Temperature control method, device and equipment of electrostatic chuck and storage medium

Abstract

The application discloses a temperature control method, device and equipment of an electrostatic chuck and a storage medium, and specifically discloses the following: an optical fiber temperature sensor arranged in the electrostatic chuck is used to obtain a real-time internal temperature measurement value of the electrostatic chuck; a predicted surface temperature measurement value of the electrostatic chuck is obtained based on a pre-generated temperature calibration parameter and in combination with the real-time internal temperature measurement value; a calibration test is performed through a preset simulation platform, the electrostatic chuck is controlled to reach at least one preset simulation temperature, and an internal temperature first measurement value and a surface temperature second measurement value of the electrostatic chuck under the preset simulation temperature are synchronously obtained; the temperature calibration parameter is determined based on the first measurement value and the second measurement value; the first measurement value is obtained based on the optical fiber temperature sensor; the second measurement value is obtained based on a preset infrared temperature measuring device; a temperature control instruction is generated according to a difference between the predicted surface temperature measurement value and a preset target temperature value, and temperature control of the electrostatic chuck is realized based on the temperature control instruction.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing technology, and in particular to a method, apparatus, equipment and storage medium for temperature control of an electrostatic chuck. Background Technology

[0002] Currently, to achieve precise temperature control of an ESC (Electrostatic Chuck), temperature measurement is typically performed first, followed by temperature regulation using a PID (Proportional-Integral-Derivative) controller. Two main approaches are used for temperature measurement: the first is a direct measurement approach based on a contact temperature sensor, where the sensor is embedded or attached to the inside of the ESC, and the temperature of the working surface is indirectly calculated by measuring the internal temperature; the second is a measurement approach based on a non-contact infrared temperature sensor, which directly measures the infrared radiation energy on the surface of the ESC to obtain its surface temperature.

[0003] While the above solutions can achieve temperature control of the electrostatic chuck, they have significant technical limitations: In the temperature measurement stage, contact sensors, due to their thermal inertia and installation location limitations, exhibit significant temperature lag, failing to reflect real-time temperature, and there is a temperature difference between the measured internal temperature and the working surface of the electrostatic chuck that is difficult to accurately compensate for. Non-contact infrared sensors, on the other hand, are susceptible to measurement path obstruction, changes in surface emissivity, and environmental electromagnetic interference, leading to unstable measurement signals and drift. Furthermore, neither of these measurement methods can avoid measurement errors caused by process variations and device aging, and both require inefficient manual periodic calibration. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, device, and storage medium for temperature control of an electrostatic chuck, aiming to solve the problems of insufficient temperature control accuracy, slow response speed, poor wafer surface temperature uniformity, and low long-term process stability caused by existing electrostatic chuck temperature control methods, such as temperature measurement lag, gradient error between internal temperature measurement results and chuck surface, susceptibility to interference of temperature sensor signals, and reliance on inefficient manual periodic calibration.

[0005] In a first aspect, embodiments of this disclosure provide a method for temperature control of an electrostatic chuck, comprising: The real-time internal temperature measurement value of the electrostatic chuck is obtained by using an optical fiber temperature sensor installed inside the electrostatic chuck. Based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value, the predicted surface temperature measurement value of the electrostatic chuck is obtained; fiber optic temperature sensor. Based on the difference between the predicted surface temperature measurement value and the preset target temperature value, a temperature control command is generated, and the temperature of the electrostatic chuck is controlled based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

[0006] Secondly, embodiments of this disclosure provide a temperature control device for an electrostatic chuck, the device comprising: The acquisition module is used to acquire the real-time internal temperature measurement value of the electrostatic chuck through an optical fiber temperature sensor set inside the electrostatic chuck. The prediction module is used to obtain the predicted surface temperature measurement value of the electrostatic chuck based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value. The control module is used to generate a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value, and to perform temperature control on the electrostatic chuck based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

[0007] Thirdly, embodiments of this disclosure provide an electronic device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect above.

[0008] Fourthly, embodiments of this disclosure provide a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the steps of the method described in the first aspect above.

[0009] Fifthly, embodiments of this disclosure provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0010] The at least one technical solution provided by the embodiments of the present invention can achieve the following technical effects: In this embodiment of the invention, firstly, a fiber optic temperature sensor embedded inside the electrostatic chuck acquires real-time internal temperature measurements. Then, based on temperature calibration parameters generated from a pre-executed calibration test, these internal temperature measurements are mapped to predicted temperatures on the working surface of the electrostatic chuck. This calibration test involves controlling the electrostatic chuck to reach various preset temperatures on a dedicated simulation platform, simultaneously recording a first internal temperature measurement from the fiber optic temperature sensor and a second surface temperature measurement directly from an infrared thermometer, to obtain temperature calibration parameters based on these two measurements. Finally, a control command is generated based on the difference between the predicted surface temperature and the target temperature, driving a heater to precisely adjust the temperature of the electrostatic chuck.

[0011] This invention enables direct internal temperature measurement using a fast-response, electromagnetically interference-resistant fiber optic temperature sensor. This overcomes the measurement lag caused by the high thermal inertia of traditional contact sensors, as well as the susceptibility to interference and signal instability of non-contact infrared sensors in the process environment. Furthermore, through infrared-based calibration testing, internal measurements are automatically and in real-time compensated and mapped to accurate surface temperature predictions. This fundamentally solves the problem of difficult-to-compensate gradient errors between internal temperature and the working surface, and completely eliminates the reliance on inefficient, periodic manual calibration. This lays a reliable measurement foundation for achieving high-precision, high-response temperature control. Attached Figure Description

[0012] Figure 1 This is one of the flowcharts illustrating a temperature control method for an electrostatic chuck according to an embodiment of the present invention; Figure 2 A schematic diagram illustrating a scenario of a temperature control method for an electrostatic chuck provided in an embodiment of the present invention; Figure 3 A second schematic flowchart illustrating a temperature control method for an electrostatic chuck according to an embodiment of the present invention; Figure 4A third schematic flowchart illustrating a temperature control method for an electrostatic chuck according to an embodiment of the present invention; Figure 5 A schematic diagram of the module composition of a temperature control device 500 for an electrostatic chuck provided in one embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0015] Please see Figure 1 , Figure 1 This is one of the flowcharts illustrating a temperature control method for an electrostatic chuck according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps: Step 102: Obtain the real-time internal temperature measurement value of the electrostatic chuck by using the fiber optic temperature sensor installed inside the electrostatic chuck.

[0016] Step 104: Based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value, obtain the predicted surface temperature measurement value of the electrostatic chuck.

[0017] Step 106: Generate a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value, and perform temperature control on the electrostatic chuck based on the temperature control command.

[0018] In embodiments of the present invention, a fiber optic temperature sensor disposed inside the electrostatic chuck can be used to obtain real-time internal temperature measurements of the electrostatic chuck. The fiber optic temperature sensor is a high-precision temperature measurement sensor based on optical principles, and can primarily include fluorescent fiber optic sensors and DBR (Distributed Bragg Reflector) fiber laser sensors. DBR fiber laser sensors achieve measurement by detecting changes in grating wavelength with temperature, and can significantly improve temperature resolution to 0.1℃, thereby facilitating precise temperature control of the electrostatic chuck.

[0019] Specifically, real-time sensing of the internal temperature of an electrostatic chuck can be achieved by embedding a fiber optic temperature sensor in a designated location on the back, base, or inside the ceramic heating element of the chuck. For example, taking a fluorescent fiber optic temperature sensor as an example, when a fluorescent material is irradiated with a specific wavelength, such as blue light, it emits fluorescence of that specific wavelength. The lifetime or intensity of this fluorescence has a definite functional relationship with the temperature of the environment in which the fluorescent material is located. The sensor can calculate the precise temperature value at the location of the sensor probe by measuring the decay change of the fluorescence lifetime or intensity and based on a pre-calibrated temperature-fluorescence characteristic curve. This calculated temperature value is the real-time internal temperature measurement of the electrostatic chuck. Fiber optic temperature sensors can have millisecond-level fast response characteristics, and their signals can be transmitted through optical fibers, completely unaffected by electromagnetic interference from strong electric and magnetic fields in the working environment of the electrostatic chuck, thus providing reliable raw data on the internal temperature.

[0020] In embodiments of the present invention, after acquiring the real-time internal temperature measurement value of the electrostatic chuck, the predicted surface temperature measurement value of the electrostatic chuck can be obtained based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value. The temperature calibration parameters can be generated based on calibration tests and are used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value. The calibration test can be performed using a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature range, and simultaneously acquire a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck within the preset simulated temperature range. The temperature calibration parameters are determined based on the simultaneously acquired first and second measurement values. The first measurement value can be acquired using a fiber optic temperature sensor, while the second measurement value can be acquired using a preset infrared temperature measurement device. The preset simulated temperature range can be determined based on a preset simulated temperature value and a preset fluctuation range. The preset fluctuation range can be ±0.2℃, meaning that the temperature range formed by fluctuations of 0.2℃ above and below the preset simulated temperature value is defined as the preset simulated temperature range.

[0021] In one example, the electrostatic chuck ceramic can be calibrated using a preset simulation platform, i.e., a calibration test platform, before it leaves the factory. A high-precision, high-response infrared temperature sensor probe can be integrated into a specific location within the cavity of this calibration test platform. This probe can directly perform non-contact, real-time temperature measurement of the electrostatic chuck surface through a specially designed high-purity germanium glass infrared transmission window. Compared to traditional contact-based OnWafer methods, this provides a faster and more direct temperature measurement response. OnWafer, as a traditional contact-based temperature measurement method, typically requires placing a test wafer (i.e., a wafer) with an integrated temperature sensor on the electrostatic chuck within the process cavity, and indirectly measuring the temperature of the electrostatic chuck surface through the temperature sensor on the wafer. In contrast, the non-contact infrared temperature measurement method proposed in this example, which directly measures through an infrared transmission window, does not rely on the test wafer as a heat transfer medium, avoiding the thermal hysteresis caused by heat transfer from the electrostatic chuck surface to the sensor inside the wafer. At the same time, it can directly receive the infrared radiation energy emitted by the surface of the electrostatic chuck through the infrared transmission window, realizing in-situ measurement of the surface temperature of the electrostatic chuck without intermediaries, which is faster than the traditional OnWafer indirect measurement method.

[0022] In one example, the electrostatic chuck can undergo a calibration process, i.e., calibration testing, before leaving the factory and during routine maintenance to determine accurate temperature calibration parameters. Specifically, firstly, the electrostatic chuck to be calibrated can be loaded onto a preset simulation platform. This simulation platform can precisely control the heater power and cooling medium temperature, and can simulate some of the environmental conditions of the process chamber. Then, at least one preset temperature variation process can be performed on the electrostatic chuck through this simulation platform. The preset temperature variation process can be used to systematically change the temperature state of the electrostatic chuck. For example, the temperature variation process can be a process of gradually increasing the temperature from room temperature to the maximum operating temperature, such as 150°C, and then gradually decreasing it, or it can be a steady-state holding process at multiple different temperature points.

[0023] When the simulation platform detects that the temperature of the electrostatic chuck has stabilized within any preset simulation temperature range—for example, through a sensor set on the simulation platform or the electrostatic chuck's own sensor—it can simultaneously trigger and record two sets of data. One set is the first measured value of the internal temperature of the electrostatic chuck at this moment, obtained by the fiber optic temperature sensor embedded inside the electrostatic chuck (denoted as...). ,in (This is a temperature point index); the other set is the second measured value of the temperature of the upper surface (i.e., the working surface) of the electrostatic chuck at this moment, obtained by a preset high-precision infrared temperature measuring device (denoted as...). Among them, the high-precision infrared temperature measurement device can be an infrared thermal imager or an infrared spot thermometer. The electrostatic chuck can contain multiple independently temperature-controlled heating zones, then the second measurement value... It can include surface temperature measurements of each individual heating zone of the electrostatic chuck, thereby establishing a complete surface temperature distribution map.

[0024] For example, such as Figure 2 The image shows the temperature or heat flux distribution on the surface of the electrostatic chuck under four different configurations: State 1, State 2, State 3, and State 4, caused by the action of multiple heating zones. The image as a whole presents a centrally symmetrical color intensity distribution pattern, resembling a bullseye or crater structure. The units for the X and Y axes are millimeters, ranging from -150 mm to 150 mm, covering the typical working surface scale of the electrostatic chuck. The color scale on the right side of the image indicates the correspondence between numerical values ​​and colors, transitioning from dark blue representing lower values, through a series of intermediate shades, to bright red and bright yellow representing higher values. Figure 2 Clearly, in all four states, the surface of the electrostatic chuck exhibits a concentric ring-shaped temperature distribution radiating outwards from the center, with the color gradually changing from the center outwards, forming multiple alternating bright and dark rings. This distribution visually demonstrates the non-uniform temperature or thermal effect field formed during the heating process of the electrostatic chuck due to the combined effects of factors such as the material's thermal conductivity, the layout of the multi-zone heaters, power distribution, and boundary cooling conditions. Figure 2 The heat map data shown provides a basis for subsequent establishment of accurate thermal effect models, calculation of temperature calibration parameters, and analysis of multi-zone thermal coupling relationships.

[0025] Then, based on multiple sets of data collected at all preset simulated temperature points ( , Using data pairs, a thermal effect model of the electrostatic chuck is established through mathematical fitting methods, such as multinomial regression and neural network fitting. This thermal effect model can be used to accurately characterize the internal temperature measurements of the electrostatic chuck. ) and its surface temperature measurement value ( The mapping relationship between ( ). For example, the mapping relationship can be represented as a linear relationship: ,in and These are model parameters; they can also be represented as more complex nonlinear relationships: .

[0026] After establishing the thermal effect model, the final temperature calibration parameters can be determined based on the established thermal effect model. These temperature calibration parameters are the coefficients in the model, such as those mentioned above. Meanwhile, to achieve dynamic compensation in real-time control, the calibration process can be repeated under various combinations of operating conditions. For example, at different heater power levels ( ) and different cooling medium temperatures ( Under this combination, the above-mentioned variable temperature process and data acquisition are performed. Thus, for each fixed ( , For each working condition, a set of model parameters can be fitted. , All these parameters constitute a multi-dimensional calibration parameter database. This database can include the pre-generated temperature calibration parameters mentioned above. This calibration parameter database can be used in real-time control based on the current real-time internal temperature measurements. Heater power and cooling water temperature By looking up tables or interpolating, a set of applicable parameters is dynamically selected or calculated, thereby enabling... Mapped to The predicted value. The calibration test can be completed through the above calibration process.

[0027] In one example, such as Figure 3 As shown, Figure 3 The left half of the diagram illustrates the specific steps of the pre-shipment calibration process: First, the electrostatic chuck ceramic can be loaded into a calibration test platform. This calibration test platform corresponds to the aforementioned preset simulation platform, which typically includes a vacuum chamber to simulate the process environment and integrates a high-precision infrared camera as an infrared temperature measurement device, as well as a sophisticated calibration platform controller. Then, the thermal performance of the electrostatic chuck can be tested according to the test process. Through the calibration platform controller, at least one preset temperature variation process is performed on the electrostatic chuck, such as stepped heating, cooling, or steady-state holding, to systematically stimulate the thermal response of the electrostatic chuck at different temperature states and record the electrostatic chuck thermal map and related sensor readings. When the temperature stabilizes at a certain preset temperature point, the calibration platform can simultaneously trigger the infrared camera to capture a thermal map of the temperature distribution across the entire working surface of the electrostatic chuck, similar to the above. Figure 2 The image shown is for State 1. Simultaneously, the internal temperature measurement value acquired by the fiber optic temperature sensor embedded within the electrostatic chuck can be recorded. This synchronous recording process is repeated at multiple temperature points and under various heater and cooling water operating conditions to collect a massive amount of data pairs. Then, based on all the collected first internal temperature measurement values ​​and second infrared surface temperature measurement values, a thermal effect model of the electrostatic chuck, characterizing the mapping relationship between the two, can be established through mathematical fitting. The established thermal effect model, along with the parameters for all operating conditions—i.e., temperature calibration parameters—is organized and stored as a digital calibration file. This digital calibration file can be stored in a pre-configured transmitter. The transmitter is pre-set and can be used to store and manage calibration data.

[0028] In one example, an electrostatic chuck may include multiple independently temperature-controlled heating zones, each equipped with its own heater. During calibration testing, the thermal coupling relationships between these heating zones can also be determined. Specifically, before acquiring real-time internal temperature measurements of the electrostatic chuck, the thermal coupling relationships between the heating zones can be determined based on temperature response data from various heating combinations. These thermal coupling relationships can be used to quantify the degree of thermal interference caused by a temperature change in one heating zone to at least one other heating zone. The various heating combinations can be determined based on the heating power of multiple heaters.

[0029] In one example, when determining the thermal coupling relationship, different heating combinations can be activated sequentially or according to a specific power sequence on a preset simulation platform. For example, while keeping the power of all other heating zones at 0 or a preset baseline value, the first heating combination can be activated individually. The power of each heating zone is increased to a specific value. Then wait for the temperature of all heating zones to stabilize. After the temperature of all heating zones has stabilized, all [temperatures] can be recorded. Each heating zone (including the first zone specifically heated) Districts and other The surface temperature (measured by an infrared measuring device) or internal temperature (measured by a separate fiber optic temperature sensor) of each zone is measured. This yields a set of data. This set of data reflects the... When each heating zone acts alone, it affects all The temperature impact caused by each heating zone can be repeated, recording the temperature impact on all other heating zones when each heating zone is heated in a specific way. By analyzing this data, a thermal coupling coefficient matrix can be constructed. Elements in the matrix ( Not equal to ) can quantitatively represent the first The unit power change of the heating zone, for the first The amount of interference caused by the temperature of each heating zone (the unit can be ℃ / W).

[0030] In one example, after obtaining the temperature calibration parameters, the electrostatic surface temperature measurement value can be obtained based on the temperature calibration parameters and the real-time internal temperature measurement value. Since the corresponding temperature calibration parameters differ under different operating conditions, in this example, the target temperature calibration parameter can first be queried from the pre-generated temperature calibration parameters based on the real-time internal temperature measurement value and the current operating condition information of the electrostatic chuck. Then, based on the target temperature calibration parameter, the real-time temperature difference compensation value between the real-time internal temperature measurement value and the heating zone on the surface of the electrostatic chuck is determined. The real-time internal temperature measurement value and the real-time temperature difference compensation value are then added to obtain the predicted surface temperature measurement value. The current operating condition information may include at least one of the current power of the heater and the current temperature of the cooling medium of the electrostatic chuck.

[0031] In one example, current operating condition information may include the heater's current power. Current temperature of cooling water And the current air pressure in the process chamber where the electrostatic chuck is located. At least one of these, and can maintain a multidimensional calibration parameter database. For example, the query process can be: using real-time internal temperature measurements... Heater power Cooling water temperature and current air pressure As a joint index, matching or interpolation is performed in this calibration parameter database to find a set of corresponding target temperature calibration parameters. This set of parameters can include at least one temperature difference compensation value. Then predict the surface temperature measurement value. The calculation formula can be:

[0032] In this way, the temperature difference between the interior and the surface that varies over time due to changes in heating power, different cooling conditions, different current air pressure, and internal heat conduction paths can be dynamically compensated. This allows the lagging internal measurements that do not directly characterize the temperature of the wafer contact surface to be converted into accurate predicted surface temperature measurements in real time.

[0033] In an embodiment of the present invention, after determining the predicted surface temperature measurement value, a temperature control command can be generated based on the difference between the predicted surface temperature measurement value and the preset target temperature value, and the temperature of the electrostatic chuck can be controlled based on the temperature control command. The temperature control command can be used to control the heater to heat the electrostatic chuck.

[0034] In one example, when generating temperature control commands, thermal coupling interference within a given area can be considered and compensated for. Specifically, when generating temperature control commands for any heating zone, a predicted thermal interference value corresponding to that heating zone can be calculated based on the thermal coupling relationship determined in calibration tests and the current or predicted surface temperature of at least one other heating zone besides that heating zone. Then, a temperature control command for that heating zone is generated based on this predicted thermal interference value. This temperature control command can be used to treat the predicted thermal interference value as a feedforward compensation amount, and thermal interference compensation can be performed based on this feedforward amount.

[0035] For example, we can assume that we currently need to control the first... Each heating zone reaches the target temperature. Then you can first read all other heating zones (such as heating zones). , The current predicted surface temperature Then, the thermal coupling coefficient matrix can be used. Calculate the effect of other heating zones on the first Total thermal disturbance predicted value of the area The calculation method can be:

[0036] in, This is a reference temperature, such as the cooling water temperature. Then, in generating the... When issuing control commands for the area, It is added to the control loop as a negative feedforward. For example, the input deviation of a controller, such as a PID controller. No longer simple Instead, it was revised to In this way, when calculating the output power, the controller can preemptively cancel out the expected thermal interference from other heating zones, thereby decoupling the temperature control of each zone and improving the temperature uniformity of multiple zones.

[0037] In one example, a more advanced multivariate model predictive control method can be used to determine the temperature control command. Specifically, based on the pre-generated thermal response matrix and the difference between the predicted surface temperature measurement and the preset target temperature value, a target heating combination that makes the predicted surface temperature of the electrostatic chuck closest to the preset target temperature value can be calculated over multiple future control cycles. The thermal response matrix can be generated based on the thermal coupling relationship between multiple heating zones of the electrostatic chuck and is used to describe the dynamic response characteristics over a future period. The heating combination can correspond to the heating power of multiple heaters and must meet the power output requirements of multiple heaters (e.g., meeting the upper and lower power limits of each heater). The control cycle can be a preset fixed time interval (e.g., 100 milliseconds). Then, the heating power value corresponding to the current control cycle can be extracted from the heating power sequence of the multiple heaters corresponding to the calculated target heating combination, and the temperature control command for the current moment can be determined and output based on the extracted heating power value.

[0038] In the example above, a specific process can be performed in each control cycle to obtain the heating combination corresponding to that control cycle. This specific process can be: first, obtaining the first temperature deviation between the current predicted surface temperature measurement and the preset target temperature value. The second temperature deviation is the predicted surface temperature measurement value at a historical time (such as the previous one or several cycles) and the preset target temperature value. And the current temperature state of the electrostatic chuck (which can be represented as a vector). (This can include the current predicted surface temperature or internal temperature of each heating zone). This can represent the current control cycle. Then, based on the acquired current and historical temperature deviations... And the aforementioned thermal response matrix, predicting in the future Within a control cycle, under different preset candidate heating combinations, the surface temperature changes of each heating zone of the electrostatic chuck are analyzed to obtain the predicted target surface temperature for multiple future control cycles. Then, an optimization problem can be established. The optimization objective of this problem is to minimize the future... Predicted target surface temperature within a period Compared with the preset target temperature value The deviation between them. Meanwhile, the optimization problem needs to satisfy constraints, mainly including the power output requirements of each heater, that is, the power output of each heater must not exceed the set maximum power value and not be less than the set minimum power value. Finally, this constrained optimization problem can be solved to obtain the future... The optimal heating power sequence within a given cycle is determined. From this optimal sequence, the heating combination corresponding to the current control cycle can be identified. Then, the next control cycle can begin, and the entire process is repeated: measuring the new temperature, obtaining the predicted target surface temperature for multiple future control cycles based on the new temperature, re-determining the target heating combination from all preset candidate heating combinations, and finally determining the heating combination corresponding to the current control cycle from the target heating combination. This rolling optimization and feedback correction method not only considers current errors but also anticipates future dynamics. Furthermore, it can calculate a globally better control action sequence while satisfying power constraints, thus significantly improving the performance of multivariable, strongly coupled, and constrained temperature control systems, achieving fast, accurate, and robust temperature control.

[0039] In one example, the loading and storing of the electrostatic chuck's calibration file and thermal response matrix, querying calibration parameters based on real-time internal temperature and operating condition information and calculating dynamic temperature difference compensation, outputting the predicted surface temperature measurement value, and generating temperature control commands based on this temperature value are all executed by a pre-configured transmitter. The transmitter is a dedicated high-precision temperature control device and serves as the core processing unit. During initialization, the transmitter can load and store a calibration file created for a specific electrostatic chuck. This calibration file may contain thermal effect model parameters and a multi-heating zone thermal coupling relationship matrix. The transmitter can read the measured values ​​from the fiber optic temperature sensor and operating condition information such as heating power in real time. Through its embedded dynamic calibration algorithm, it queries and interpolates the calibration parameter library to calculate the real-time temperature difference compensation value, thereby obtaining an accurate predicted surface temperature. Simultaneously, the transmitter can act as a core controller, running intelligent collaborative control algorithms such as model predictive control. This algorithm uses the calibrated predicted surface temperature as feedback and, combined with the stored thermal coupling model, actively predicts and cancels inter-zone thermal interference when calculating the control output of each zone, and solves for the optimal heating power combination that meets the constraints, outputting it to the heater.

[0040] In one example, such as Figure 3 As shown, Figure 3The right half of the diagram details the control flow described above. Specifically, firstly, the system can be initialized and a calibration file loaded. The calibration file, generated during the pre-shipment calibration phase and containing the thermal effect model and temperature calibration parameters, is loaded into the control system and the preset transmitter. Then, basic parameters can be set, including cooling water temperature and target heating temperature. After setting the basic parameters, heating can begin. Simultaneously, two cyclic processes can be executed in parallel. One process uses fluorescent fiber optic temperature acquisition to obtain temperature data and drive the heater; the other process involves the calibration model predicting the output and calibrating the fluorescent fiber optic temperature measurement. Specifically, the transmitter can acquire the corrected temperature data. This correction involves processing the original fluorescent fiber optic measurement using the loaded calibration model to obtain the predicted surface temperature value. Finally, the transmitter can predict the output power required to reach the target temperature based on the corrected virtual data and generate corresponding control commands to drive the heater.

[0041] In one example, such as Figure 4 As shown, the main modules of the above calibration and control processes can be illustrated. Among them, Figure 4 The left side of the system is the preset simulation platform for the electrostatic chuck, which also serves as the calibration test platform. It mainly includes a vacuum chamber, an infrared camera, the ceramic body of the electrostatic chuck, a temperature measuring structure, a heater, and a calibration platform controller. The vacuum chamber can simulate the actual process environment; the infrared camera can be used as a high-precision temperature measuring device aimed at the working surface of the ceramic body of the electrostatic chuck; the temperature measuring structure can include at least one fiber optic temperature sensor, which can be embedded inside the ceramic body to directly measure the internal temperature near each heating zone; the heater can be integrated inside the electrostatic chuck to provide a heat source; the calibration platform controller is responsible for coordinating the entire test process; it controls the heater to operate according to a preset temperature variation process and simultaneously triggers the infrared camera to collect surface temperature data and record the internal temperature data measured by the temperature measuring structure.

[0042] Figure 4 The right side of the diagram represents the temperature control system of the electrostatic chuck. This system primarily includes the ceramic body of the electrostatic chuck (the controlled object), its internal temperature sensing structure and heater, and the transmitter, the core component responsible for intelligent control. The transmitter integrates two key algorithm modules: an electrostatic chuck temperature prediction model and a heating drive control algorithm. The temperature prediction model uses the calibration parameter file generated and stored during the calibration process on the left to dynamically compensate and map the raw temperature measurements collected in real-time by the fiber optic temperature sensor in the temperature sensing structure, outputting an accurate predicted surface temperature value. The heating drive control algorithm receives this predicted surface temperature value, compares it with a preset target temperature, and then calculates the optimal heater power control command.

[0043] Based on the above, the embodiments of the present invention have the following advantages: In terms of accuracy, by using fiber optic temperature sensors to directly measure the temperature and combining the temperature calibration parameters generated through calibration tests before leaving the factory, the heat transfer error between the internal measurement points and the working surface is corrected in real time, thereby improving the wafer-level temperature control accuracy to within ±0.5℃; in terms of response speed, the use of millisecond-level response sensing technology reduces detection lag, and combined with predictive control algorithms for forward-looking optimization, resulting in a significant reduction in temperature stabilization time; in terms of uniformity, multi-zone intelligent collaborative control actively cancels the thermal coupling interference between zones, achieving high uniformity control of wafer surface temperature; in terms of reliability and adaptability, the non-contact temperature measurement method has strong anti-electromagnetic interference capability and long lifespan. Through the calibration process, it has automatic dynamic calibration capability, which can compensate for drift caused by equipment aging and component replacement, ensuring long-term process stability and repeatability; in terms of versatility, the specific physical characteristics of the equipment are abstracted into configurable model parameters (such as thermal effect model coefficients and thermal response matrices), enabling the solution to be flexibly applied to various models of electrostatic chucks and semiconductor process equipment by loading different calibration files.

[0044] In this embodiment of the invention, firstly, a fiber optic temperature sensor embedded inside the electrostatic chuck acquires real-time internal temperature measurements. Then, based on temperature calibration parameters generated from a pre-executed calibration test, these internal temperature measurements are mapped to predicted temperatures on the working surface of the electrostatic chuck. This calibration test involves controlling the electrostatic chuck to reach various preset temperatures on a dedicated simulation platform, simultaneously recording a first internal temperature measurement from the fiber optic temperature sensor and a second surface temperature measurement directly from an infrared thermometer, to obtain temperature calibration parameters based on these two measurements. Finally, a control command is generated based on the difference between the predicted surface temperature and the target temperature, driving a heater to precisely adjust the temperature of the electrostatic chuck.

[0045] This invention enables direct internal temperature measurement using a fast-response, electromagnetically interference-resistant fiber optic temperature sensor. This overcomes the measurement lag caused by the high thermal inertia of traditional contact sensors, as well as the susceptibility to interference and signal instability of non-contact infrared sensors in the process environment. Furthermore, through infrared-based calibration testing, internal measurements are automatically and in real-time compensated and mapped to accurate surface temperature predictions. This fundamentally solves the problem of difficult-to-compensate gradient errors between internal temperature and the working surface, and completely eliminates the reliance on inefficient, periodic manual calibration. This lays a reliable measurement foundation for achieving high-precision, high-response temperature control.

[0046] Figure 5 The temperature control device 500 for the electrostatic chuck shown can achieve... Figure 1The method described in the embodiment achieves the same technical effect, and can be specifically referred to in the above description. Figure 1 The temperature control method for the electrostatic chuck in the illustrated embodiment will not be described in detail here. The temperature control device 500 for the electrostatic chuck includes: The acquisition module 501 is used to acquire the real-time internal temperature measurement value of the electrostatic chuck through an optical fiber temperature sensor set inside the electrostatic chuck. Prediction module 502 is used to predict based on pre-generated temperature calibration parameters and the real-time internal temperature measurement value; The control module 503 is used to generate a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value, and to perform temperature control on the electrostatic chuck based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to determine and generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fluorescent fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

[0047] Optionally, the device further includes ( Figure 5 (not shown in the image) The loading module 504 is used to load the electrostatic chuck onto the preset simulation platform before acquiring the real-time internal temperature measurement value of the electrostatic chuck; The temperature-changing module 503 is used to perform at least one preset temperature-changing process on the electrostatic chuck through the preset simulation platform; the preset temperature-changing process is used to change the temperature of the electrostatic chuck. The recording module 506 is used to simultaneously record the first measured value of the internal temperature of the electrostatic chuck obtained by the fiber optic temperature sensor and the second measured value of the surface temperature obtained by the infrared temperature measuring device when the temperature of the electrostatic chuck is detected to be stable within any preset simulated temperature range; the second measured value includes the surface temperature measured value of at least one heating zone of the electrostatic chuck. The test module 507 is used to establish a thermal effect model of the electrostatic chuck based on the first measurement value and the second measurement value, so as to complete the calibration test; the thermal effect model is used to characterize the mapping relationship between the internal temperature measurement value and the surface temperature measurement value of the electrostatic chuck. The first determining module 508 is used to determine the temperature calibration parameters based on the thermal effect model; the temperature calibration parameters are used to map the real-time internal temperature measurement value of the electrostatic chuck to the predicted value of the surface temperature measurement value of the electrostatic chuck.

[0048] Optionally, the prediction module 502 is used for: Based on the real-time internal temperature measurement value and the current operating condition information of the electrostatic chuck, the target temperature calibration parameter is queried from the pre-generated temperature calibration parameters; the current operating condition information includes at least one of the current power of the heater, the current temperature of the cooling medium of the electrostatic chuck, and the current air pressure of the process chamber in which the electrostatic chuck is located. Based on the target temperature calibration parameters, determine the real-time temperature difference compensation value between the real-time internal temperature measurement value and the heating zone on the surface of the electrostatic chuck; The predicted surface temperature measurement value is obtained by adding the real-time internal temperature measurement value to the real-time temperature difference compensation value.

[0049] Optionally, the electrostatic chuck includes multiple independently controlled heating zones; each heating zone is equipped with an independent heater; the device further includes ( Figure 5 (not shown in the image) The second determining module 509 is used to determine the thermal coupling relationship between multiple heating zones in the calibration test before acquiring the real-time internal temperature measurement value of the electrostatic chuck; the thermal coupling relationship is used to quantify the degree of thermal interference caused by the temperature change of one heating zone to at least one other heating zone; the multiple heating combinations are determined based on the heating power of the multiple heaters; The control module 503 is used for: When determining a temperature control command for any of the heating zones, a predicted thermal interference value corresponding to any of the heating zones is calculated based on the thermal coupling relationship and the current temperature of at least one other heating zone besides any of the heating zones. Based on the predicted thermal interference value, a temperature control command is determined for any of the heating zones; the temperature control command is used to take the predicted thermal interference value as a feedforward quantity and perform thermal interference compensation based on the feedforward quantity.

[0050] Optionally, the control module 503 is used for: Based on a pre-generated thermal response matrix and the difference between the predicted surface temperature measurement and the preset target temperature value, a target heating combination is calculated over multiple future control cycles to make the predicted surface temperature of the electrostatic chuck closest to the preset target temperature value. The thermal response matrix is ​​generated based on the thermal coupling relationship between the multiple heating zones of the electrostatic chuck. The heating power of the multiple heaters corresponding to the heating combination meets the power output requirements of the heaters. The control cycle is a preset fixed time interval. From the heating power of the multiple heaters corresponding to the target heating combination, the heating power value corresponding to the current control cycle is extracted, and the temperature control command is determined based on the extracted heating power value.

[0051] Optionally, the control module 503 is used for: For any of the control cycles, a specific process is performed to obtain the heating combination corresponding to the control cycle; The specific processing is as follows: The system acquires a first temperature deviation between the current predicted surface temperature measurement value and the preset target temperature value, a second temperature deviation between the predicted surface temperature measurement value and the preset target temperature value at a historical time, and the current temperature of the electrostatic chuck. Based on the acquired first temperature deviation, second temperature deviation, current temperature, and thermal response matrix, the surface temperature change of the electrostatic chuck under different preset candidate heating combinations is predicted in multiple future control cycles, and the target surface temperature prediction value corresponding to the multiple future control cycles is obtained. The optimization objective is to minimize the deviation between the predicted target surface temperature value corresponding to the multiple future control cycles and the preset target temperature value. Under the constraint of meeting the power output requirements of each heater, the target heating combination is determined from all preset candidate heating combinations. From the target heating combinations, determine the heating combination corresponding to the current control cycle.

[0052] In this embodiment of the invention, firstly, a fiber optic temperature sensor embedded inside the electrostatic chuck acquires real-time internal temperature measurements. Then, based on temperature calibration parameters generated from a pre-executed calibration test, these internal temperature measurements are mapped to predicted temperatures on the working surface of the electrostatic chuck. This calibration test involves controlling the electrostatic chuck to reach various preset temperatures on a dedicated simulation platform, simultaneously recording a first internal temperature measurement from the fiber optic temperature sensor and a second surface temperature measurement directly from an infrared thermometer, to obtain temperature calibration parameters based on these two measurements. Finally, a control command is generated based on the difference between the predicted surface temperature and the target temperature, driving a heater to precisely adjust the temperature of the electrostatic chuck.

[0053] This invention enables direct internal temperature measurement using a fast-response, electromagnetically interference-resistant fiber optic temperature sensor. This overcomes the measurement lag caused by the high thermal inertia of traditional contact sensors, as well as the susceptibility to interference and signal instability of non-contact infrared sensors in the process environment. Furthermore, through infrared-based calibration testing, internal measurements are automatically and in real-time compensated and mapped to accurate surface temperature predictions. This fundamentally solves the problem of difficult-to-compensate gradient errors between internal temperature and the working surface, and completely eliminates the reliance on inefficient, periodic manual calibration. This lays a reliable measurement foundation for achieving high-precision, high-response temperature control.

[0054] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Please refer to it. Figure 6 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0055] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0056] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0057] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a non-contiguous transfer configuration at the logical level. The processor executes the program stored in memory and specifically performs the following operations: The real-time internal temperature measurement value of the electrostatic chuck is obtained by using an optical fiber temperature sensor installed inside the electrostatic chuck. Based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value; Based on the difference between the predicted surface temperature measurement value and the preset target temperature value, a temperature control command is generated, and the temperature of the electrostatic chuck is controlled based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to determine and generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fluorescent fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

[0058] The above is as stated in this application. Figure 1The temperature control method for the electrostatic chuck disclosed in the embodiments described above can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in one or more embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in one or more embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0059] The electronic device can also perform Figure 1 The temperature control method for the electrostatic chuck is not described in detail here.

[0060] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple applications, enable the portable electronic device to perform... Figure 1 The methods of the embodiments shown are not described in detail here.

[0061] This application also proposes a computer program product, which is stored in a storage medium and executed by at least one processor to implement... Figure 1 The methods of the embodiments shown are not described in detail here.

[0062] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0063] In summary, the above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this application should be included within the scope of protection of one or more embodiments of this application.

[0064] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0065] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined in the embodiments of this application, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0066] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0067] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

Claims

1. A temperature control method of an electrostatic chuck, characterized by, The method includes: The real-time internal temperature measurement value of the electrostatic chuck is obtained by using an optical fiber temperature sensor installed inside the electrostatic chuck. Based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value, the predicted surface temperature measurement value of the electrostatic chuck is obtained; Based on the difference between the predicted surface temperature measurement value and the preset target temperature value, a temperature control command is generated, and the temperature of the electrostatic chuck is controlled based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

2. The method of claim 1, wherein, Before acquiring the real-time internal temperature measurement value of the electrostatic chuck, the method further includes: The electrostatic chuck is loaded onto the preset simulation platform; At least one preset temperature change process is performed on the electrostatic chuck through the preset simulation platform; the preset temperature change process is used to change the temperature of the electrostatic chuck. When the temperature of the electrostatic chuck is detected to be stable within any preset simulated temperature range, the first measured value of the internal temperature of the electrostatic chuck obtained by the fiber optic temperature sensor and the second measured value of the surface temperature obtained by the infrared temperature measuring device are recorded simultaneously; the second measured value includes the surface temperature measured value of at least one heating zone of the electrostatic chuck. Based on the first and second measured values, a thermal effect model of the electrostatic chuck is established to complete the calibration test; the thermal effect model is used to characterize the mapping relationship between the internal temperature measurement value and the surface temperature measurement value of the electrostatic chuck. Based on the thermal effect model, the temperature calibration parameters are determined; the temperature calibration parameters are used to map the real-time internal temperature measurement value of the electrostatic chuck to the predicted value of the surface temperature measurement value of the electrostatic chuck.

3. The method of claim 1, wherein, The step of obtaining the predicted surface temperature measurement value of the electrostatic chuck based on the pre-generated temperature calibration parameters and the real-time internal temperature measurement value includes: Based on the real-time internal temperature measurement value and the current operating condition information of the electrostatic chuck, the target temperature calibration parameter is queried from the pre-generated temperature calibration parameters; the current operating condition information includes at least one of the current power of the heater, the current temperature of the cooling medium of the electrostatic chuck, and the current air pressure of the process chamber where the electrostatic chuck is located. Based on the target temperature calibration parameters, determine the real-time temperature difference compensation value between the real-time internal temperature measurement value and the heating zone on the surface of the electrostatic chuck; The predicted surface temperature measurement value is obtained by adding the real-time internal temperature measurement value to the real-time temperature difference compensation value.

4. The method of claim 1, wherein, The electrostatic chuck includes multiple independently controlled heating zones; each heating zone is equipped with an independent heater. Before acquiring the real-time internal temperature measurement value of the electrostatic chuck, the method further includes: In the calibration test, the thermal coupling relationship between the multiple heating zones is determined based on the temperature response data of the multiple heating zones under various heating combinations; the thermal coupling relationship is used to quantify the degree of thermal interference caused by the temperature change of one heating zone to at least one other heating zone; the multiple heating combinations are determined based on the heating power of the multiple heaters; The step of generating a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value includes: When determining a temperature control command for any of the heating zones, a predicted thermal interference value corresponding to any of the heating zones is calculated based on the thermal coupling relationship and the current temperature of at least one other heating zone besides any of the heating zones. Based on the predicted thermal interference value, a temperature control command is determined for any of the heating zones; the temperature control command is used to take the predicted thermal interference value as a feedforward quantity and perform thermal interference compensation based on the feedforward quantity.

5. The method of claim 4, wherein, The step of generating a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value includes: Based on a pre-generated thermal response matrix and the difference between the predicted surface temperature measurement and the preset target temperature value, a target heating combination is calculated over multiple future control cycles to make the predicted surface temperature of the electrostatic chuck closest to the preset target temperature value. The thermal response matrix is ​​generated based on the thermal coupling relationship between the multiple heating zones of the electrostatic chuck. The heating power of the multiple heaters corresponding to the heating combination meets the power output requirements of the heaters. The control cycle is a preset fixed time interval. From the heating power of the multiple heaters corresponding to the target heating combination, the heating power value corresponding to the current control cycle is extracted, and the temperature control command is determined based on the extracted heating power value.

6. The method of claim 5, wherein, The rolling calculation of the target heating combination that makes the predicted surface temperature of the electrostatic chuck closest to the preset target temperature value over multiple future control cycles includes: For any of the control cycles, a specific process is performed to obtain the heating combination corresponding to the control cycle; The specific processing is as follows: The system acquires a first temperature deviation between the current predicted surface temperature measurement value and the preset target temperature value, a second temperature deviation between the predicted surface temperature measurement value and the preset target temperature value at a historical time, and the current temperature of the electrostatic chuck. Based on the acquired first temperature deviation, second temperature deviation, current temperature, and thermal response matrix, the surface temperature change of the electrostatic chuck under different preset candidate heating combinations is predicted in multiple future control cycles, and the target surface temperature prediction value corresponding to the multiple future control cycles is obtained. The optimization objective is to minimize the deviation between the predicted target surface temperature value and the preset target temperature value corresponding to the multiple future control cycles. Under the constraint of meeting the power output requirements of each heater, the target heating combination is determined from all preset candidate heating combinations. From the target heating combinations, determine the heating combination corresponding to the current control cycle.

7. A temperature control apparatus for an electrostatic chuck, characterized by comprising: The device includes: The acquisition module is used to acquire the real-time internal temperature measurement value of the electrostatic chuck through an optical fiber temperature sensor set inside the electrostatic chuck. The prediction module is used to obtain the predicted surface temperature measurement value of the electrostatic chuck based on pre-generated temperature calibration parameters and the real-time internal temperature measurement value; fiber optic temperature sensor. The control module is used to generate a temperature control command based on the difference between the predicted surface temperature measurement value and the preset target temperature value, and to perform temperature control on the electrostatic chuck based on the temperature control command. The temperature control command is used to control the heater to heat the electrostatic chuck; the temperature calibration parameter is generated based on calibration tests and is used to map the internal temperature measurement value of the electrostatic chuck to the surface temperature measurement value of the electrostatic chuck; the calibration test uses a preset simulation platform to control the electrostatic chuck to reach at least one preset simulated temperature, and simultaneously acquires a first internal temperature measurement value and a second surface temperature measurement value of the electrostatic chuck at the preset simulated temperature, so as to generate the temperature calibration parameter based on the first measurement value and the second measurement value; the first measurement value is acquired based on the fiber optic temperature sensor; the second measurement value is acquired based on a preset infrared temperature measurement device.

8. An electronic device, comprising: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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