Temperature control method of semiconductor measurement detection machine and related equipment

Abstract

The embodiment of the invention discloses a temperature control method and temperature control equipment of a semiconductor measurement and detection machine and a computer readable storage medium, which are used for controlling the temperature of the semiconductor measurement and detection machine under the condition of improving the accuracy of the temperature control of the semiconductor measurement and detection machine. The method comprises the steps of inputting a current temperature and a target temperature into a temperature PID control algorithm to obtain a first power corresponding to a heating capacitor output by the temperature PID control algorithm, inputting the target temperature into a deep learning model to obtain a second power corresponding to the heating capacitor output by the deep learning model, and determining a target power based on the first power and the second power, generating a power control command of the heating capacitor based on the target power, and sending the power control command of the heating capacitor to the semiconductor measurement detection machine, so that the semiconductor measurement detection machine controls the power of the heating capacitor to the target power based on the power control command.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of temperature control of semiconductor measurement and testing machines, and more specifically, to a temperature control method, a temperature control device, and a computer-readable storage medium for a semiconductor measurement and testing machine. Background Art

[0002] Semiconductor measurement and testing equipment is a device used for measurement and defect detection of semiconductor devices. In the semiconductor manufacturing process, semiconductor measurement and testing equipment is widely used in the wafer testing stage. Specifically, the detection device can be used to detect the surface of the wafer, and the computer equipment can process the detected image and extract the defects, and the processed image data can be stored in the computer equipment. The wafer defect detection image can be displayed in the computer equipment, and the distribution of defects on the wafer can be determined based on the detection image.

[0003] However, since the temperature of the semiconductor measurement and testing machine may be unstable, it will affect the measurement accuracy of the relevant measuring instruments in the semiconductor measurement and testing machine. For example, the laser will be affected by the temperature instability of the semiconductor measurement and testing machine, resulting in low accuracy of the measurement data. The existing solution is to manually control the temperature of the semiconductor measurement and testing machine based on experience. However, the error of manual control is large. Therefore, a temperature control method for the semiconductor measurement and testing machine is urgently needed to improve the accuracy of the temperature control of the semiconductor measurement and testing machine. Summary of the invention

[0004] The embodiments of the present application provide a temperature control method, a temperature control device and a computer-readable storage medium for a semiconductor measurement and testing machine, which are used to perform temperature control of the semiconductor measurement and testing machine while improving the accuracy of the temperature control of the semiconductor measurement and testing machine.

[0005] In a first aspect, an embodiment of the present application provides a temperature control method for a semiconductor measurement and testing machine, comprising:

[0006] Determine the current temperature and target temperature of semiconductor measurement and inspection equipment;

[0007] The current temperature and the target temperature are input into the temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to the preset error threshold; and the heating capacitor is a component of the circuit in the semiconductor measurement and detection machine;

[0008] The target temperature is input into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, the power corresponding to the heating capacitor is predicted when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required for the heating capacitor to reach when the temperature of the semiconductor measurement and detection machine reaches the target temperature;

[0009] determining a target power based on the first power and the second power, and generating a power control command for the heating capacitor based on the target power;

[0010] The power control command of the heating capacitor is sent to the semiconductor measurement and testing machine, so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command, so as to control the temperature of the semiconductor measurement and testing machine.

[0011] In a second aspect, an embodiment of the present application provides a temperature control device, including:

[0012] A determination unit, used to determine the current temperature and target temperature of the semiconductor measurement and inspection machine;

[0013] An input unit, used to input the current temperature and the target temperature into the temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of a circuit in a semiconductor measurement and detection machine;

[0014] The input unit is further used to input the target temperature into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, predict the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature;

[0015] a generating unit, configured to determine a target power based on the first power and the second power, and to generate a power control command for the heating capacitor based on the target power;

[0016] The sending unit is used to send a power control command of the heating capacitor to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and detection machine.

[0017] In a third aspect, an embodiment of the present application provides a temperature control device, including:

[0018] CPU, memory, input and output interfaces, wired or wireless network interfaces, and power supply;

[0019] The memory is either transient storage memory or persistent storage memory;

[0020] The central processing unit is configured to communicate with the memory and execute the instruction operation in the memory to execute the temperature control method of the semiconductor measurement and inspection machine.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which includes instructions. When the instructions are executed on a computer, the computer executes the temperature control method of the aforementioned semiconductor measurement and testing machine.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer executes the temperature control method of the aforementioned semiconductor measurement and testing machine.

[0023] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: the current temperature and the target temperature can be input into the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, the target temperature can be input into the pre-trained deep learning model to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power is determined based on the first power and the second power, and a power control command for the heating capacitor is generated based on the target power, and the power control command for the heating capacitor is sent to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command. The temperature control of the semiconductor measurement and detection machine can be performed based on the deep learning model and the temperature PID control algorithm, the temperature control of the semiconductor measurement and detection machine can be performed, and the accuracy of the temperature control of the semiconductor measurement and detection machine can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the architecture of a temperature control system of a semiconductor measurement and testing machine disclosed in an embodiment of the present application;

[0025] Figure 2 A schematic flow chart of a temperature control method for a semiconductor measurement and testing machine disclosed in an embodiment of the present application;

[0026] Figure 3 A schematic flow chart of another temperature control method for a semiconductor measurement and testing machine disclosed in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of a temperature control device disclosed in an embodiment of the present application;

[0028] Figure 5 This is a schematic diagram of the structure of another temperature control device disclosed in an embodiment of the present application;

[0029] Figure 6 This is a schematic diagram of the structure of another temperature control device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application provide a temperature control method, a temperature control device and a computer-readable storage medium for a semiconductor measurement and testing machine, which are used to perform temperature control of the semiconductor measurement and testing machine while improving the accuracy of the temperature control of the semiconductor measurement and testing machine.

[0031] See also Figure 1 In the embodiment of the present application, the architecture of the temperature control system of the semiconductor measurement and detection machine includes:

[0032] Temperature control device 101 and semiconductor measurement and testing machine 102. When performing temperature control on the semiconductor measurement and testing machine 102, the temperature control device 101 can obtain the current temperature and target temperature of the semiconductor measurement and testing machine 102, can determine the target power corresponding to the heating capacitor based on the current temperature and the target temperature, and generate a power control command for the heating capacitor, and can send the power control command to the semiconductor measurement and testing machine 102, so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command, so as to control the temperature of the semiconductor measurement and testing machine.

[0033] based on Figure 1 The temperature control system of the semiconductor measurement and inspection equipment shown in the figure is shown in the figure. Figure 2 , Figure 2 The present invention discloses a method for controlling the temperature of a semiconductor measuring and testing machine. The method includes:

[0034] 201. Determine the current temperature and target temperature of a semiconductor measurement and testing machine.

[0035] In this embodiment, when the temperature of the semiconductor measurement and testing machine is controlled, the current temperature and the target temperature of the semiconductor measurement and testing machine can be determined.

[0036] 202. Input the current temperature and the target temperature into the temperature PID control algorithm, and adjust the error between the current temperature and the target temperature by the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of the circuit in the semiconductor measurement and detection machine.

[0037] After determining the current temperature and target temperature of the semiconductor measurement and detection machine, the current temperature and target temperature can be input into the temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of the circuit in the semiconductor measurement and detection machine.

[0038] Among them, the method of adjusting the error between the current temperature and the target temperature by the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm may be that the temperature PID control algorithm adjusts the current temperature according to the size, duration and / or change rate of the error to adjust the error to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm. Other reasonable methods may also be used, which are not specifically limited here.

[0039] 203. Input the target temperature into a pre-trained deep learning model. After the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, the power corresponding to the heating capacitor is predicted when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature.

[0040] The current temperature and the target temperature are input into the temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required to be achieved by the heating capacitor when the error is less than or equal to the preset error threshold; and after the heating capacitor is a component of the circuit in the semiconductor measurement and detection machine, the target temperature can be input into a pre-trained deep learning model, and the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature.

[0041] 204. Determine a target power based on the first power and the second power, and generate a power control command for the heating capacitor based on the target power.

[0042] The target temperature is input into a pre-trained deep learning model. The pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and then predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state. After obtaining the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power can be determined based on the first power and the second power, and a power control command for the heating capacitor can be generated based on the target power.

[0043] 205. Send a power control command of the heating capacitor to the semiconductor measurement and testing machine, so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command, so as to control the temperature of the semiconductor measurement and testing machine.

[0044] After determining the target power based on the first power and the second power and generating a power control command for the heating capacitor based on the target power, the power control command for the heating capacitor can be sent to a semiconductor measurement and testing machine, so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and testing machine.

[0045] In the embodiment of the present application, the current temperature and the target temperature can be input into the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, the target temperature can be input into the pre-trained deep learning model to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power can be determined based on the first power and the second power, and a power control command for the heating capacitor can be generated based on the target power, and the power control command for the heating capacitor can be sent to the semiconductor measurement and detection machine so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command. The temperature control of the semiconductor measurement and detection machine can be performed based on the deep learning model and the temperature PID control algorithm, the temperature control of the semiconductor measurement and detection machine can be performed, and the accuracy of the temperature control of the semiconductor measurement and detection machine can be improved.

[0046] In the embodiment of the present application, there are multiple methods for adjusting the error between the current temperature and the target temperature by the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, based on Figure 2 The temperature control method of the semiconductor measurement and inspection machine shown in the figure, one of the methods is described below.

[0047] In this embodiment, when the temperature of the semiconductor measurement and testing machine is controlled, the current temperature and the target temperature of the semiconductor measurement and testing machine can be determined.

[0048] After determining the current temperature and target temperature of the semiconductor measurement and detection machine, the current temperature and target temperature can be input into the temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of the circuit in the semiconductor measurement and detection machine.

[0049] Among them, the method for adjusting the error between the current temperature and the target temperature by the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm can be that the temperature PID control algorithm adjusts the current temperature according to the size, duration and / or change rate of the error to adjust the error to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm.

[0050] Specifically, the temperature PID control algorithm is a PID control algorithm, and the working principle of the PID control algorithm is based on feedback control of the error. The PID algorithm is a classic control algorithm that minimizes the system response error by continuously adjusting the output of the controller. The PID algorithm includes three main control parameters: proportional gain, integral gain, and derivative gain. By adjusting these three parameters, the PID controller can adjust the error according to the actual response of the system. The proportional gain adjusts the output of the controller according to the size of the error. When the error increases, the proportional gain increases the output of the controller, thereby increasing the response of the system; when the error decreases, the proportional gain decreases the output of the controller, thereby reducing the response of the system. The integral gain adjusts the output of the controller according to the duration of the error. When the error persists, the integral gain increases the output of the controller, thereby speeding up the response of the system; when the error gradually decreases, the integral gain decreases the output of the controller, thereby reducing the response of the system. The derivative gain adjusts the output of the controller according to the rate of change of the error. When the error rate of change is large, the differential gain increases the controller output, thereby increasing the system's response speed; when the error rate of change is small, the differential gain decreases the controller output, thereby reducing the system's response speed. It can be understood that by continuously adjusting the proportional gain, integral gain, and differential gain, the PID controller is able to keep the error to a minimum when the system reaches a stable state and can respond quickly to external disturbances. In summary, the working principle of the PID control algorithm is to adjust the controller output according to the size, duration, and rate of change of the error to minimize the system's response error.

[0051] Among them, the method of adjusting the error between the current temperature and the target temperature by the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm can be that the error between the current temperature and the target temperature is adjusted by the incremental PID algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, wherein the temperature PID control algorithm is an incremental PID algorithm.

[0052] It is worth mentioning that the error between the current temperature and the target temperature is adjusted by the incremental PID algorithm, which can eliminate the tolerance and make the calculated first power corresponding to the heating capacitor more accurate. It is understandable that in addition to adjusting the error between the current temperature and the target temperature by the incremental PID algorithm, the error between the current temperature and the target temperature can also be adjusted by the position PID algorithm, or other reasonable temperature PID control algorithms, which are not specifically limited here.

[0053] After determining the target temperature of the semiconductor measurement and inspection machine, the target temperature can be input into a pre-trained deep learning model. The pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required for the heating capacitor to reach when the temperature of the semiconductor measurement and inspection machine reaches the target temperature.

[0054] It is worth mentioning that a method for inputting the current temperature and the target temperature into the temperature PID control algorithm, and having the temperature PID control algorithm adjust the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm may be to execute a first task through a first thread; wherein the first task is to input the current temperature and the target temperature into the temperature PID control algorithm, and have the temperature PID control algorithm adjust the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; and input the target temperature into a pre-trained deep learning model, and the pre-trained deep learning model is based on The method for predicting the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state after adjusting the voltage and current of the heating capacitor according to the target temperature, so as to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model may be to execute the second task through the second thread; wherein, the second task is to input the target temperature into the pre-trained deep learning model, and the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and then predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain the task corresponding to the second power corresponding to the heating capacitor output by the pre-trained deep learning model; and the first thread and the second thread are executed in parallel.

[0055] Before inputting the current temperature and the target temperature into the temperature PID control algorithm, the target temperature sample can be obtained first, wherein the target temperature sample is marked with the second power corresponding to the heating capacitor. Then, the target temperature sample is input into the deep learning model to obtain the predicted second power corresponding to the target temperature sample output by the deep learning model. Finally, the loss between the predicted second power and the marked second power is calculated according to the regression loss function. When the loss meets the convergence condition, the trained deep learning model is obtained.

[0056] The target temperature is input into a pre-trained deep learning model. The pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and then predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state. After obtaining the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power can be determined based on the first power and the second power, and a power control command for the heating capacitor can be generated based on the target power.

[0057] Among them, the method for determining the target power based on the first power and the second power can be to first determine the first weight corresponding to the first power and the second weight corresponding to the second power, and then perform weighted averaging on the first power, the second power, the first weight and the second weight to obtain the target power. Specifically, if the first weight and the second weight are both 50%, the first power and the second power can be averaged to obtain the target power. It is understandable that there can also be other reasonable methods for determining the target power based on the first power and the second power, which are not specifically limited here.

[0058] After determining the target power based on the first power and the second power and generating a power control command for the heating capacitor based on the target power, the power control command for the heating capacitor can be sent to a semiconductor measurement and testing machine, so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and testing machine.

[0059] Among them, the power control command of the heating capacitor is sent to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command. After controlling the temperature of the semiconductor measurement and detection machine, the control effect of controlling the power of the heating capacitor can be obtained first, and then the deep learning model is fine-tuned based on the control effect to obtain the fine-tuned deep learning model.

[0060] It is worth mentioning that model fine-tuning can adjust the pre-trained model according to task requirements. Global fine-tuning, layer fine-tuning or parameter freezing can be selected according to the specific situation to achieve better performance and generalization ability.

[0061] For details, please refer to Figure 3 , Figure 3 Another temperature control method of a semiconductor measuring and testing machine disclosed in an embodiment of the present application is as follows: Figure 3It can be known that the input sensor signal and the control state binary signal can be obtained, and the temperature PID control algorithm can calculate the ratio, integration, and differentiation of the error between the current value (current temperature) of the control item (temperature item) and the target value (target temperature). The temperature PID control algorithm can be an incremental PID, and the power of the current heating capacitor can be calculated by the incremental PID algorithm. The deep learning prediction algorithm models and predicts the power of the heating capacitor, and the incremental PID algorithm can be parallel with the AI ​​prediction algorithm (pre-trained deep learning model). The first power output by the incremental PID calculation and the second power output by the AI ​​prediction algorithm can be cross-checked, and regularized and added to merge into a higher accuracy result (target power). Among them, for the AI ​​prediction algorithm, summarized historical data can be obtained to establish a model, and reinforcement learning can be used to fine-tune the AI ​​prediction algorithm, and the AI ​​prediction algorithm (AI model) can be continuously updated and fine-tuned according to the actual control situation.

[0062] In this embodiment, the current temperature and the target temperature can be input into the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, the target temperature can be input into the pre-trained deep learning model to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power can be determined based on the first power and the second power, and the power control command of the heating capacitor can be generated based on the target power, and the power control command of the heating capacitor can be sent to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command. The temperature control of the semiconductor measurement and detection machine can be performed based on the deep learning model and the temperature PID control algorithm, the temperature control of the semiconductor measurement and detection machine can be performed, the accuracy of the temperature control of the semiconductor measurement and detection machine can be improved, and the accuracy of the measurement data of the relevant measuring instruments in the semiconductor measurement and detection machine can be improved. Secondly, the temperature control of the semiconductor measurement and detection machine can be performed based on the deep learning model and the temperature PID control algorithm, the temperature can be stabilized in real time, and the measurement accuracy of the machine can be improved while the stability of the machine's measurement performance can be improved. Furthermore, the temperature can be controlled efficiently, accurately and quickly, and it can be unaffected by the ambient temperature, and instantaneous feedback can be calculated in real time to achieve high-sensitivity temperature control. Furthermore, PID can be integrated with deep learning to improve control accuracy, and cross-validation can be used to avoid noise interference and reduce measurement errors. Furthermore, multi-threaded parallel algorithms can be used to efficiently and quickly process multi-memory data in batches, with a speed of 1ms, almost real-time processing, and real-time control, which can reduce the time for drawing. Furthermore, the error between the current temperature and the target temperature can be adjusted by the incremental PID algorithm, and the tolerance can be eliminated, so that the first power corresponding to the calculated heating capacitor is more accurate. Finally, the model can be fine-tuned to adjust the pre-trained model according to the task requirements. Global fine-tuning, hierarchical fine-tuning or parameter freezing can be selected according to the specific situation to achieve better performance and generalization capabilities.

[0063] The temperature control method of the semiconductor measurement and detection machine in the embodiment of the present application is described above. The temperature control device in the embodiment of the present application is described below. Figure 4 , one embodiment of the temperature control device in the embodiment of the present application includes:

[0064] A determination unit 401 is used to determine the current temperature and target temperature of the semiconductor measurement and testing machine;

[0065] An input unit 402 is used to input the current temperature and the target temperature into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of a circuit in the semiconductor measurement and detection machine;

[0066] The input unit 402 is further used to input the target temperature into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, predict the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature;

[0067] A generating unit 403, configured to determine a target power based on the first power and the second power, and generate a power control command for the heating capacitor based on the target power;

[0068] The sending unit 404 is used to send the power control command of the heating capacitor to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and detection machine.

[0069] In the embodiment of the present application, the current temperature and the target temperature can be input into the temperature PID control algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm, the target temperature can be input into the pre-trained deep learning model to obtain the second power corresponding to the heating capacitor output by the pre-trained deep learning model, the target power can be determined based on the first power and the second power, and a power control command for the heating capacitor can be generated based on the target power, and the power control command for the heating capacitor can be sent to the semiconductor measurement and detection machine so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command. The temperature control of the semiconductor measurement and detection machine can be performed based on the deep learning model and the temperature PID control algorithm, the temperature control of the semiconductor measurement and detection machine can be performed, and the accuracy of the temperature control of the semiconductor measurement and detection machine can be improved.

[0070] The temperature control device in the embodiment of the present application is described in detail below. Figure 5 Another embodiment of the temperature control device in the embodiment of the present application includes:

[0071] A determination unit 501 is used to determine the current temperature and target temperature of the semiconductor measurement and testing machine;

[0072] An input unit 502 is used to input the current temperature and the target temperature into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of a circuit in the semiconductor measurement and detection machine;

[0073] The input unit 502 is further used to input the target temperature into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, predict the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature;

[0074] A generating unit 503, configured to determine a target power based on the first power and the second power, and generate a power control command for the heating capacitor based on the target power;

[0075] The sending unit 504 is used to send the power control command of the heating capacitor to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and detection machine.

[0076] The input unit 502 is specifically used to adjust the current temperature according to the size, duration and / or change rate of the error by the temperature PID control algorithm, so as to adjust the error and obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm.

[0077] The input unit 502 is specifically used to adjust the error between the current temperature and the target temperature by the incremental PID algorithm to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm; the temperature PID control algorithm is an incremental PID algorithm.

[0078] The temperature control device further includes: an obtaining unit 505; a calculating unit 506;

[0079] An obtaining unit 505 is used to obtain a target temperature sample; wherein the target temperature sample is marked with a second power corresponding to the heating capacitor;

[0080] The input unit 502 is further configured to input the target temperature sample into a deep learning model to obtain a predicted second power corresponding to the target temperature sample output by the deep learning model;

[0081] The calculation unit 506 is used to calculate the loss between the predicted second power and the marked second power according to the regression loss function, and when the loss meets the convergence condition, a trained deep learning model is obtained.

[0082] The temperature control device further comprises: a fine-tuning unit 507;

[0083] The obtaining unit 505 is further used to obtain a control effect of controlling the power of the heating capacitor;

[0084] The fine-tuning unit 507 is used to fine-tune the deep learning model based on the control effect to obtain a fine-tuned deep learning model.

[0085] The generating unit 503 is specifically configured to determine a first weight corresponding to the first power and a second weight corresponding to the second power, and perform weighted averaging processing on the first power, the second power, the first weight and the second weight to obtain the target power.

[0086] The input unit 502 is specifically used to execute a first task through a first thread; wherein, the first task is to input the current temperature and the target temperature into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a task corresponding to the first power of the heating capacitor output by the temperature PID control algorithm; and execute a second task through a second thread; wherein, the second task is to input the target temperature into a pre-trained deep learning model, and the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, and predicts the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a task corresponding to the second power of the heating capacitor output by the pre-trained deep learning model; and the first thread and the second thread are executed in parallel.

[0087] In this embodiment, each unit in the temperature control device performs the above Figure 2 The operation of the temperature control device in the illustrated embodiment will not be described in detail here.

[0088] See below Figure 6Another embodiment of the temperature control device 600 in the embodiment of the present application includes:

[0089] CPU 601, memory 605, input / output interface 604, wired or wireless network interface 603 and power supply 602;

[0090] The memory 605 is a temporary storage memory or a permanent storage memory;

[0091] The CPU 601 is configured to communicate with the memory 605 and execute the instructions in the memory 605 to perform the aforementioned Figure 2 The method in the illustrated embodiment.

[0092] The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium includes instructions, when the instructions are executed on a computer, the computer executes the aforementioned Figure 2 The method in the illustrated embodiment.

[0093] The present application also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the aforementioned Figure 2 The method in the illustrated embodiment.

[0094] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0095] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0096] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A temperature control method for a semiconductor measuring and testing machine, characterized in that: include: Determine the current temperature and target temperature of semiconductor measurement and inspection equipment; The current temperature and the target temperature are input into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of a circuit in the semiconductor measurement and detection machine; The target temperature is input into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, the power corresponding to the heating capacitor is predicted when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature; determining a target power based on the first power and the second power, and generating a power control command for the heating capacitor based on the target power; The power control command of the heating capacitor is sent to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command, so as to control the temperature of the semiconductor measurement and detection machine.

2. The method according to claim 1, characterized in that The temperature PID control algorithm is used to adjust the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm, including: The temperature PID control algorithm adjusts the current temperature according to the size, duration and / or change rate of the error to adjust the error, so as to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm.

3. The method according to claim 1, characterized in that The temperature PID control algorithm is an incremental PID algorithm; The temperature PID control algorithm is used to adjust the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm, including: The incremental PID algorithm is used to adjust the error between the current temperature and the target temperature to obtain the first power corresponding to the heating capacitor output by the temperature PID control algorithm.

4. The method according to claim 1, characterized in that: Before inputting the target temperature into the pre-trained deep learning model, the method further includes: Obtaining a target temperature sample; wherein the target temperature sample is marked with the second power corresponding to the heating capacitor; Inputting the target temperature sample into a deep learning model to obtain a predicted second power corresponding to the target temperature sample output by the deep learning model; The loss between the predicted second power and the labeled second power is calculated according to the regression loss function, and when the loss meets the convergence condition, a trained deep learning model is obtained.

5. The method according to any one of claims 1 to 4, characterized in that: After sending the power control command of the heating capacitor to the semiconductor measurement and testing machine so that the semiconductor measurement and testing machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and testing machine, the method further includes: Obtaining a control effect of controlling the power of the heating capacitor; The deep learning model is fine-tuned based on the control effect to obtain a fine-tuned deep learning model.

6. The method according to any one of claims 1 to 4, characterized in that The determining the target power based on the first power and the second power includes: Determine a first weight corresponding to the first power and a second weight corresponding to the second power; The first power, the second power, the first weight, and the second weight are weighted averaged to obtain the target power.

7. The method according to any one of claims 1 to 4, characterized in that The current temperature and the target temperature are input into a temperature PID control algorithm, and the temperature PID control algorithm adjusts an error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm, including: Executing a first task through a first thread; wherein the first task is to input the current temperature and the target temperature into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a task corresponding to a first power corresponding to the heating capacitor output by the temperature PID control algorithm; The target temperature is input into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, the power corresponding to the heating capacitor is predicted when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model, including: A second task is executed through a second thread; wherein, the second task is to input the target temperature into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, predict the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a task corresponding to the second power corresponding to the heating capacitor output by the pre-trained deep learning model; and the first thread and the second thread are executed in parallel.

8. A temperature control device, characterized in that: include: A determination unit, used to determine the current temperature and target temperature of the semiconductor measurement and inspection machine; An input unit, used for inputting the current temperature and the target temperature into a temperature PID control algorithm, and the temperature PID control algorithm adjusts the error between the current temperature and the target temperature to obtain a first power corresponding to the heating capacitor output by the temperature PID control algorithm; wherein the first power represents the power required by the heating capacitor when the error is less than or equal to a preset error threshold; and the heating capacitor is a component of a circuit in the semiconductor measurement and detection machine; The input unit is further used to input the target temperature into a pre-trained deep learning model, and after the pre-trained deep learning model adjusts the voltage and current of the heating capacitor based on the target temperature, predict the power corresponding to the heating capacitor when the voltage and current of the heating capacitor reach a stable state, so as to obtain a second power corresponding to the heating capacitor output by the pre-trained deep learning model; wherein the second power represents the power required to be achieved by the heating capacitor when the temperature of the semiconductor measurement and detection machine reaches the target temperature; a generating unit, configured to determine a target power based on the first power and the second power, and to generate a power control command for the heating capacitor based on the target power; A sending unit is used to send a power control command of the heating capacitor to the semiconductor measurement and detection machine, so that the semiconductor measurement and detection machine controls the power of the heating capacitor to the target power based on the power control command to control the temperature of the semiconductor measurement and detection machine.

9. A temperature control device, characterized in that: include: CPU and memory; The memory is a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium comprises instructions, and when the instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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