Chip temperature control method, device, equipment and medium

Abstract

The embodiment of the invention discloses a chip temperature control method, device and equipment and a medium, and relates to the technical field of chip testing. The method comprises the following steps: acquiring a temperature fluctuation standard deviation and an error change rate of a to-be-tested chip based on an acquired temperature of the to-be-tested chip; matching a target weighting coefficient combination in a preset fuzzy rule base according to the temperature fluctuation standard deviation and the error change rate; determining a dynamic PID control parameter based on the target weighting coefficient combination and a preset basic PID parameter; and adjusting the power output of the temperature control module by adopting a preset PID control algorithm according to the dynamic PID control parameters so as to control the temperature of the chip. According to the invention, under the condition that only local heating or cooling is carried out on the chip, severe temperature fluctuation of the chip caused by dynamic load change can be effectively handled, and rapid, stable and high-precision control of the chip temperature is realized, so that strict requirements of extreme temperature testing are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chip testing, and in particular to a chip temperature control method, device, equipment and medium. BACKGROUND

[0002] In the design and development stage of a semiconductor chip, the verification of the chip function and reliability is a crucial link. One of the key test requirements is to place the chip in an extreme high-low temperature environment for running, so as to simulate the severe working conditions that the chip may encounter in actual application, and thus to observe the performance, functional integrity and potential failure mode of the chip under temperature limit conditions. Such a test environment usually requires to reach an extreme temperature range far below the freezing point (such as subzero temperature) or far above the normal temperature (such as hundreds of degrees high temperature).

[0003] However, implementing such extreme temperature testing faces significant technical challenges. One of the challenges is that the chip does not exist independently, but is integrated on a test mainboard and works cooperatively with many other electronic components (such as capacitors, resistors, connectors, PCB substrate materials, etc.). Although the chip to be tested itself is designed to withstand these extreme temperatures, most of the other components on the mainboard and the structure of the mainboard itself often have a much narrower temperature tolerance range than the chip. If the entire test mainboard is directly placed in the target extreme temperature environment (such as a high-low temperature test chamber) for testing, these peripheral components and materials are likely to be physically damaged, degraded in performance or failed in function due to exceeding their tolerance limits, resulting in the test being unable to proceed or the results being distorted.

[0004] Therefore, a feasible solution is to avoid overall environmental testing and instead adopt a local temperature control strategy, that is, in a normal temperature environment, only the chip to be tested itself is precisely heated or cooled to create the required extreme temperature microenvironment for the target chip, while ensuring that the other parts of the mainboard are in a safe normal temperature state. This requires a special local temperature control device to achieve.

[0005] The second challenge comes from the dynamic characteristics of the chip testing process. In the development verification stage, the chip needs to run various complex software stress models and load test programs to simulate different working scenarios (such as startup, full load, standby, burst task, etc.) in real applications. Different software loads will significantly change the power consumption and working state of the chip, resulting in rapid and large changes in its heat generation. This dynamically changing load directly leads to dramatic fluctuations in the temperature of the chip. In order to ensure that the chip can be accurately controlled at the preset target temperature point or target temperature range under various load working conditions to meet the strict test standards, a highly sensitive and adaptable temperature control algorithm must be equipped, which can respond to the changes in the temperature of the chip in real time and accurately adjust the heating or cooling power.

[0006] Although the concept of local temperature control has been proposed, the temperature control methods in the prior art, especially their core control algorithms, often perform unsatisfactorily in the face of the severe temperature fluctuations inherent in chip testing caused by dynamic load changes. These methods have limitations in terms of regulation accuracy, response speed, and adaptability, making it difficult to achieve rapid, stable, and high-precision control of chip temperature.

[0007] In summary, the core technical problem existing in the prior art is: how to effectively cope with the severe temperature fluctuations of the chip caused by dynamic load changes under the condition of only local heating or cooling of the chip, and achieve rapid, stable, and high-precision control of the chip temperature to meet the strict requirements of extreme temperature testing. SUMMARY

[0008] Embodiments of the present application provide a chip temperature control method, device, equipment and medium, aiming to solve the problem of how to effectively cope with the severe temperature fluctuations of the chip caused by dynamic load changes under the condition of only local heating or cooling of the chip, and achieve rapid, stable, and high-precision control of the chip temperature to meet the strict requirements of extreme temperature testing.

[0009] In a first aspect, embodiments of the present application provide a chip temperature control method, wherein a locally heating or cooling of a chip under test is performed by an independently arranged temperature control module, and the method comprises:

[0010] Based on the collected temperature of the chip under test, a temperature fluctuation standard deviation and an error change rate of the chip under test are obtained;

[0011] According to the temperature fluctuation standard deviation and the error change rate, a target weighting coefficient combination in a pre-set fuzzy rule base is matched;

[0012] Based on the target weighting coefficient combination and pre-set basic PID parameters, dynamic PID control parameters are determined;

[0013] According to the dynamic PID control parameters, a pre-set PID control algorithm is used to adjust the power output of the temperature control module to control the temperature of the chip.

[0014] A further technical solution is that the method further comprises: if the current time reaches a pre-set sampling node, the temperature of the chip under test is collected, wherein the time interval between adjacent sampling nodes is the same.

[0015] A further technical solution is that the temperature fluctuation standard deviation is calculated by the following method:

[0016] The standard deviation of the temperature values of the chip under test at a continuous pre-set number of sampling nodes is calculated to obtain the temperature fluctuation standard deviation.

[0017] A further technical solution is that the error change rate is calculated in the following way:

[0018] The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

[0019] A further technical solution is that the fuzzy rule base contains multiple weighted coefficient combinations, each weighted coefficient combination corresponding to different combinations of temperature fluctuation levels and error change rate levels.

[0020] A further technical solution is that the temperature fluctuation level is divided into low fluctuation, medium fluctuation, and high fluctuation according to a preset standard deviation range;

[0021] The error change rate levels are divided into slow change, medium change, and fast change according to the preset error change rate range.

[0022] A further technical solution is that the basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient; the weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively; the step of determining the dynamic PID control parameters based on the target weighting coefficient combination and the preset basic PID parameters includes:

[0023] The basic proportional coefficient, basic integral coefficient, and basic derivative coefficient are multiplied by the corresponding weighting coefficients in the target weighting coefficient combination to obtain the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient, wherein the dynamic PID control parameters include the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient.

[0024] Secondly, embodiments of the present invention also provide a chip temperature control device, which includes a unit for performing the above-described method.

[0025] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0026] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0027] This invention provides a chip temperature control method, apparatus, device, and medium. The method includes: acquiring the standard deviation of temperature fluctuation and the rate of change of error of the chip under test based on the collected temperature; matching a target weighted coefficient combination from a preset fuzzy rule base according to the standard deviation of temperature fluctuation and the rate of change of error; determining dynamic PID control parameters based on the target weighted coefficient combination and preset basic PID parameters; and adjusting the power output of the temperature control module to control the chip temperature using a preset PID control algorithm according to the dynamic PID control parameters. This invention can effectively cope with drastic temperature fluctuations caused by dynamic load changes in chips under conditions where only local heating or cooling is applied, achieving rapid, stable, and high-precision control of chip temperature to meet the stringent requirements of extreme temperature testing. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic flowchart of a chip temperature control method provided in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the temperature control module of the chip under test provided in an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a fuzzy rule base provided in an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram illustrating the adjustment principle of the fuzzy rule base provided in an embodiment of the present invention;

[0033] Figure 5 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0036] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0037] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0039] Please see Figure 1 This invention provides a chip temperature control method, which includes the following steps:

[0040] S1, based on the collected temperature of the chip under test, obtain the standard deviation of the temperature fluctuation and the error change rate of the chip under test.

[0041] In practice, a separately configured temperature control module locally heats or cools the chip under test. (See also...) Figure 2 The temperature control module includes a thermoelectric cooler (TEC), a heat sink, and a fan assembly. It switches between heating and cooling modes by adjusting the current direction of the TEC. The TEC is mounted in close contact with the chip surface and is physically isolated from other components on the motherboard to avoid affecting them. Simultaneously, a temperature sensor collects the temperature of the chip under test.

[0042] In some preferred embodiments, if the current time reaches a preset sampling node, the temperature of the chip under test is collected, wherein the time interval between adjacent sampling nodes is the same. The sampling nodes can be set by those skilled in the art, and this invention is not specifically limited. In this invention, a sampling node is set at each preset time interval. The time interval can be set by those skilled in the art, and this invention is not specifically limited. In this invention, by setting sampling nodes with fixed time intervals (e.g., collecting temperature data every 0.1 seconds), a stable timing control basis is established. The periodic sampling mechanism ensures that the acquisition of temperature data has temporal uniformity, avoiding signal aliasing or data gaps caused by random sampling.

[0043] Furthermore, the standard deviation of temperature fluctuation and the rate of error change of the chip under test are obtained.

[0044] Specifically, the standard deviation of temperature fluctuation is calculated in the following way:

[0045] The standard deviation of the temperature values ​​of the chip under test at a consecutive preset number of sampling nodes is calculated to obtain the standard deviation of temperature fluctuation.

[0046] For example, the preset number can be 20, that is, to calculate the standard deviation of the temperature values ​​of the current sampling node and the previous 19 sampling nodes, and obtain the standard deviation of the temperature fluctuation at the current sampling node.

[0047] Because chip load changes have a time duration (such as a high power consumption state lasting 10 seconds when running a stress test software), a single sample cannot capture the fluctuation characteristics, while the standard deviation calculation of multi-period data can effectively filter random noise and truly reflect the intensity of temperature oscillations caused by load changes.

[0048] The rate of change of error is calculated in the following manner:

[0049] The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

[0050] In practice, the ratio of the difference between the temperature error of the current sampling node and the temperature error of the previous sampling node to the time interval between sampling nodes is calculated to obtain the error change rate at the current sampling node.

[0051] The error change rate is obtained by calculating the ratio of the temperature error difference between adjacent sampling nodes to the time interval. Its core function is to capture the acceleration of temperature deviation from the target. For example, when the chip power consumption increases sharply, the actual temperature will continue to be higher than the target value. At this time, the error difference between two adjacent cycles is positive. Dividing it by the fixed sampling interval yields a positive change rate, indicating that the temperature will continue to rise. Conversely, when the cooling power is excessive, a negative change rate warns of the risk of temperature overshoot.

[0052] S2, based on the standard deviation of temperature fluctuation and the rate of change of error, match the target weighting coefficient combination in the preset fuzzy rule base.

[0053] In practical implementation, the fuzzy rule base contains multiple weighted coefficient combinations, each corresponding to different combinations of temperature fluctuation levels and error change rate levels. The temperature fluctuation levels are categorized into low fluctuation, medium fluctuation, and high fluctuation based on a preset standard deviation range; the error change rate levels are categorized into slow change, medium change, and fast change based on a preset error change rate range. This categorization of temperature fluctuation levels and error change rate levels provides quantifiable decision boundaries for the fuzzy rule base. The core technology lies in transforming continuous physical quantities into discrete logical variables through preset range thresholds, avoiding the computational overhead of high-precision comparisons while retaining key operating condition characteristics. See also... Figure 3 In one example of this invention, the fuzzy rule base contains nine fuzzy rules. These rules categorize the actual scenario into several scenarios based on the standard deviation of temperature fluctuation and the rate of change of error: large fluctuation, medium fluctuation, small fluctuation, fast change, medium change, and slow change. The fluctuation and rate of change are combined for different scenarios to obtain nine fuzzy rules. The activation intensity is the weighting coefficient of the basic PID parameters after matching the corresponding fuzzy rule. From left to right, these are the weighting coefficients of the basic proportional coefficient, basic integral coefficient, and basic derivative coefficient. For example, 0.8, 6.0, and 0.5 are the weighting coefficients of the basic proportional coefficient, basic integral coefficient, and basic derivative coefficient, respectively.

[0054] In this invention, a pre-defined fuzzy rule base is used to construct a control strategy matrix covering the entire working scenario by establishing two-dimensional combined operating conditions (e.g., 3×3=9 rules) of temperature fluctuation level and error change rate level. Its technical advantage lies in decomposing complex nonlinear control problems into a finite number of discrete operating conditions, each corresponding to a set of optimized PID weighting coefficient combinations. For example, see... Figure 4 For the upper half (blue area), the weight of the proportional coefficient is reduced while the weight of the derivative coefficient is increased, weakening the impact of small temperature fluctuations on the temperature control unit's scheduling and suppressing temperature stabilization. For the lower half (red area), the weight of the proportional coefficient is increased while the weight of the derivative coefficient is decreased, which helps to cope with sudden temperature changes and improves the response speed of the temperature control unit.

[0055] Furthermore, c. Define membership rules, preset fluctuation membership degree, and preset error membership degree. Example:

[0056] Membership rules for temperature fluctuations: SMALL:[0,1); MEDIUM:[1,2); LARGE:[2,∞]. For temperature fluctuations, SMALL corresponds to low fluctuations, MEDIUM corresponds to medium fluctuations, and LARGE corresponds to high fluctuations.

[0057] Error variation membership rules: SMALL: [0, 0.125); MEDIUM: [0.125, 0.25); LARGE: [0.25, ∞]. SMALL corresponds to slow variation, MEDIUM to medium variation, and LARGE to fast variation.

[0058] Further, based on the standard deviation of temperature fluctuation and the rate of change of error, the type (SMALL, MED IUM, LARGE) corresponding to the standard deviation of temperature fluctuation and the rate of change of error is determined. Then, based on the type of the standard deviation of temperature fluctuation and the rate of change of error, the fuzzy rule base is queried to match the corresponding target weighted coefficient combination (i.e., ...). Figure 3 (Activation intensity in).

[0059] S3. Based on the target weighted coefficient combination and the preset basic PID parameters, determine the dynamic PID control parameters.

[0060] In this embodiment of the invention, dynamic PID control parameters are determined based on the target weighted coefficient combination and the preset basic PID parameters.

[0061] Specifically, the basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient. The weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively. Multiplying the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient by their corresponding weighting coefficients in the target weighting coefficient combination yields the actual proportional coefficient, the actual integral coefficient, and the actual derivative coefficient. The dynamic PID control parameters include the actual proportional coefficient, the actual integral coefficient, and the actual derivative coefficient.

[0062] S4. Based on the dynamic PID control parameters, a preset PID control algorithm is used to adjust the power output of the temperature control module to control the chip temperature.

[0063] In this embodiment of the invention, based on the dynamic PID control parameters, a preset PID control algorithm is used to adjust the power output of the temperature control module (specifically, to adjust the power output of the semiconductor cooling device) to control the chip temperature. The control method of the PID control algorithm is well known to those skilled in the art, and this invention does not specifically limit it. Because the dynamic PID control parameters are closer to the actual operating conditions of the chip under test, the accuracy of temperature control for the chip under test can be improved.

[0064] This invention proposes a chip temperature control method, comprising: acquiring the temperature fluctuation standard deviation and error rate of the chip under test based on the collected temperature; matching a target weighted coefficient combination in a preset fuzzy rule base according to the temperature fluctuation standard deviation and error rate; determining dynamic PID control parameters based on the target weighted coefficient combination and preset basic PID parameters; and adjusting the power output of the temperature control module to control the chip temperature using a preset PID control algorithm according to the dynamic PID control parameters. This invention can effectively address the drastic temperature fluctuations caused by dynamic load changes in chips under conditions where only local heating or cooling is applied, achieving rapid, stable, and high-precision control of chip temperature to meet the stringent requirements of extreme temperature testing. Specifically, the chip temperature control method provided by this invention achieves local temperature regulation of the chip under test through an independent temperature control module, effectively overcoming the inherent defect of other components on the motherboard failing due to their inability to withstand extreme temperatures in traditional overall environmental testing. Its core innovation lies in creatively using the temperature fluctuation standard deviation and error rate as collaborative decision indicators and dynamically correcting the PID control parameters based on a fuzzy rule base. Since the standard deviation of temperature fluctuation can quantify the intensity of temperature oscillations of a chip under dynamic load changes (e.g., the continuous temperature divergence caused by sudden changes in chip power consumption when running different pressure models), while the error change rate reflects the rate of deviation between the target temperature and the actual temperature in real time (e.g., the temperature rise slope per unit time when the chip is fully loaded), the two together constitute an accurate characterization of the system's dynamic characteristics. After the target weighted coefficient combination is matched according to the dual indicators, the system adjusts the preset basic PID parameters in real time with weights, so that the intensity of the proportional, integral, and derivative control dimensions adapts to the operating conditions. For example, when the error change rate increases significantly (indicating that the temperature is about to deviate from the controllable range), the cumulative deviation is eliminated faster by increasing the weight of the integral term; when the standard deviation of temperature fluctuation remains high (indicating severe load oscillations), the derivative action is enhanced to predictively suppress overshoot. This dynamic parameter adjustment mechanism fundamentally breaks through the response hysteresis bottleneck of traditional fixed-parameter PID control in nonlinear systems. It enables the effective smoothing of severe temperature fluctuations caused by sudden load changes even under the physical constraint of only local temperature control of the chip. Ultimately, it achieves rapid convergence and high-precision stable control of chip temperature in extreme testing environments, meeting the comprehensive and stringent requirements of chip development and verification for temperature control speed, stability, and accuracy.

[0065] Corresponding to the above chip temperature control method, the present invention also provides a chip temperature control device. This chip temperature control device includes a unit for performing the above chip temperature control method, and can be configured in a desktop computer, tablet computer, laptop computer, or other terminal. Specifically, the chip temperature control device includes:

[0066] The acquisition unit is used to acquire the standard deviation of the temperature fluctuation and the error change rate of the chip under test based on the collected temperature of the chip under test;

[0067] The matching unit is used to match the target weighted coefficient combination in the preset fuzzy rule base according to the standard deviation of temperature fluctuation and the error change rate;

[0068] The determining unit is used to determine the dynamic PID control parameters based on the target weighted coefficient combination and the preset basic PID parameters;

[0069] The adjustment unit is used to adjust the power output of the temperature control module to control the chip temperature according to the dynamic PID control parameters and a preset PID control algorithm.

[0070] In some preferred embodiments, the chip temperature control device further includes:

[0071] A sampling unit is used to collect the temperature of the chip under test when the current time reaches a preset sampling node, wherein the time interval between adjacent sampling nodes is the same.

[0072] In some preferred embodiments, the standard deviation of temperature fluctuation is calculated in the following manner:

[0073] The standard deviation of the temperature values ​​of the chip under test at a consecutive preset number of sampling nodes is calculated to obtain the standard deviation of temperature fluctuation.

[0074] In some preferred embodiments, the rate of change of error is calculated in the following manner:

[0075] The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

[0076] In some preferred embodiments, the fuzzy rule base contains multiple weighted coefficient combinations, each weighted coefficient combination corresponding to different combinations of temperature fluctuation levels and error change rate levels.

[0077] In some preferred embodiments, the temperature fluctuation levels are divided into low fluctuation, medium fluctuation, and high fluctuation according to a preset standard deviation range;

[0078] The error change rate levels are divided into slow change, medium change, and fast change according to the preset error change rate range.

[0079] In some preferred embodiments, the basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient; the weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively; determining the dynamic PID control parameters based on the target weighting coefficient combination and the preset basic PID parameters includes:

[0080] The basic proportional coefficient, basic integral coefficient, and basic derivative coefficient are multiplied by the corresponding weighting coefficients in the target weighting coefficient combination to obtain the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient, wherein the dynamic PID control parameters include the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient.

[0081] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned chip temperature control device and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0082] The aforementioned chip-based temperature control device can be implemented as a computer program, which can, for example... Figure 5 It runs on the computer device shown.

[0083] Please see Figure 5 , Figure 5 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0084] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0085] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it causes the processor 502 to perform a chip temperature control method.

[0086] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0087] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a chip temperature control method.

[0088] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0089] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps:

[0090] Based on the collected temperature of the chip under test, the standard deviation of the temperature fluctuation and the error change rate of the chip under test are obtained;

[0091] Based on the standard deviation of temperature fluctuation and the rate of change of error, a target weighting coefficient combination is matched with a preset fuzzy rule base;

[0092] Based on the target weighted coefficient combination and the preset basic PID parameters, the dynamic PID control parameters are determined;

[0093] Based on the dynamic PID control parameters, a preset PID control algorithm is used to adjust the power output of the temperature control module to control the chip temperature.

[0094] In some preferred embodiments, the method further includes: if the current time reaches a preset sampling node, collecting the temperature of the chip under test, wherein the time interval between adjacent sampling nodes is the same.

[0095] In some preferred embodiments, the standard deviation of temperature fluctuation is calculated in the following manner:

[0096] The standard deviation of the temperature values ​​of the chip under test at a consecutive preset number of sampling nodes is calculated to obtain the standard deviation of temperature fluctuation.

[0097] In some preferred embodiments, the rate of change of error is calculated in the following manner:

[0098] The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

[0099] In some preferred embodiments, the fuzzy rule base contains multiple weighted coefficient combinations, each weighted coefficient combination corresponding to different combinations of temperature fluctuation levels and error change rate levels.

[0100] In some preferred embodiments, the temperature fluctuation levels are divided into low fluctuation, medium fluctuation, and high fluctuation according to a preset standard deviation range;

[0101] The error change rate levels are divided into slow change, medium change, and fast change according to the preset error change rate range.

[0102] In some preferred embodiments, the basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient; the weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively; determining the dynamic PID control parameters based on the target weighting coefficient combination and the preset basic PID parameters includes:

[0103] The basic proportional coefficient, basic integral coefficient, and basic derivative coefficient are multiplied by the corresponding weighting coefficients in the target weighting coefficient combination to obtain the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient, wherein the dynamic PID control parameters include the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient.

[0104] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0105] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0106] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform the following steps:

[0107] Based on the collected temperature of the chip under test, the standard deviation of the temperature fluctuation and the error change rate of the chip under test are obtained;

[0108] Based on the standard deviation of temperature fluctuation and the rate of change of error, a target weighting coefficient combination is matched with a preset fuzzy rule base;

[0109] Based on the target weighted coefficient combination and the preset basic PID parameters, the dynamic PID control parameters are determined;

[0110] Based on the dynamic PID control parameters, a preset PID control algorithm is used to adjust the power output of the temperature control module to control the chip temperature.

[0111] In some preferred embodiments, the method further includes: if the current time reaches a preset sampling node, collecting the temperature of the chip under test, wherein the time interval between adjacent sampling nodes is the same.

[0112] In some preferred embodiments, the standard deviation of temperature fluctuation is calculated in the following manner:

[0113] The standard deviation of the temperature values ​​of the chip under test at a consecutive preset number of sampling nodes is calculated to obtain the standard deviation of temperature fluctuation.

[0114] In some preferred embodiments, the rate of change of error is calculated in the following manner:

[0115] The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

[0116] In some preferred embodiments, the fuzzy rule base contains multiple weighted coefficient combinations, each weighted coefficient combination corresponding to different combinations of temperature fluctuation levels and error change rate levels.

[0117] In some preferred embodiments, the temperature fluctuation levels are divided into low fluctuation, medium fluctuation, and high fluctuation according to a preset standard deviation range;

[0118] The error change rate levels are divided into slow change, medium change, and fast change according to the preset error change rate range.

[0119] In some preferred embodiments, the basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient; the weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively; determining the dynamic PID control parameters based on the target weighting coefficient combination and the preset basic PID parameters includes:

[0120] The basic proportional coefficient, basic integral coefficient, and basic derivative coefficient are multiplied by the corresponding weighting coefficients in the target weighting coefficient combination to obtain the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient, wherein the dynamic PID control parameters include the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient.

[0121] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0123] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0124] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0127] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A chip temperature control method, characterized in that, The method involves using an independently configured temperature control module to locally heat or cool the chip under test, and includes: Based on the collected temperature of the chip under test, the standard deviation of the temperature fluctuation and the error change rate of the chip under test are obtained; Based on the standard deviation of temperature fluctuation and the rate of change of error, a target weighting coefficient combination is matched with a preset fuzzy rule base; Based on the target weighted coefficient combination and the preset basic PID parameters, the dynamic PID control parameters are determined; Based on the dynamic PID control parameters, a preset PID control algorithm is used to adjust the power output of the temperature control module to control the chip temperature.

2. The chip temperature control method according to claim 1, characterized in that, The method further includes: if the current time reaches a preset sampling node, collecting the temperature of the chip under test, wherein the time interval between adjacent sampling nodes is the same.

3. The chip temperature control method according to claim 2, characterized in that, The standard deviation of the temperature fluctuation is calculated in the following way: The standard deviation of the temperature values ​​of the chip under test at a consecutive preset number of sampling nodes is calculated to obtain the standard deviation of temperature fluctuation.

4. The chip temperature control method according to claim 2, characterized in that, The rate of change of error is calculated in the following manner: The error change rate is obtained by calculating the ratio of the temperature error difference between two adjacent sampling nodes to the time interval between the sampling nodes.

5. The chip temperature control method according to claim 1, characterized in that, The fuzzy rule base contains multiple weighted coefficient combinations, each of which corresponds to different combinations of temperature fluctuation levels and error change rate levels.

6. The chip temperature control method according to claim 5, characterized in that, The temperature fluctuation levels are classified into low fluctuation, medium fluctuation, and high fluctuation according to a preset standard deviation range. The error change rate levels are divided into slow change, medium change, and fast change according to the preset error change rate range.

7. The chip temperature control method according to claim 1, characterized in that, The basic PID parameters include a basic proportional coefficient, a basic integral coefficient, and a basic derivative coefficient; the weighting coefficient combination includes weighting coefficients corresponding to the basic proportional coefficient, the basic integral coefficient, and the basic derivative coefficient, respectively. The determination of dynamic PID control parameters based on the target weighted coefficient combination and preset basic PID parameters includes: The basic proportional coefficient, basic integral coefficient, and basic derivative coefficient are multiplied by the corresponding weighting coefficients in the target weighting coefficient combination to obtain the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient, wherein the dynamic PID control parameters include the actual proportional coefficient, actual integral coefficient, and actual derivative coefficient.

8. A chip temperature control device, characterized in that, Includes a unit for performing the method as described in any one of claims 1-7.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-7.

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