperature control calibration curve is scored, and the curve with the highest comprehensive score is selected as the optimal temperature control curve: Scurve=ω1⋅Sstability+ω2⋅Sresponse+ω3⋅Sefficiency; where ω1,ω2,ω3 are weight parameters.

[0035] Step S4: construct a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scene to generate a semiconductor chip scene temperature control strategy; perform temperature control management on the semiconductor chip scene temperature control strategy to generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

[0036] In an embodiment of the present invention, a semiconductor chip temperature control curve data set is generated by extracting multi-dimensional temperature control data including heating curves, cooling curves, temperature stabilization curves, etc. from the test scene of the semiconductor chip. The temperature control requirements under different test scenes are fitted based on the temperature control curve data set by using multiple regression analysis and machine learning algorithms (such as neural network regression or support vector regression) to generate an optimal temperature control curve model. According to the optimal temperature control curve model, the initial temperature control strategy of the semiconductor chip is constructed in combination with the power consumption, heat dissipation conditions and environmental parameters of the target test scene. The real-time temperature data, power consumption data and ambient temperature data of the chip are collected through the sensor network to generate real-time temperature control feedback data. Based on the real-time temperature control feedback data, the initial temperature control strategy is optimized by using the reinforcement learning algorithm to generate a semiconductor chip scene temperature control strategy that dynamically adapts to different test stages. According to different stages (such as heating, stabilization, and cooling) in the test process, the scene temperature control strategy is applied to perform staged temperature control management and generate staged temperature control management data. Abnormal conditions (such as temperature drift or exceeding the standard) that occur during the temperature control process are monitored, and the temperature control strategy is automatically adjusted by using the abnormal detection model to generate temperature control abnormal adjustment data. Perform data statistics and analysis on the execution results, including temperature fluctuation range, power consumption, response speed, etc., to generate semiconductor chip scene temperature control performance data. In response to lightweight requirements, simplify the complex calculations in the scene temperature control strategy, such as using local optimization or edge computing solutions to generate lightweight temperature control strategies. Deploy lightweight temperature control strategies to semiconductor test systems, perform lightweight temperature control operations, and ultimately generate semiconductor chip scene temperature control management data.

[0037] Preferably, step S1 comprises the following steps: Step S11: obtaining the location of the semiconductor chip test area based on the semiconductor test system; Step S12: deploying temperature sensors at the semiconductor chip test area, and collecting semiconductor chip temperature distribution data at the semiconductor chip test area based on the deployed temperature sensors to obtain real-time temperature distribution data of the semiconductor chip; Step S13: determining key thermodynamic parameters based on the material properties and structural design of the semiconductor chip to obtain the thermodynamic parameters of the semiconductor chip; performing chip spatial temperature distribution analysis on the thermodynamic parameters of the semiconductor chip using the real-time temperature distribution data of the semiconductor chip to generate a field distribution diagram of the thermodynamic parameters of the semiconductor chip; Step S14: construct a high-level heat conduction model based on the thermodynamic parameter field distribution map of the semiconductor chip to generate a preliminary high-dimensional heat conduction model; calibrate the preliminary high-dimensional heat conduction model to generate high-dimensional heat conduction model calibration data; optimize the preliminary high-dimensional heat conduction model through the high-dimensional heat conduction model calibration data to generate a high-dimensional heat conduction model.

[0038] In an embodiment of the present invention, the position of the semiconductor chip test area is accurately determined by using a semiconductor test system using laser scanning or three-dimensional scanning technology. The sensor of the test system captures the chip position data in real time and processes it through an algorithm to obtain accurate test area position coordinates. Ensure that the obtained position data is aligned with the chip physical coordinate system to provide an accurate reference frame for subsequent temperature distribution measurements. Select a suitable temperature sensor (such as a thermocouple, an infrared sensor, or an integrated temperature sensor) to ensure that it has high precision and real-time data acquisition capabilities. The temperature sensors are evenly arranged in the test area of ​​the semiconductor chip to ensure that all key heat source areas, especially power-intensive areas, are covered. The sensor starts to collect temperature data in real time, and the data is transmitted to the data acquisition system via wireless transmission or a wired interface, and preprocessed (such as noise filtering and data compensation). Based on the collected temperature data, a real-time temperature distribution map of the semiconductor chip is generated, showing the temperature gradient and hot spot areas at different locations. According to the thermal conductivity, specific heat capacity and other characteristics of the semiconductor chip material (such as silicon, gallium nitride, etc.), a preliminary estimate of the thermodynamic parameters is performed. A preliminary construction of a thermal model is performed in combination with the chip geometry (such as chip thickness, surface covering layer, etc.). Based on the known material properties and structural design, key thermodynamic parameters such as thermal conductivity, thermal diffusivity, specific heat, etc. are calculated to form a preliminary set of thermodynamic parameters. According to the real-time temperature data and combined with the thermodynamic parameters, the temperature distribution inside the chip is analyzed in detail using heat conduction simulation methods (such as finite element analysis, computational fluid dynamics, etc.). The temperature distribution is combined with the thermodynamic parameters to generate a three-dimensional thermodynamic parameter field distribution map to show the thermal response characteristics of different areas inside the chip. Professional software (such as Matlab, COMSOL, etc.) is used to generate a visual image of the thermodynamic parameter field to help analyze the thermal behavior of the chip under working conditions. Based on the thermodynamic parameter field distribution map, a high-dimensional heat conduction model is constructed, taking into account multiple influencing factors such as material nonlinearity and boundary conditions. Combine multiple physical fields such as thermodynamics, electricity, and mechanics to establish a comprehensive heat conduction model. A preliminary heat conduction simulation is performed by simplifying the model to check whether the model conforms to the heat conduction law in the actual operation of the chip. According to the actual collected chip temperature distribution data, the preliminary heat conduction model is calibrated and the parameters in the model are adjusted to better fit the actual situation. By comparing the temperature distribution predicted by the model with the actual measured data, the error source is analyzed and adjusted. Generate model calibration data, including calibrated parameter values, error correction values, etc.

[0039] Preferably, constructing a high-dimensional heat conduction model based on the thermodynamic parameter field distribution map of the semiconductor chip includes: Based on the heat conduction equation, a mathematical model is established for the thermodynamic parameter field distribution diagram of the semiconductor chip to generate a thermal conduction mathematical model of the semiconductor chip. The formula for establishing the mathematical model is as follows: In the formula, The temperature at a point Over time The rate of change, Expressed as the divergence of the heat flux density, is represented as the gradient operator, Expressed as thermal conductivity, Expressed as temperature, Expressed as heat capacity, is represented as a heat source term; The dimensional boundary conditions are introduced into the semiconductor chip heat conduction mathematical model to generate the dimensional boundary conditions of the semiconductor chip heat conduction, wherein the dimensional boundary conditions include: the temperature value of the fixed chip boundary, the heat flux density of the given boundary, and the heat exchange of the environment to the chip boundary; The semiconductor chip heat conduction dimensional boundary conditions are used to discretize the semiconductor chip heat conduction mathematical model and generate a preliminary high-dimensional heat conduction model.

[0040] In the embodiment of the present invention, the heat conduction model through the semiconductor chip is based on the classical heat conduction equation, in which the change of the temperature field over time is affected by the heat source and thermal conductivity. The heat conduction equation is as follows: In the formula, The temperature at a point Over time The rate of change, Expressed as the divergence of the heat flux density, is represented as the gradient operator, Expressed as thermal conductivity, Expressed as temperature, Expressed as heat capacity, Expressed as a heat source term; for certain fixed areas of the chip (such as cooling areas or external cooling surfaces), the temperature is known. These areas can define boundary conditions with constant temperature values. Mathematical expression: Tboundary=T0, where T0 is the known boundary temperature. At the outer boundary or contact surface of the chip, there is a known heat flux density. This heat flux density is caused by the heat exchange between the chip and the cooling device, air or other materials. Mathematical expression: q⋅n^=qboundary, where q is the heat flux density vector, n^ is the normal vector of the boundary surface, and qboundary is the known heat flux density. Heat exchange between the chip surface and the external environment is usually an important boundary condition. It can be considered by the convection heat transfer coefficient h and the ambient temperature Tenv. Mathematical expression: h(T−Tenv), where h is the convection heat transfer coefficient, which represents the heat exchange intensity between the surface and the environment, and Tenv is the ambient temperature. When establishing a mathematical model of heat conduction, the above boundary conditions are substituted into the model, and the boundary conditions are applied to the discrete grid by interpolation method. The heat conduction equation is discretized by the finite difference method (FDM), finite element method (FEM) or other numerical methods. For each grid node, the discretized heat conduction equation is applied: at each time step Δt, the temperature change at the node is calculated. For each spatial grid cell, an appropriate difference format is applied to approximate the spatial derivative of the heat conduction equation. The discretized heat conduction equation can be written as: in, and Respectively The temperature of a grid node at the current moment and the previous moment, is the discretization coefficient of thermal conductivity, indicating the and The heat conduction coupling relationship between the nodes is is the heat source term at the node Discrete representation of . Represented as a heat source term at the node The heat capacity at the point. The heat conduction coefficient matrix and heat source term vector obtained through the discretization process constitute the core part of the high-dimensional heat conduction model. The discretized heat conduction equation is solved using a numerical solver (such as the conjugate gradient method, LU decomposition method, etc.) to obtain the temperature change of each grid node at different time steps. Compare and verify with experimental data or other known heat conduction data to ensure the accuracy of the model.

[0041] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: using a high-dimensional heat conduction model to dynamically predict chip thermal effects on the real-time temperature distribution data of the semiconductor chip, and generating chip thermal effect dynamic prediction data; performing time-step chip temperature change analysis on the chip thermal effect dynamic prediction data, and generating time-step chip temperature change data; Step S22: dividing the chip temperature change data of the time step into chip temperature scenarios based on a preset temperature change threshold value to generate a first chip temperature scenario and a second chip temperature scenario; performing chip temperature control demand analysis on the real-time temperature distribution data of the semiconductor chip based on the first chip temperature scenario and the second chip temperature scenario to generate chip temperature control demand data; Step S23: Optimizing the multi-point temperature control distribution of the first chip temperature scenario and the second chip temperature scenario according to the chip temperature control demand data to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data; Step S24: Perform chip adaptive thermal compensation on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data to generate chip thermal compensation data; perform compensation feedback on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data through the chip thermal compensation data, thereby generating chip thermal compensation feedback data.

[0042] In an embodiment of the present invention, a dynamic prediction is performed by utilizing an established high-dimensional heat conduction model and inputting the real-time temperature distribution data of a semiconductor chip. The process is based on the following mathematical model: the real-time temperature distribution data Treal (x, y, z, t of a semiconductor chip, including the temperature distribution of the chip at the current time t and in the spatial region (x, y, z). Numerical simulation is performed using the heat conduction equation to predict the temperature change in the next time step. The heat conduction model is used to predict the temperature change in the next few time steps, and to generate dynamic prediction data of chip thermal effects, that is, the temperature change of the chip in the future time steps. By performing a time step analysis on the dynamic prediction data of chip thermal effects, the temperature change of the chip in each time step is calculated, and time step chip temperature change data is generated, which records the temperature distribution of the chip at different time steps. To predict the temperature change of the chip For effective management, you first need to set a preset temperature change threshold, which is used to distinguish different temperature scenarios. According to the working characteristics and thermal management requirements of the semiconductor chip, select appropriate temperature thresholds, such as 50°C, 70°C, and 100°C. Based on the preset temperature change threshold, the time step chip temperature change data is divided: The first chip temperature scenario: When the chip temperature is below a certain threshold (such as 50°C) at a certain moment, it belongs to the first chip temperature scenario. In this scenario, the thermal effect of the chip is small and the temperature control requirement is low. The second chip temperature scenario: When the chip temperature exceeds a certain threshold (such as 70°C or 100°C), it belongs to the second chip temperature scenario. At this time, the thermal effect of the chip is The response is large, and more stringent temperature control measures are required. Based on the first chip temperature scenario and the second chip temperature scenario, the temperature control demand analysis is performed on the real-time temperature distribution data of the semiconductor chip to obtain the chip temperature control demand data. The analysis includes the following steps: The temperature control demand of the first chip temperature scenario usually means that the temperature is low or close to the normal operating range, and the temperature needs to be controlled through appropriate heat dissipation, heating, etc. The temperature control demand of the second chip temperature scenario is that when the chip temperature is high, stronger cooling is required, involving a more complex thermal management system, such as using more efficient heat dissipation systems, fans, liquid cooling, etc. to maintain the chip temperature within a safe range. Through the analysis of the chip temperature control demand data, based on the first chip The goal of this step is to effectively distribute temperature control measures to different areas of the chip to ensure the overall thermal management effect of the chip: including the generation of temperature control optimization data for the first chip scenario and the second chip scenario, and determining the temperature control intensity and adjustment method for each area. Based on the temperature control optimization data for the first chip scenario and the temperature control optimization data for the second chip scenario, chip adaptive thermal compensation is performed. This process takes into account local temperature changes under temperature scenarios and makes dynamic adjustments based on the working characteristics of the chip: in high temperature environments, the thermal compensation mechanism of the chip will automatically enhance cooling measures, while in low temperature environments it will reduce power consumption or adjust temperature control measures.Based on the chip thermal compensation data, the temperature control optimization data of the first chip scenario and the temperature control optimization data of the second chip scenario are compensated and fed back. Through the feedback mechanism, the temperature control measures are dynamically adjusted to ensure that the temperature of the chip is always within the ideal working range. The data includes real-time temperature control optimization strategy, which is adjusted by feedback of actual temperature changes, and the temperature control scheme is gradually optimized to ensure the stable operation of the chip under different environmental conditions.

[0043] Preferably, step S23 includes the following steps: Step S231: measuring the temperature distribution of the first chip temperature scene and the second chip temperature scene according to the chip temperature control requirement data to generate initial temperature field data of the first scene and initial temperature field data of the second scene; performing data fitting on the initial temperature field data of the first scene and the initial temperature field data of the second scene to generate initial temperature field distribution data; Step S232: performing high temperature scene multi-point controller parameter configuration on the initial temperature field data of the first scene to generate first scene multi-point controller configuration parameters, wherein the high temperature scene multi-point controller parameter configuration includes power adjustment range and response time; Step S233: performing low-temperature scene multi-point controller parameter configuration on the initial temperature field data of the second scene to generate second scene multi-point controller configuration parameters, wherein the low-temperature scene multi-point controller parameter configuration includes cooling efficiency and steady-state maintenance capability; Step S234: Perform real-time temperature control signal distribution on the initial temperature field distribution data through the first scene multi-point controller configuration parameters and the second scene multi-point controller configuration parameters to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

[0044] In an embodiment of the present invention, data fitting is performed by fitting the initial temperature field data of the first scene and the initial temperature field data of the second scene. The purpose of data fitting is to convert the temperature distribution data into a smooth temperature field model through a numerical method for subsequent optimization and analysis: by fitting technology (such as least squares method or interpolation method), the two sets of initial temperature field data are converted into a set of continuous and smooth temperature field distribution data to ensure the accuracy and operability of the temperature distribution. For the first chip temperature scenario, it usually means that the chip is in a higher temperature range, so cooling needs to be strengthened. At this time, the multi-point controller in the temperature control system needs to configure parameters according to the characteristics of the scenario: set the power output range of the controller to ensure that it can meet the needs of different temperature areas. In high temperature scenarios, the controller needs to provide a large cooling power to reduce the chip temperature. Set the response time of the controller to temperature changes to ensure that the control parameters can be adjusted quickly when the temperature changes to avoid excessive temperature fluctuations and cause chip overheating. The above configuration is converted into the configuration parameters of the multi-point controller of the first scenario to provide the controller with clear adjustment indicators so that it can keep the temperature of the chip within a safe range in a high temperature environment. For the second chip temperature scenario, that is, when the chip is in a lower temperature range, the multi-point controller in the temperature control system needs to be optimized for low temperature conditions: in the low temperature scenario, the controller's cooling system does not need to be too strong, and the focus needs to be on optimizing the cooling efficiency to maintain the chip temperature within the optimal working range while avoiding overcooling. In the low temperature scenario, the temperature control system needs to have a strong steady-state maintenance capability, that is, it can effectively maintain the chip within the preset temperature range during long-term operation to avoid excessive temperature fluctuations. According to the requirements of the low temperature scenario, the configuration parameters of the second scenario multi-point controller are generated to ensure that the temperature control system can provide accurate temperature regulation under low temperature conditions. According to the controller configuration parameters in different scenarios, control signals that adapt to the current temperature requirements are dynamically generated. These signals will be transmitted to the multi-point controller to guide it to perform corresponding temperature control operations. The generated control signals are distributed to each control point to ensure that different areas of the chip are properly adjusted according to the temperature requirements. Through the distribution of temperature control signals, the temperature control system of the chip will adjust the temperature of each area. Finally, through real-time adjustment, optimized temperature control distribution data is generated.

[0045] Preferably, distributing the real-time temperature control signal to the initial temperature field distribution data by configuring the first scene multi-point controller and the second scene multi-point controller includes the following: The control signal is generated by the initial temperature field distribution data through the configuration parameters of the first scene multi-point controller and the configuration parameters of the second scene multi-point controller to obtain the first scene control signal and the second scene control signal; Performing signal control area identification on the first scene control signal and the second scene control signal to obtain the first scene control signal area and the second scene control signal area; performing control signal area overlap on the first scene control signal area and the second scene control signal area to obtain a control boundary area signal; Based on the boundary conditions of the semiconductor chip's thermal conduction dimension, the control boundary area signal is adjusted to generate a control boundary area adjustment signal; according to the control boundary area adjustment signal, the first scene control signal area and the second scene control signal area are distributed to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

[0046] In the embodiment of the present invention, in the high temperature scenario, the temperature of the semiconductor chip is high, and the temperature needs to be adjusted by cooling means. The control signal mainly includes adjusting the power output of the heat dissipation device (such as a fan, a heat pipe, etc.) to enhance the cooling effect. This signal will dynamically adjust the power output according to the data provided by the temperature sensor to reduce the temperature of the chip to a preset safe range. In the low temperature scenario, the temperature of the chip is too low to affect its working efficiency or stability. At this time, it is necessary to adjust the temperature of the chip by heating or reducing the cooling effect. The control signal will adjust the output of the heating device (such as a heating plate or a temperature control power supply) to ensure that the chip temperature is maintained within the normal working range. The control signals of these two scenarios will be dynamically adjusted according to the actual temperature data of the chip to meet the temperature control requirements in two different scenarios. After the temperature scene is divided, it is necessary to further identify and delineate the area where the temperature control signal acts. The purpose of this process is to accurately control different parts of the chip and perform local adjustments in high temperature or low temperature scenarios. Areas with higher temperatures need to be adjusted by stronger cooling signals. Through the temperature sensor data on the chip, these areas can be identified, and then efficient cooling treatment can be performed. For areas with lower temperatures, the temperature is adjusted by heating or reducing the cooling signal. This part includes components on the chip that are sensitive to low temperatures, such as power supplies or some high-performance computing units. In the actual temperature control process, there will be a certain intersection between the temperature control signals of the first scene and the second scene. In particular, the edge area of ​​the chip is close to the boundary of the two scenes. In order to avoid uneven temperature at the junction, precise adjustment of the junction area is required: the junction area is the intersection area of ​​the temperature control signals of the first scene (high temperature) and the second scene (low temperature). This part requires special attention to avoid mutual interference between the two signals or causing the temperature to change too quickly. Using the boundary conditions of the chip's thermal conduction dimension, the signal in the junction area is adjusted to ensure that the temperature of the chip can transition smoothly without drastic fluctuations: these conditions include factors such as the chip's geometry, material thermal conductivity, and temperature distribution of the boundary layer. Based on these boundary conditions, the temperature transfer law of the junction area is calculated to reasonably distribute the strength of the temperature control signal in the junction area. By adjusting the temperature control signal in the junction area, a smooth temperature transition between high and low temperature scenes is ensured, and sudden temperature changes caused by excessively strong or weak temperature control signals are avoided, protecting the chip from damage caused by temperature fluctuations. After adjusting the signal in the boundary area, the temperature control signal of the entire chip needs to be finally optimized and distributed to ensure that the temperature control signal of each area meets the needs of different scenarios: For high-temperature scenarios, the power of the heat sink is adjusted in real time to ensure that the high-temperature area of ​​the chip is effectively cooled to avoid excessive temperature. For low-temperature scenarios, the low-temperature area is heated or the cooling signal is reduced to ensure that the chip does not work unstably.

[0047] Preferably, step S24 includes the following steps: Step S241: performing thermal behavior analysis on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data to generate chip thermal behavior data; Step S242: performing closed-loop control on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data through the chip thermal behavior data to generate chip scenario temperature control closed-loop control data; Step S243: using an adaptive thermal compensation formula to perform real-time thermal compensation on the chip scene temperature control closed-loop control data to generate chip thermal compensation data; Step S244: Compensate and feedback the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data using chip thermal compensation data, thereby generating chip thermal compensation feedback data.

[0048] In an embodiment of the present invention, the temperature control optimization data of the first chip scene and the temperature control optimization data of the second chip scene are collected, mainly including parameters such as temperature, power regulation, and response time. The thermal behavior of the chip is modeled using the heat conduction equation and the thermophysical properties of the material (such as thermal conductivity, specific heat, etc.). The modeling method includes: using the finite element method to calculate the temperature field distribution according to the geometric shape and thermal conduction characteristics of the chip. Combined with the optimization data, numerical simulation software (such as ANSYS, COMSOL) is used to simulate the thermal behavior of the chip under different loads and different environmental conditions. By analyzing the temperature distribution, heat flux, heat dissipation efficiency and other parameters of the chip, the thermal behavior data of the chip is generated to reflect the thermal response and stability of the chip under different working conditions. According to the chip thermal behavior data, a suitable closed-loop control algorithm (such as PID control, fuzzy control, etc.) is selected to realize real-time adjustment of the temperature control optimization data. The temperature control signal of the chip is adjusted by proportion, integration, and differentiation to maintain the chip temperature within a preset range. When the temperature change is more complex or uncertain, the temperature control signal is adjusted using fuzzy logic. The temperature control optimization data of the first chip scene and the second chip scene are adjusted in real time using thermal behavior data to ensure that the temperature control strategy always remains in the optimal state. Based on the real-time temperature control adjustment results, the closed-loop control data of the chip scene is generated to further optimize the temperature regulation of the chip. An adaptive thermal compensation formula is designed to consider the thermal response characteristics and real-time temperature changes of the chip to compensate for the temperature control errors caused by environmental fluctuations, heat dissipation changes and other factors. According to the real-time chip temperature changes and control data, the temperature control data is adaptively adjusted to compensate for the thermal deviation of the chip. Through the adaptive thermal compensation formula, the chip thermal compensation data is generated to ensure the stability and accuracy of the chip temperature control under different conditions. A feedback mechanism is established through the thermal compensation data, and the thermal compensation data is returned to the temperature control system to adjust the subsequent temperature control strategy. The compensated data is fed back to the control system to pre-adjust and optimize the temperature of the next cycle. According to the compensation feedback data, the chip scene temperature control optimization data is dynamically adjusted to optimize the temperature control strategy. The chip thermal compensation feedback data is generated according to the compensation feedback mechanism, and the temperature control adjustment, stability evaluation and compensation effect during the compensation process are recorded to provide a basis for further optimization.

[0049] Preferably, the adaptive thermal compensation formula in step S243 is as follows: In the formula, Represented as the current time and location The compensation temperature, Represented as the current time and location The temperature error, Expressed as the compensation gain coefficient, Expressed as the temperature history integration coefficient, Represented as the current time and location The actual measured temperature, Expressed as the feedback gain coefficient.

[0050] This invention analyzes and integrates an adaptive thermal compensation formula. The purpose of this adaptive thermal compensation formula is to dynamically compensate for the temperature error of the temperature control system to ensure that the chip can maintain stable temperature control while maintaining high performance. It achieves adaptive adjustment through three main components (current error, historical error integration, and real-time feedback). The current temperature error term in the formula is: It directly reflects the temperature error of the chip at the current moment. The greater the temperature deviation, the greater the compensation signal. is the difference between the actual temperature and the target temperature, which determines the intensity of compensation required. This term enables the system to respond to temperature errors in real time, ensuring that the chip temperature can quickly approach the target value. To adjust the response strength, ensure that the system can cope with sudden temperature fluctuations and prevent overcompensation. Historical temperature error integral term: Represents the accumulation of temperature errors over a period of time. It considers the impact of historical errors on current compensation through integration. The historical integral term can reduce the reaction delay of the temperature control system, avoid overreaction to instantaneous errors, and smooth the entire temperature control process. This avoids oscillation or instability caused by too fast response of the system. Real-time temperature feedback term: Indicates that the compensation amount is adjusted based on the real-time temperature feedback signal. Feedback signal It is the actual temperature detected by the sensor, reflecting the real situation of the chip at a certain moment. The real-time feedback term is used to modify the temperature control strategy to ensure that the system dynamically adjusts the control signal according to the actual chip temperature to avoid the situation where the theoretical prediction does not match the actual situation. Feedback enables the temperature control system to cope with external changes or internal uncertainties. , the contribution of the feedback signal to the compensation can be flexibly adjusted to ensure more efficient and accurate compensation. When using the conventional adaptive thermal compensation formula in the field, it can be obtained at the current time and location By applying the adaptive thermal compensation formula provided by the present invention, the compensation temperature at the current time can be calculated more accurately. and location The formula uses the real-time temperature error to drive a fast compensation response, and uses historical errors and feedback to smooth the control process, ensuring that the temperature control process is both fast and accurate. The integral term of the historical temperature error avoids the system from overreacting to short-term fluctuations, thereby improving the stability of the system. ), which can self-adjust according to the real-time working status of the chip, changes in the external environment and other factors to ensure that the system always maintains optimal performance under different working conditions.

[0051] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: performing scenario-based testing on the optimized temperature control distribution data based on chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; Step S32: converting the semiconductor chip scene temperature control test data into a temperature control curve to generate a semiconductor chip scene temperature control curve; calibrating the semiconductor chip scene temperature control curve to generate a semiconductor chip scene temperature control calibration curve; Step S33: Screening the optimal temperature control curve for the semiconductor chip scene temperature control calibration curve, thereby obtaining the semiconductor chip scene temperature control optimal curve.

[0052] In an embodiment of the present invention, temperature adjustment information and compensated temperature control data are collected based on chip thermal compensation feedback data. These data include feedback signals of the temperature control system, real-time temperature, power regulation and other information. Different test scenarios are designed according to the working conditions and external environment of the chip (such as load, heat dissipation conditions, ambient temperature, etc.). For example: high load scenario, low load scenario, constant temperature scenario, etc. In each scenario, the temperature control distribution is tested according to the thermal compensation feedback data. Monitor indicators such as the temperature response, power consumption, and heat dissipation effect of the chip to ensure that the test process covers the operating state of the chip. During the test process, real-time temperature control data is collected and recorded to form semiconductor chip scenario temperature control test data. According to the test results, semiconductor chip scenario temperature control test data is generated. These data include information such as temperature change curves, power regulation data, and temperature control optimization effects in each test scenario. Use filtering algorithms (such as Kalman filtering, sliding average filtering, etc.) to eliminate noise in the test data and extract a smooth temperature curve. The temperature control data is converted into a standard temperature control curve form by a curve fitting method (such as polynomial fitting, spline curve fitting, etc.). The curve should be able to show the law of temperature change over time or power. The converted curves will reflect the temperature control performance of the chip in different scenarios, including the temperature change trend over time. These temperature control curves can show information such as heat rise and cooling process, providing a basis for subsequent temperature control optimization. Through the calibration process, the curve deviation caused by equipment errors, test environment changes and other factors is eliminated. The goal of calibration is to ensure the accuracy of temperature control of the chip temperature control system in different environments. The measured temperature control curve is corrected using linear transformation to ensure the linear relationship between the chip temperature and the control signal. For complex nonlinear temperature control systems, more complex algorithms such as polynomial fitting and neural network are used to accurately calibrate the temperature control curve. According to the design requirements of the chip, set the target parameters of the temperature control curve, such as temperature stability, response time, energy efficiency, etc. Select the curve with the smoothest temperature change and the smallest error. Screen the curve that consumes the least power within the set temperature range. Select the curve that can maintain temperature stability under various load and environmental conditions. Based on the stability and accuracy requirements of the temperature control system, select the best one from multiple calibrated temperature control curves as the final temperature control standard curve. The calibrated and screened temperature control curve will be used as the optimal temperature control curve for the chip temperature control system. This curve can provide the most accurate and stable temperature control effect, ensuring the most efficient temperature management of the chip under various working conditions.

[0053] Preferably, step S4 comprises the following steps: Step S41: constructing a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scenario to generate a temperature control strategy for the semiconductor chip scenario; Step S42: Upload the semiconductor chip scene temperature control strategy to the cloud platform for automatic push, and generate automatic temperature control strategy deployment data; perform temperature control management on the automatic temperature control strategy deployment data, and generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

[0054] In the embodiment of the present invention, the thermal behavior and thermal management objectives of the chip are analyzed according to the temperature control requirements of the chip under different working conditions. Consider temperature control objectives such as temperature accuracy, stability, power consumption limit, etc. Determine the parameters of the temperature control strategy, including temperature setting range, temperature control response time, power adjustment amplitude, etc., to ensure that it can meet the temperature management requirements of the chip under various load conditions. Combined with the optimal curve of temperature control in semiconductor chip scenarios, the temperature control strategy is designed through mathematical modeling methods (such as PID control, fuzzy control, neural network control, etc.). The model should be able to adjust the temperature control parameters in real time to cope with temperature changes under different working conditions. The core of the temperature control strategy is to adjust the power output according to real-time temperature data and external environmental changes to ensure that the temperature of the chip is within the optimal range. Determine the algorithm and scheduling scheme of the temperature control strategy, support dynamic adjustment and feedback, and ensure the efficiency and stability of the temperature control system. According to the preliminary construction of the temperature control strategy, a simulation test is carried out to evaluate its effect on the regulation of chip temperature to ensure that the strategy can work stably under high load, low load and other typical working conditions. Optimize the temperature control strategy, adjust the control parameters and algorithms to improve the temperature control response speed and stability, and reduce over-adjustment and overheating. Based on the model and optimization, the semiconductor chip scenario temperature control strategy is finally generated to ensure that it adapts to the working environment and task requirements of the chip. The optimized semiconductor chip scenario temperature control strategy is transmitted to the cloud platform through API or file upload. The cloud platform needs to have the temperature control strategy management and deployment functions to ensure that the strategy can be remotely accessed and controlled. The upload process ensures the security and integrity of the data, and an encrypted transmission method can be used to prevent data from being tampered with during transmission. In the cloud platform, an automatic push mechanism is configured to automatically deploy the temperature control strategy to each test device or related chip system through cloud services. The pushed temperature control strategy includes real-time temperature control algorithm, parameter setting, temperature feedback mechanism, etc., which can automatically perform temperature control tasks on the target device. During the automatic push process, the cloud platform will record the policy version, deployment time, device status and other information of each deployment, and generate automatic temperature control strategy deployment data. The deployment data includes the configuration file of the temperature control strategy, push records, device feedback and other information for subsequent management and analysis. A temperature control management system is established in the cloud platform to monitor the temperature status of each device in real time, ensure that the temperature control strategy can take effect in real time, and make necessary adjustments. The temperature control management system will receive temperature feedback data from each chip or device, compare it with the set optimal temperature control curve, and automatically adjust the temperature control strategy. The management system also needs to take into account changes in the external environment, such as temperature and humidity, to make timely adjustments and optimizations to the temperature control strategy. The temperature control management system generates semiconductor chip scene temperature control management data, including real-time temperature data, control signals, adjustment parameters, etc.

[0055] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0056] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A lightweight temperature control method based on a semiconductor test system, characterized in that: The following steps are involved: Step S1: obtaining the location of the semiconductor chip test area; collecting chip temperature distribution data at the location of the semiconductor chip test area to obtain real-time temperature distribution data of the semiconductor chip; constructing a heat conduction model for the real-time temperature distribution data of the semiconductor chip to generate a high-dimensional heat conduction model; Step S2: using a high-dimensional heat conduction model to divide the real-time temperature distribution data of the semiconductor chip into chip temperature scenarios, generating a first chip temperature scenario and a second chip temperature scenario; performing multi-point temperature control distribution optimization on the first chip temperature scenario and the second chip temperature scenario to generate optimized temperature control distribution data; performing chip adaptive thermal compensation feedback on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data, thereby generating chip thermal compensation feedback data; Step S3: Performing scenario-based testing on the optimized temperature control distribution data based on chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; Screen the optimal temperature control curve for the semiconductor chip scene temperature control test data to obtain the optimal temperature control curve for the semiconductor chip scene; Step S4: constructing a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scenario to generate a temperature control strategy for the semiconductor chip scenario; Perform temperature control management on semiconductor chip scenario temperature control strategies and generate semiconductor chip scenario temperature control management data to perform lightweight temperature control operations on semiconductor test systems.

2. The lightweight temperature control method based on a semiconductor test system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining the location of the semiconductor chip test area based on the semiconductor test system; Step S12: deploying temperature sensors at the semiconductor chip test area, and collecting semiconductor chip temperature distribution data at the semiconductor chip test area based on the deployed temperature sensors to obtain real-time temperature distribution data of the semiconductor chip; Step S13: determining key thermodynamic parameters based on the material properties and structural design of the semiconductor chip to obtain the thermodynamic parameters of the semiconductor chip; performing chip spatial temperature distribution analysis on the thermodynamic parameters of the semiconductor chip using the real-time temperature distribution data of the semiconductor chip to generate a field distribution diagram of the thermodynamic parameters of the semiconductor chip; Step S14: construct a high-dimensional heat conduction model based on the thermodynamic parameter field distribution map of the semiconductor chip to generate a preliminary high-dimensional heat conduction model; calibrate the preliminary high-dimensional heat conduction model to generate high-dimensional heat conduction model calibration data; optimize the preliminary high-dimensional heat conduction model through the high-dimensional heat conduction model calibration data to generate a high-dimensional heat conduction model.

3. The lightweight temperature control method based on a semiconductor test system according to claim 2, characterized in that: The construction of high-dimensional heat conduction model based on the thermodynamic parameter field distribution map of semiconductor chips includes: Based on the heat conduction equation, a mathematical model is established for the thermodynamic parameter field distribution diagram of the semiconductor chip to generate a thermal conduction mathematical model of the semiconductor chip. The formula for establishing the mathematical model is as follows: In the formula, The temperature at a point Over time The rate of change, Expressed as the divergence of the heat flux density, is represented as the gradient operator, Expressed as thermal conductivity, Expressed as temperature, Expressed as heat capacity, is represented as a heat source term; The dimensional boundary conditions are introduced into the semiconductor chip heat conduction mathematical model to generate the dimensional boundary conditions of the semiconductor chip heat conduction, wherein the dimensional boundary conditions include: the temperature value of the fixed chip boundary, the heat flux density of the given boundary, and the heat exchange of the environment to the chip boundary; The semiconductor chip heat conduction dimensional boundary conditions are used to discretize the semiconductor chip heat conduction mathematical model and generate a preliminary high-dimensional heat conduction model.

4. The lightweight temperature control method based on a semiconductor test system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: using a high-dimensional heat conduction model to dynamically predict chip thermal effects on the real-time temperature distribution data of the semiconductor chip, and generating chip thermal effect dynamic prediction data; performing time-step chip temperature change analysis on the chip thermal effect dynamic prediction data, and generating time-step chip temperature change data; Step S22: dividing the chip temperature change data of the time step into chip temperature scenarios based on a preset temperature change threshold value to generate a first chip temperature scenario and a second chip temperature scenario; performing chip temperature control demand analysis on the real-time temperature distribution data of the semiconductor chip based on the first chip temperature scenario and the second chip temperature scenario to generate chip temperature control demand data; Step S23: Optimizing the multi-point temperature control distribution of the first chip temperature scenario and the second chip temperature scenario according to the chip temperature control demand data to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data; Step S24: Perform chip adaptive thermal compensation on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data to generate chip thermal compensation data; perform compensation feedback on the first chip scene temperature control optimization data and the second chip scene temperature control optimization data through the chip thermal compensation data, thereby generating chip thermal compensation feedback data.

5. The lightweight temperature control method based on a semiconductor test system according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: measuring the temperature distribution of the first chip temperature scene and the second chip temperature scene according to the chip temperature control requirement data to generate initial temperature field data of the first scene and initial temperature field data of the second scene; performing data fitting on the initial temperature field data of the first scene and the initial temperature field data of the second scene to generate initial temperature field distribution data; Step S232: performing high temperature scene multi-point controller parameter configuration on the initial temperature field data of the first scene to generate first scene multi-point controller configuration parameters, wherein the high temperature scene multi-point controller parameter configuration includes power adjustment range and response time; Step S233: performing low-temperature scene multi-point controller parameter configuration on the initial temperature field data of the second scene to generate second scene multi-point controller configuration parameters, wherein the low-temperature scene multi-point controller parameter configuration includes cooling efficiency and steady-state maintenance capability; Step S234: Perform real-time temperature control signal distribution on the initial temperature field distribution data through the first scene multi-point controller configuration parameters and the second scene multi-point controller configuration parameters to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

6. The lightweight temperature control method based on a semiconductor test system according to claim 5, characterized in that: Distributing the real-time temperature control signal for the initial temperature field distribution data by configuring the first scene multi-point controller and the second scene multi-point controller includes the following: The control signal is generated by the initial temperature field distribution data through the configuration parameters of the first scene multi-point controller and the configuration parameters of the second scene multi-point controller to obtain the first scene control signal and the second scene control signal; Performing signal control area identification on the first scene control signal and the second scene control signal to obtain the first scene control signal area and the second scene control signal area; performing control signal area overlap on the first scene control signal area and the second scene control signal area to obtain a control boundary area signal; Based on the boundary conditions of the semiconductor chip's thermal conduction dimension, the control boundary area signal is adjusted to generate a control boundary area adjustment signal; according to the control boundary area adjustment signal, the first scene control signal area and the second scene control signal area are distributed to generate optimized temperature control distribution data, wherein the optimized temperature control distribution data includes the first chip scene temperature control optimization data and the second chip scene temperature control optimization data.

7. The lightweight temperature control method based on a semiconductor test system according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing thermal behavior analysis on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data to generate chip thermal behavior data; Step S242: performing closed-loop control on the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data through the chip thermal behavior data to generate chip scenario temperature control closed-loop control data; Step S243: using an adaptive thermal compensation formula to perform real-time thermal compensation on the chip scene temperature control closed-loop control data to generate chip thermal compensation data; Step S244: Compensate and feedback the first chip scenario temperature control optimization data and the second chip scenario temperature control optimization data using chip thermal compensation data, thereby generating chip thermal compensation feedback data.

8. The lightweight temperature control method based on a semiconductor test system according to claim 7, characterized in that: The adaptive thermal compensation formula in step S243 is as follows: In the formula, Represented as the current time and location The compensation temperature, Represented as the current time and location The temperature error, Expressed as the compensation gain coefficient, Expressed as the temperature history integration coefficient, Represented as the current time and location The actual measured temperature, Expressed as the feedback gain coefficient.

9. The lightweight temperature control method based on a semiconductor test system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing scenario-based testing on the optimized temperature control distribution data based on chip thermal compensation feedback data to generate semiconductor chip scenario temperature control test data; Step S32: converting the semiconductor chip scene temperature control test data into a temperature control curve to generate a semiconductor chip scene temperature control curve; calibrating the semiconductor chip scene temperature control curve to generate a semiconductor chip scene temperature control calibration curve; Step S33: Screening the optimal temperature control curve for the semiconductor chip scene temperature control calibration curve, thereby obtaining the semiconductor chip scene temperature control optimal curve.

10. The lightweight temperature control method based on a semiconductor test system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a temperature control strategy based on the optimal temperature control curve of the semiconductor chip scenario to generate a temperature control strategy for the semiconductor chip scenario; Step S42: Upload the semiconductor chip scene temperature control strategy to the cloud platform for automatic push, and generate automatic temperature control strategy deployment data; perform temperature control management on the automatic temperature control strategy deployment data, and generate semiconductor chip scene temperature control management data to perform lightweight temperature control operations of the semiconductor test system.

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