Temperature measurement method, system, device, computer device and storage medium

Abstract

The application provides a temperature measurement method, system, device, computer device and storage medium. The application corrects a target spectrum signal by using an attenuation coefficient calculated from a reference spectrum signal and a theoretical blackbody radiation value to obtain a corrected radiation amount. A link equation is alternately iteratively optimized based on the corrected radiation amount to obtain temperature information, which includes a target temperature and a target emissivity model parameter. The application can compensate for link response attenuation in real time, maintain long-term stability and consistency of the temperature measurement link without configuring a high-temperature blackbody source. In addition, the link equation of the application realizes joint solving of the emissivity model parameter and the wafer surface temperature, reduces the influence of emissivity uncertainty on the temperature inversion result, thereby realizing high-precision and wide-range measurement of the wafer temperature in a complex process environment, improving the precision of temperature closed-loop control and the process yield, and facilitating engineering application in a compact temperature measurement probe with limited volume.

Description

Technical Field

[0001] This application relates to the field of semiconductor testing technology, and in particular to a temperature measurement method, system, temperature measurement device, computer equipment, and storage medium. Background Technology

[0002] In high-end industrial manufacturing, especially in semiconductor chip manufacturing processes such as thin film deposition and rapid thermal processing (RTP), accurate measurement and closed-loop control of wafer temperature are key factors determining the electrical performance, consistency, and yield of devices. Due to the extreme environment inside the process chamber, wafers are typically in high-temperature, high-vacuum, or highly reactive atmospheres. Traditional contact temperature measurement methods are difficult to apply directly to the wafer surface and can introduce particulate contamination and process interference. Therefore, non-contact radiation thermometry has gradually become the mainstream temperature measurement solution in such equipment.

[0003] Traditional radiation temperature measurement systems typically collect thermal radiation signals from the wafer surface via optical windows, optical fibers, or probes. These signals are then combined with a pre-defined emissivity model and optical link calibration coefficients to calculate the equivalent temperature of the wafer surface. However, during long-term operation, the cavity window and optical components are susceptible to effects such as process byproduct deposition and particulate contamination, leading to decreased transmittance or spectral distortion. Simultaneously, factors such as detector sensitivity drift over time and changes in amplifier gain can introduce slow shifts in link gain and response curves. Without effective compensation and recalibration, these errors will directly accumulate, impacting process repeatability and product yield.

[0004] To mitigate the impact of system drift, some high-precision radiation thermometry devices integrate a high-temperature blackbody reference source. The system response is calibrated by periodically switching the optical path and measuring the blackbody's radiation signal. However, the high-temperature blackbody itself requires significant power for heating, placing stringent requirements on heat dissipation and insulation structures, which can easily cause thermal interference to the probe and adjacent devices. Summary of the Invention

[0005] The purpose of this application is to provide a temperature measurement method, system, temperature measurement device, computer device and storage medium to overcome the defects of traditional technology that uses a high-temperature blackbody as a reference source, and improper heat dissipation and insulation settings can easily cause thermal interference to the probe body and adjacent devices.

[0006] In a first aspect, this application provides a temperature measurement method, comprising: an application to a temperature measuring device, wherein the temperature measuring device is partially disposed within a wafer reaction cavity, the temperature measuring device including a reference light source and a target light source; the method comprising: A reference spectral signal is acquired, and an attenuation coefficient is calculated based on the reference spectral signal and the theoretical blackbody radiation value; wherein the reference spectral signal is generated by the reference light source irradiating the wafer surface, and the theoretical blackbody radiation value is obtained based on a standard blackbody reference source; The target spectral signal is acquired, and the target spectral signal is corrected according to the attenuation coefficient to obtain the corrected radiance; wherein the target spectral signal is generated by the target light source irradiating the wafer surface; Based on the corrected radiance, the link equation is iteratively optimized until a preset iteration condition is met to obtain temperature information; wherein, the link equation is used to characterize the correspondence between the radiance and the pre-constructed emissivity model and blackbody radiance function; the temperature information includes the target temperature and its corresponding target emissivity model parameters.

[0007] In one embodiment, calculating the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value includes: The attenuation coefficient is obtained by calculating the ratio of the reference spectral signal to the theoretical blackbody radiation value. In one embodiment, correcting the target spectral signal based on the attenuation coefficient to obtain the corrected radiance includes: The attenuation coefficient and the first product of the system constant are calculated, and the ratio of the target spectral signal to the first product is calculated to obtain the corrected radiation amount; wherein the system constant is related to the optical geometry parameters and magnification of the measurement optical path.

[0008] In one embodiment, the step of performing alternating iterative optimization of the link equation based on the corrected radiation amount until a preset iteration condition is met to obtain temperature information includes: Set a temperature estimate, fix the temperature estimate, and iteratively optimize the emissivity model parameters in the link equation based on the temperature estimate and the corrected radiation amount until the first preset condition is met, and obtain the optimal emissivity model parameters. The optimal emissivity model parameters are fixed, and the temperature data in the link equation is iteratively optimized based on the optimal emissivity model parameters until the second preset condition is met, thereby obtaining the optimal temperature data. The optimal temperature data is taken as the so-called target temperature, and the optimal emissivity model parameters are taken as the target emissivity model parameters.

[0009] In one embodiment, the emissivity model parameters include a first parameter and a second parameter; The step of iteratively optimizing the emissivity model parameters in the link equation based on the temperature estimate until a first preset condition is met to obtain the optimal emissivity model parameters includes: For each wavelength channel, the corresponding radiation quantity in the link equation is expressed as a linear combination of the blackbody radiance term and the wavelength-normalized blackbody radiance term, so as to construct an overdetermined linear equation system about the first parameter and the second parameter. Based on the overdetermined linear equations, the least squares algorithm is used to solve for the first parameter and the second parameter to obtain the updated emissivity model parameters under the current temperature estimate. Substitute the updated emissivity model parameters into the link equation to calculate the error between the predicted radiation and the corrected radiation. If the error is less than a first preset threshold, the updated emissivity model parameters are used as the optimal emissivity model parameters. Otherwise, the updated emissivity model parameters are used to reconstruct the overdetermined linear equations and repeat the least squares solution process until the error is less than the first preset threshold. In one embodiment, the step of iteratively optimizing the temperature data in the link equation based on the optimal emissivity model parameters until a second preset condition is met to obtain the optimal temperature data includes: For each wavelength channel, based on the optimal emissivity model parameters and the blackbody radiance function, an objective function is constructed to characterize the sum of squared residuals between the model-predicted radiation and the measured radiation, with temperature data as the independent variable in the objective function. Using the temperature estimate as initial temperature data, the objective function is numerically solved based on a univariate nonlinear optimization algorithm to obtain updated temperature data. Calculate the absolute value of the difference between the temperature data before and after the update. If the absolute value is less than the second preset threshold, the updated temperature data is determined to be the optimal temperature data. Otherwise, the updated temperature data is used as the new temperature estimate, and the numerical solution process is repeated until the absolute value of the difference between the temperature data before and after the update is less than the second preset threshold.

[0010] Secondly, this application provides a temperature measurement system disposed within a temperature measurement device, wherein the temperature measurement device is partially disposed within a wafer reaction cavity, and the temperature measurement device includes a reference light source and a target light source; the system includes: An acquisition module is used to acquire a reference spectral signal and a target spectral signal, wherein the reference spectral signal is generated by the reference light source irradiating the wafer surface, and the target spectral signal is generated by the target light source irradiating the wafer surface; The first processing module is used to calculate the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value, wherein the theoretical blackbody radiation value is obtained based on a standard blackbody reference source; it is also used to correct the target spectral signal based on the attenuation coefficient to obtain the corrected radiation amount; The second processing module is used to perform alternating iterative optimization of the link equation based on the corrected radiance until a preset iteration condition is met to obtain temperature information; wherein, the link equation is used to characterize the correspondence between the predicted spectral signal and the pre-constructed emissivity model and blackbody radiance function; the temperature information includes the target temperature and its corresponding target emissivity model parameters.

[0011] Thirdly, this application also provides a temperature measuring device, partially disposed within a wafer reaction cavity; the temperature measuring device includes: A reference light source is used to output a reference light signal to the wafer surface during wafer fabrication. Target light source, used to output target light signals to the wafer surface during wafer fabrication; A baffle mechanism is used to switch between the measurement position and the calibration position; A light guide is used to receive and transmit the reference light signal when the baffle mechanism is in the calibration position; and to receive and transmit the target light signal when the baffle mechanism is in the measurement position. A detector is used to convert a reflected reference light signal into a reference spectral signal, or a reflected target light signal into a target spectral signal; wherein the reflected reference light signal is generated by the reflection of the reference light signal from the wafer surface, and the reflected target light signal is generated by the reflection of the target light signal from the wafer surface; The temperature measurement system shown in the second aspect is used to perform alternating iterative optimization of the link equation based on the reference spectral signal and the target spectral signal until a preset iteration condition is met, thereby obtaining temperature information, which includes the target temperature and its corresponding target emissivity model parameters.

[0012] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method steps of the first aspect.

[0013] Fifthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the method steps of the first aspect.

[0014] The above-mentioned temperature measurement methods, systems, equipment, computer devices, and storage media have at least the following advantages: This application uses an attenuation coefficient calculated from a reference spectral signal and theoretical blackbody radiation value to correct the target spectral signal, obtaining the corrected radiance. Based on the corrected radiance, the link equation, which simultaneously includes a pre-built emissivity model and a blackbody radiance function, is iteratively optimized to obtain temperature information, including the target temperature and its corresponding target emissivity model parameters. Using this approach, this application can compensate in real time for link response attenuation caused by optical window contamination, changes in optical element transmittance, and detector gain drift, maintaining long-term stability and consistency of the temperature measurement link without the need for a high-temperature blackbody source. Furthermore, the link equation in this application achieves joint solution of emissivity model parameters and wafer surface temperature, reducing the impact of emissivity uncertainty on temperature inversion results. This enables high-precision, wide-range measurement of wafer temperature in complex process environments, improving the accuracy of temperature closed-loop control and process yield, and facilitating engineering applications in compact temperature probes with limited space. Attached Figure Description

[0015] Figure 1 This is a structural block diagram of a temperature measuring device in one embodiment; Figure 2 This is a flowchart illustrating a temperature measurement method in one embodiment; Figure 3 This is a flowchart illustrating the steps for obtaining temperature information in one embodiment; Figure 4 This is a flowchart illustrating the steps for obtaining the optimal emissivity model parameters in one embodiment. Figure 5 This is a schematic diagram of the process for obtaining optimal temperature data in one embodiment; Figure 6 This is a structural block diagram of a temperature measurement system in one embodiment; Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0016] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0017] Some exemplary embodiments of this application have been described for illustrative purposes. It should be understood that this application may be implemented in other ways not specifically shown in the accompanying drawings.

[0018] Please see Figure 1 In one exemplary embodiment, this application provides a temperature measuring device, which is partially disposed within a wafer reaction chamber. The reaction chamber includes a base for placing the wafer, the base being made of high-purity graphite and supported by a rotating shaft driven by a motor, allowing it to rotate continuously during wafer processing.

[0019] Specifically, the temperature measuring equipment includes a reference light source, a target light source, a baffle mechanism, a light guide, a detector, and a temperature measuring system.

[0020] A reference light source is used to output a reference light signal to the wafer surface during wafer fabrication. It should be noted that this embodiment uses a broadband reference light source instead of a traditional blackbody light source. For example, a miniature halogen tungsten lamp or LED array with an emission spectrum covering all temperature measurement bands and highly stable output light intensity is selected. This reference light source is driven by a high-precision constant current source to ensure its luminous stability. While it does not possess blackbody spectral characteristics, its stability and band coverage are crucial.

[0021] The target light source is used to output a target light signal to the wafer surface during wafer processing.

[0022] A baffle mechanism is used to switch between the measurement position and the calibration position. Exemplarily, the baffle mechanism in this embodiment is driven by a micro-electro-mechanical system (MEMS) or piezoelectric ceramic. Specifically, in this embodiment, when the baffle mechanism is in the measurement position, the target optical signal is introduced into the measurement optical path; when the baffle mechanism is in the calibration position, the reference optical signal is introduced into the measurement optical path.

[0023] A light guide is used to receive and transmit a reference light signal when the baffle mechanism is in the calibration position; and to receive and transmit a target light signal when the baffle mechanism is in the measurement position. Exemplarily, the light guide portion is disposed within the reaction chamber, with one end facing the wafer surface and the other end connected to the baffle mechanism.

[0024] The detector is used to convert the reflected reference light signal into a reference spectral signal, or to convert the reflected target light signal into a target spectral signal; wherein the reflected reference light signal is generated by the reflected reference light signal from the wafer surface, and the reflected target light signal is generated by the reflected target light signal from the wafer surface. It should be understood that the above-mentioned temperature measuring device also includes a spectrometer for separating the received broadband infrared radiation into multiple different specific bands. Exemplarily, the spectrometer can be a spectrometer grating or a filter wheel. In this case, the detector will synchronously or rapidly switch to measure the radiation signal intensity in each band.

[0025] The aforementioned reference light source, target light source, baffle mechanism, light guide, and detector constitute a measurement optical path.

[0026] The temperature measurement system is used to iteratively optimize the link equation based on a reference spectral signal and a target spectral signal until a preset iteration condition is met, thereby obtaining temperature information. This temperature information includes the target temperature and its corresponding target emissivity model parameters. Specifically, the system acquires the reference spectral signal, calculates the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value, corrects the acquired target spectral signal based on the attenuation coefficient to obtain corrected radiance, and iteratively optimizes the link equation based on the corrected radiance until a preset iteration condition is met, thus obtaining temperature information. The link equation characterizes the correspondence between the radiance and the pre-constructed emissivity model and blackbody radiance function.

[0027] The aforementioned temperature measurement device employs a baffle mechanism to introduce a reference light source or a target light source into the light guide tube, enabling the detector to convert the reflected reference light signal into a reference spectral signal, or the reflected target light signal into a target spectral signal. Further, the target spectral signal is corrected using an attenuation coefficient calculated from the reference spectral signal and the theoretical blackbody radiation value, yielding a corrected radiance. Based on the corrected radiance, the link equation, which simultaneously includes a pre-built emissivity model and a blackbody radiance function, is iteratively optimized to obtain temperature information, including the target temperature and its corresponding target emissivity model parameters. By adopting the above scheme, this application can compensate for the link response attenuation caused by optical window contamination, changes in optical element transmittance, and detector gain drift in real time. It can maintain the long-term stability and consistency of the temperature measurement link without the need to configure a high-temperature blackbody source. In addition, the link equation of this application realizes the joint solution of emissivity model parameters and wafer surface temperature, reducing the impact of emissivity uncertainty on temperature inversion results. This enables high-precision, wide-range measurement of wafer temperature in complex process environments, improves the accuracy of temperature closed-loop control and process yield, and facilitates engineering applications in compact temperature measurement probes with limited volume.

[0028] Please see Figure 2 In one exemplary embodiment, this application provides a temperature measurement method applicable to a temperature measuring device. The temperature measuring device is partially disposed within a wafer reaction cavity and includes a reference light source and a target light source. The temperature measurement method includes: Step 202: Obtain the reference spectral signal and calculate the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value; wherein, the reference spectral signal is generated by the reference light source irradiating the wafer surface, and the theoretical blackbody radiation value is obtained based on the standard blackbody reference source.

[0029] Specifically, a standard blackbody reference source refers to a precisely designed and calibrated laboratory instrument that can produce known, stable spectral radiation that closely approximates theoretical blackbody radiation at a given temperature. The theoretical blackbody radiation value is the power radiated by the blackbody reference source per unit area, unit solid angle, and unit wavelength interval at a specific temperature T, calculated according to the physical laws of blackbody radiation (Planck's law). This theoretical blackbody radiation value does not depend on any physical measurement and is entirely determined by physical constants, temperature T, and wavelength λ.

[0030] The attenuation factor is used to characterize the degree of optical signal loss throughout the measurement optical path from the light source to the detector. Generally speaking, loss factors include at least one of transmission loss, reflection loss, geometric collection loss, optical guide loss, and optical element contamination or aging.

[0031] During the temperature measurement process, the system first enters the online self-calibration stage, which involves controlling the baffle mechanism to switch to the calibration position, measuring the reference spectral signal, and calculating the attenuation coefficient of the system at the current moment based on the reference spectral signal and the theoretical blackbody radiation value, which serves as the basis for subsequent high-precision measurements.

[0032] Step 204: Obtain the target spectral signal and correct the target spectral signal according to the attenuation coefficient to obtain the corrected radiation amount; wherein, the target spectral signal is generated by the target light source irradiating the wafer surface.

[0033] Specifically, the target spectral signal is generated by the detector based on the reflected target light signal, which is the result of the actual target's thermal radiation after being attenuated by its surface emissivity and the optical system. It should be understood that the target spectral signal is obtained by subtracting the dark reference signal from the original target spectral signal. The dark reference signal refers to the electrical signal generated by the detector and its associated circuitry under the same integration time and operating temperature after all light sources are turned off or blocked. This electrical signal includes background noise introduced by all non-target light sources, such as dark current, thermal noise, and readout noise.

[0034] The corrected radiance is the true radiance value of the wafer under specified conditions, obtained by removing all distortions and attenuation effects from the measurement optical path from the target spectral signal. Its value is objective and traceable; it faithfully reflects the proportion of the target's radiance intensity at various wavelengths, without introducing distortions due to differences in the response of optical components, detectors, etc. at different wavelengths.

[0035] Step 206: Based on the corrected radiance, the link equation is iteratively optimized until the preset iteration conditions are met to obtain temperature information; wherein, the link equation is used to characterize the correspondence between the radiance and the pre-constructed emissivity model and blackbody radiance function; the temperature information includes the target temperature and its corresponding target emissivity model parameters.

[0036] Specifically, the link equation is a mathematical model used to describe the relationship between an observable physical quantity and an unknown physical quantity to be solved. In this embodiment, the observable physical quantity is the radiation measured from the wafer surface, and the unknown physical quantities are temperature and emissivity. In a set of radiation signals, temperature and emissivity are strongly coupled and cannot be uniquely separated. Therefore, this embodiment uses an emissivity model to characterize the emissivity behavior of the entire spectrum, expressed as: ε(λ) = a + b / λ; where a and b are the emissivity model parameters to be optimized, and λ is the wavelength. The blackbody radiance function L_bb(λ_i, T) is a known function of temperature T and wavelength λ_i, characterizing the radiation intensity of an ideal blackbody at that temperature T and wavelength λ_i.

[0037] Furthermore, for the i-th wavelength channel, the link equation formed by modeling the voltage signal V_i corresponding to the target spectral signal output by the detector can be expressed as: V_i=ξ(λ_i)×ε(λ_i)×C×L_bb(λ_i, T); where ξ(λ_i) represents the attenuation coefficient; ε(λ_i) represents the spectral emissivity at wavelength λ_i, which is an unknown parameter dependent on the target and surface state; C represents the system constant related to the optical geometry and amplification of the measurement optical path, which is usually determined through a single overall calibration and can be normalized or canceled in subsequent calculations.

[0038] The aforementioned temperature measurement method corrects the target spectral signal using an attenuation coefficient calculated from the reference spectral signal and the theoretical blackbody radiation value, yielding a corrected radiance. Based on this corrected radiance, the link equation, which simultaneously incorporates a pre-built emissivity model and a blackbody radiance function, is iteratively optimized to obtain temperature information, including the target temperature and its corresponding target emissivity model parameters. Using this approach, this application can compensate in real-time for link response attenuation caused by optical window contamination, changes in optical element transmittance, and detector gain drift, maintaining long-term stability and consistency of the temperature measurement link without requiring a high-temperature blackbody source. Furthermore, the link equation in this application achieves joint solving of emissivity model parameters and wafer surface temperature, reducing the impact of emissivity uncertainty on temperature inversion results. This enables high-precision, wide-range measurement of wafer temperature in complex process environments, improving the accuracy of temperature closed-loop control and process yield, and facilitating engineering applications in compact temperature probes with limited space.

[0039] Optionally, the attenuation coefficient is calculated based on the reference spectral signal and the theoretical blackbody radiation value, including: The attenuation coefficient is obtained by calculating the ratio of the reference spectral signal to the theoretical blackbody radiation value. Specifically, the expression for calculating the attenuation coefficient is: ξ(λ) = S_meas(λ) / L_ref(λ); where S_meas(λ) represents the reference spectral signal and L_ref(λ) represents the theoretical blackbody radiation value.

[0040] Optionally, the target spectral signal is corrected based on the attenuation coefficient to obtain the corrected radiance, including: The corrected radiance is obtained by calculating the first product of the attenuation coefficient and the system constant, and then calculating the ratio of the target spectral signal to the first product. The system constant is related to the optical geometric parameters and magnification of the measurement optical path. The optical geometric parameters characterize the spatial light-gathering capability of the measurement optical path for the radiated energy of the target. For example, these parameters include the effective aperture size of the probe end face or optical window, the working distance to the surface of the measured wafer, the field of view and / or the corresponding solid angle, and the effective cross-sectional area of ​​the optical guide or fiber. The optical magnification characterizes the imaging scaling relationship of the radiating area on the surface of the measured wafer on the detector's sensitive surface. It is determined by the combination of the front-end imaging lens group, the optical guide coupling structure, and the relay optical system. Under given optical design and mechanical installation conditions, this optical magnification is a fixed parameter used to convert the radiance at the wafer end into the equivalent radiant flux incident on the detector.

[0041] Specifically, the expression for calculating the corrected radiation quantity R_i is: R_i=V_i / [ξ(λ_i)×C]= ε(λ_i)×L_bb(λ_i, T); Substituting the emissivity model into the above expression, the link equation can be expressed as: R_i= (a + b / λ_i)×L_bb(λ_i, T).

[0042] As can be seen from the above expression, the attenuation coefficient ξ(λ_i) is used to restore the original measurement signal V_i to R_i, which is only related to the emissivity ε and temperature T of the physical properties. Therefore, the core problem to be solved in this application is transformed into: given multiple (λ_i, R_i) data points, solving for a set of (T, a, b) such that the above equation optimally fits all data points in the least squares sense.

[0043] Please see Figure 3 Optionally, based on the corrected radiation, the link equation is iteratively optimized alternately until a preset iteration condition is met to obtain temperature information, including: Step 302: Set the temperature estimate, fix the temperature estimate, and iteratively optimize the emissivity model parameters in the link equation based on the temperature estimate and the corrected radiation amount until the first preset condition is met, and obtain the optimal emissivity model parameters.

[0044] Step 304: Fix the optimal emissivity model parameters, and iteratively optimize the temperature data in the link equation based on the optimal emissivity model parameters until the second preset condition is met, and obtain the optimal temperature data.

[0045] Step 306: Use the optimal temperature data as the so-called target temperature and the optimal emissivity model parameters as the target emissivity model parameters.

[0046] Specifically, since the equation R_i = (a + b / λ_i) × L_bb(λ_i, T) is nonlinear with respect to temperature T and (a, b), it cannot be solved analytically directly. Therefore, the alternating iterative optimization method is adopted in the embodiments of this application.

[0047] First, using the temperature data as a fixed value, iteratively optimize the emissivity model parameters (a, b) to find the optimal emissivity model parameters (a_new, b_new) at the current temperature, so that the error between the model-predicted R_i and the actual measured R_i is minimized. Here, the actual measured R_i is the corrected radiation quantity mentioned above.

[0048] Secondly, the optimal emissivity model parameters (a_new, b_new) obtained through optimization are used as fixed values ​​to iteratively optimize the temperature data, so that the temperature change is less than a set value, and the final temperature value is obtained.

[0049] Please see Figure 4 Optionally, the emissivity model parameters include a first parameter a and a second parameter b; the emissivity model parameters in the link equation are iteratively optimized based on the temperature estimate until a first preset condition is met, resulting in the optimal emissivity model parameters, including: Step 402: For each wavelength channel, the corresponding radiation quantity in the link equation is expressed as a linear combination of the blackbody radiance term and the wavelength-normalized blackbody radiance term, so as to construct an overdetermined linear equation system with respect to the first parameter and the second parameter.

[0050] Step 404: Based on the overdetermined linear equations, the least squares algorithm is used to solve for the first and second parameters to obtain the updated emissivity model parameters under the current temperature estimate.

[0051] Step 406: Substitute the updated emissivity model parameters into the link equation to calculate the error between the predicted radiation and the corrected radiation. If the error is less than the first preset threshold, the updated emissivity model parameters are used as the optimal emissivity model parameters; otherwise, the updated emissivity model parameters are used to reconstruct the overdetermined linear equation system and repeat the least squares solution process until the error is less than the first preset threshold.

[0052] Specifically, if a temperature estimate T_cur is set, then under a fixed temperature estimate T_cur, the link mode can be rewritten as: R_i = a × L_bb(λ_i, T_cur) + b × [L_bb(λ_i, T_cur) / λ_i]; Let X_i = L_bb(λ_i, T_cur), Y_i = X_i / λ_i, then the equation can be expressed as: R_i = a × X_i + b × Y_i.

[0053] For all N wavelength channels, a system of overdetermined linear equations is formed: R = X × θ; where, It should be noted that T here stands for transpose.

[0054] Solve using the standard least squares method: The updated emissivity model parameters are obtained. These updated parameters are then substituted into the equation R_i = a×X_i + b×Y_i to calculate the predicted radiance. This predicted radiance is compared with the corrected radiance, and the error between them is calculated. If the error is less than a first preset threshold, the first preset condition is met, and the current emissivity model parameters are taken as the optimal emissivity parameters (a_new, b_new) at the current temperature T_cur.

[0055] If the error is greater than the first preset threshold, the current emissivity model parameters are used to reconstruct the overdetermined linear equations and repeat the above least squares solution process until the error is less than the first preset threshold.

[0056] By employing the above scheme, under a given temperature estimate, the radiance corresponding to each wavelength channel in the link equation is uniformly expressed as a linear combination of a blackbody radiance term and a wavelength-normalized blackbody radiance term. An overdetermined linear equation system is constructed regarding the first and second parameters. This transforms the solution of the emissivity model parameters into a standard linear least squares problem, simultaneously fitting multiple wavelength data across the entire spectrum using the minimum residual sum of squares as the criterion. This significantly suppresses the influence of single-point noise or local measurement anomalies on the fitting results. Furthermore, by substituting the updated emissivity model parameters back into the link equation, calculating the error between the predicted and corrected radiance, and combining this with iterative convergence control using a first preset threshold, adaptive optimization and automatic convergence of the emissivity model parameters can be achieved. This makes the obtained optimal emissivity model parameters physically more consistent with actual radiation characteristics, thereby reducing the error propagation of emissivity uncertainty to temperature inversion and improving the accuracy and stability of the temperature solution results. Meanwhile, this process involves only the least squares solution of two linear parameters, which has low computational complexity and low implementation cost. It is suitable for real-time online calculation in embedded temperature measurement devices, which is beneficial for maintaining the consistency and reliability of the system's temperature measurement performance under long-term continuous operation. Please see Figure 5 Optionally, the temperature data in the link equation is iteratively optimized based on the optimal emissivity model parameters until the second preset condition is met, resulting in optimal temperature data, including: Step 502: For each wavelength channel, based on the optimal emissivity model parameters and the blackbody radiance function, construct an objective function to characterize the sum of squared residuals between the predicted radiation and the measured radiation, with temperature data as the independent variable in the objective function.

[0057] Step 504: Using the temperature estimate as the initial temperature data, the objective function is numerically solved based on a univariate nonlinear optimization algorithm to obtain the updated temperature data.

[0058] Step 506: Calculate the absolute value of the difference between the temperature data before and after the update. When the absolute value is less than the second preset threshold, the updated temperature data is determined to be the optimal temperature data. Otherwise, the updated temperature data is used as the new temperature estimate, and the numerical solution process is repeated until the absolute value of the difference between the temperature data before and after the update is less than the second preset threshold.

[0059] Specifically, the emissivity model (a_new, b_new) obtained through the above iterative optimization is used as a fixed value to iteratively optimize the temperature data. The goal is to find a new temperature T_new that best matches the overall radiant quantity and corrected radiant quantity across all wavelength channels. The above steps constitute a univariate nonlinear optimization problem. Therefore, this embodiment first defines an objective function, which is the residual sum of squares F(T):

[0060] To find the T that minimizes F(T), this embodiment exemplifies the use of numerical methods such as Newton's iteration method or Brent's method. The solution steps include: calculating the objective function F(T_cur) and its derivative or numerical difference with respect to T; updating the temperature estimate according to the optimization algorithm. Taking Newton's iteration method as an example, its expression is T_new = T_cur - F'(T_cur) / F''(T_cur); where F'(T_cur) represents the first derivative of F(T) with respect to temperature T, and F''(T_cur) represents the second derivative of F(T) with respect to temperature T.

[0061] The iteration continues until F(T) converges, and the corresponding T is denoted as T_new. The temperature T_old obtained in the previous iteration is calculated, and the absolute value of the temperature change is calculated as: ΔT = |T_new - T_old|. If ΔT < δ_T (i.e., the second preset threshold mentioned above), then the second preset condition is met, the loop is exited, and the current temperature value is taken as the final target temperature; otherwise, the iteration continues using the current temperature.

[0062] Thus, the target temperature and its corresponding target emissivity model parameters are obtained.

[0063] By adopting the above scheme, and based on the convergence of the emissivity model parameters, an objective function is further constructed, using temperature data as the independent variable to characterize the sum of squared residuals between the model-predicted and measured radiance. This transforms the temperature solution into a strictly univariate nonlinear optimization problem, enabling the comprehensive utilization of information from multiple wavelength channels across the entire spectrum and imposing global consistency constraints on temperature, thus reducing the impact of single-wavelength deviations on the temperature inversion results. Furthermore, using the previous round of temperature estimates as initial temperature data, numerical solutions are obtained using a univariate nonlinear optimization algorithm. By comparing the absolute value of the difference between the updated and unupdated temperature data with a second preset threshold, convergence control of the temperature iteration process is achieved. This ensures that the temperature estimation process possesses good numerical stability and a controllable convergence speed while maintaining convergence accuracy. Therefore, optimal temperature data with higher physical consistency and stronger noise resistance can be obtained in complex process environments and over a wide temperature range, significantly improving the accuracy and long-term operational stability of radiation-based temperature inversion, which is beneficial for achieving high-precision temperature monitoring and closed-loop control in wafer fabrication processes.

[0064] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0065] Based on the same inventive concept, this application also provides a temperature measurement system. This system is applicable to the above-described temperature measurement method. The solution provided by this system is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more system embodiments provided below can be found in the limitations of the method above, and will not be repeated here.

[0066] Please see Figure 6 In one embodiment, the temperature measurement system is disposed within the temperature measurement device, and the temperature measurement device is partially disposed within the wafer reaction cavity. The temperature measurement device includes a reference light source and a target light source. The temperature measurement system includes: an acquisition module, a first processing module, and a second processing module.

[0067] The acquisition module is used to acquire a reference spectral signal and a target spectral signal, wherein the reference spectral signal is generated by a reference light source illuminating the wafer surface, and the target spectral signal is generated by a target light source illuminating the wafer surface. The first processing module is used to calculate the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value, which is obtained based on a standard blackbody reference source; it is also used to correct the target spectral signal based on the attenuation coefficient to obtain the corrected radiation amount. The second processing module is used to perform alternating iterative optimization of the link equation based on the corrected radiance until the preset iteration conditions are met to obtain temperature information. The link equation is used to characterize the correspondence between the predicted spectral signal and the pre-built emissivity model and blackbody radiance function. The temperature information includes the target temperature and its corresponding target emissivity model parameters.

[0068] Optionally, the first processing module calculates the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value, including: calculating the ratio of the reference spectral signal to the theoretical blackbody radiation value to obtain the attenuation coefficient. Optionally, the first processing module corrects the target spectral signal based on the attenuation coefficient to obtain the corrected radiance, including: The corrected radiation quantity is obtained by calculating the first product of the attenuation coefficient and the system constant, and by calculating the ratio of the target spectral signal to the first product; wherein, the system constant is related to the optical geometry parameters and amplification of the measurement optical path.

[0069] Optionally, the second processing module performs alternating iterative optimization of the link equation based on the corrected radiance until a preset iteration condition is met to obtain temperature information. This includes: setting a temperature estimate; fixing the temperature estimate; and iteratively optimizing the emissivity model parameters in the link equation based on the temperature estimate and the corrected radiance until a first preset condition is met to obtain the optimal emissivity model parameters; fixing the optimal emissivity model parameters; and iteratively optimizing the temperature data in the link equation based on the optimal emissivity model parameters until a second preset condition is met to obtain the optimal temperature data; and using the optimal temperature data as the so-called target temperature and the optimal emissivity model parameters as the target emissivity model parameters.

[0070] Optionally, the emissivity model parameters include a first parameter and a second parameter. The second processing module iteratively optimizes the emissivity model parameters in the link equation based on the temperature estimate until a first preset condition is met, obtaining the optimal emissivity model parameters. This includes: for each wavelength channel, representing the corresponding radiation quantity in the link equation as a linear combination of a blackbody radiance term and a wavelength-normalized blackbody radiance term to construct an overdetermined linear equation system about the first and second parameters; based on the overdetermined linear equation system, using a least squares solution algorithm to solve for the first and second parameters, obtaining the updated emissivity model parameters under the current temperature estimate; substituting the updated emissivity model parameters into the link equation, calculating the error between the predicted radiation quantity and the corrected radiation quantity; if the error is less than a first preset threshold, then the updated emissivity model parameters are used as the optimal emissivity model parameters; otherwise, using the updated emissivity model parameters, reconstructing the overdetermined linear equation system and repeating the least squares solution process until the error is less than the first preset threshold.

[0071] Optionally, the second processing module iteratively optimizes the temperature data in the link equation based on the optimal emissivity model parameters until the second preset condition is met to obtain the optimal temperature data. This includes: for each wavelength channel, constructing an objective function representing the sum of squared residuals between the predicted radiation and the measured radiation based on the optimal emissivity model parameters and the blackbody radiance function, with temperature data as the independent variable in the objective function; using the temperature estimate as the initial temperature data, numerically solving the objective function based on a univariate nonlinear optimization algorithm to obtain the updated temperature data; calculating the absolute value of the difference between the temperature data before and after the update, and determining that the updated temperature data is the optimal temperature data when the absolute value is less than the second preset threshold; otherwise, using the updated temperature data as the new temperature estimate and repeating the numerical solution process until the absolute value of the difference between the temperature data before and after the update is less than the second preset threshold.

[0072] The aforementioned temperature measurement system corrects the target spectral signal using an attenuation coefficient calculated from a reference spectral signal and theoretical blackbody radiation values, yielding a corrected radiance. Based on this corrected radiance, the link equation, which simultaneously incorporates a pre-built emissivity model and a blackbody radiance function, is iteratively optimized to obtain temperature information, including the target temperature and its corresponding target emissivity model parameters. Using this approach, this application can compensate in real-time for link response attenuation caused by optical window contamination, changes in optical element transmittance, and detector gain drift, maintaining long-term stability and consistency of the temperature measurement link without requiring a high-temperature blackbody source. Furthermore, the link equation in this application achieves joint solving of emissivity model parameters and wafer surface temperature, reducing the impact of emissivity uncertainty on temperature inversion results. This enables high-precision, wide-range measurement of wafer temperature in complex process environments, improving the accuracy of temperature closed-loop control and process yield, and facilitating engineering applications in compact temperature probes with limited space. Each module in the aforementioned temperature measurement system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0073] In one feasible embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements the aforementioned temperature measurement method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0074] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0075] In one feasible embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps in the above-described temperature measurement method.

[0076] In one feasible embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method steps in the temperature measurement method described above.

[0077] In one feasible embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the method steps in the above-described temperature measurement method.

[0078] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0079] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A temperature measurement method, characterized by, Applicable to temperature measuring equipment, wherein the temperature measuring equipment is partially disposed within a wafer reaction cavity, and the temperature measuring equipment includes a reference light source and a target light source; the method includes: A reference spectral signal is acquired, and an attenuation coefficient is calculated based on the reference spectral signal and the theoretical blackbody radiation value; wherein the reference spectral signal is generated by the reference light source irradiating the wafer surface, and the theoretical blackbody radiation value is obtained based on a standard blackbody reference source; The target spectral signal is acquired, and the target spectral signal is corrected according to the attenuation coefficient to obtain the corrected radiance; wherein the target spectral signal is generated by the target light source irradiating the wafer surface; Based on the corrected radiation, the link equation is iteratively optimized until a preset iteration condition is met to obtain temperature information. This includes setting a temperature estimate, fixing the temperature estimate, and iteratively optimizing the emissivity model parameters in the link equation based on the temperature estimate and the corrected radiation until a first preset condition is met to obtain the optimal emissivity model parameters. The optimal emissivity model parameters are fixed, and the temperature data in the link equation is iteratively optimized based on the optimal emissivity model parameters until the second preset condition is met, thereby obtaining the optimal temperature data. The optimal temperature data is taken as the so-called target temperature, and the optimal emissivity model parameters are taken as the target emissivity model parameters; The link equation is used to characterize the correspondence between the radiation quantity and the pre-built emissivity model and blackbody radiance function; the temperature information includes the target temperature and its corresponding target emissivity model parameters.

2. The method of claim 1, wherein, The calculation of the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value includes: The attenuation coefficient is obtained by calculating the ratio of the reference spectral signal to the theoretical blackbody radiation value.

3. The method according to claim 1, characterized in that, The step of correcting the target spectral signal according to the attenuation coefficient to obtain the corrected radiance includes: The attenuation coefficient and the first product of the system constant are calculated, and the ratio of the target spectral signal to the first product is calculated to obtain the corrected radiation amount; wherein the system constant is related to the optical geometry parameters and magnification of the measurement optical path.

4. The method according to claim 1, characterized in that, The emissivity model parameters include a first parameter and a second parameter; The step of iteratively optimizing the emissivity model parameters in the link equation based on the temperature estimate until a first preset condition is met to obtain the optimal emissivity model parameters includes: For each wavelength channel, the corresponding radiation quantity in the link equation is expressed as a linear combination of the blackbody radiance term and the wavelength-normalized blackbody radiance term, so as to construct an overdetermined linear equation system about the first parameter and the second parameter. Based on the overdetermined linear equations, the least squares algorithm is used to solve for the first parameter and the second parameter to obtain the updated emissivity model parameters under the current temperature estimate. Substitute the updated emissivity model parameters into the link equation to calculate the error between the predicted radiation and the corrected radiation. If the error is less than a first preset threshold, the updated emissivity model parameters are used as the optimal emissivity model parameters. Otherwise, the updated emissivity model parameters are used to reconstruct the overdetermined linear equations and repeat the least squares solution process until the error is less than the first preset threshold.

5. The method according to claim 1, characterized in that, The iterative optimization of the temperature data in the link equation based on the optimal emissivity model parameters until the second preset condition is met, to obtain the optimal temperature data, includes: For each wavelength channel, based on the optimal emissivity model parameters and the blackbody radiance function, an objective function is constructed to characterize the sum of squared residuals between the model-predicted radiation and the measured radiation, with temperature data as the independent variable in the objective function. Using the temperature estimate as initial temperature data, the objective function is numerically solved based on a univariate nonlinear optimization algorithm to obtain updated temperature data. Calculate the absolute value of the difference between the temperature data before and after the update. If the absolute value is less than the second preset threshold, the updated temperature data is determined to be the optimal temperature data. Otherwise, the updated temperature data is used as the new temperature estimate, and the numerical solution process is repeated until the absolute value of the difference between the temperature data before and after the update is less than the second preset threshold.

6. A temperature measurement system, characterized in that, The temperature measurement system applicable to the temperature measurement method according to any one of claims 1-5 is disposed within a temperature measurement device, wherein the temperature measurement device is partially disposed within a wafer reaction cavity, and the temperature measurement device includes a reference light source and a target light source; the system includes: An acquisition module is used to acquire a reference spectral signal and a target spectral signal, wherein the reference spectral signal is generated by the reference light source irradiating the wafer surface, and the target spectral signal is generated by the target light source irradiating the wafer surface; The first processing module is used to calculate the attenuation coefficient based on the reference spectral signal and the theoretical blackbody radiation value, wherein the theoretical blackbody radiation value is obtained based on a standard blackbody reference source; it is also used to correct the target spectral signal based on the attenuation coefficient to obtain the corrected radiation amount; The second processing module is used to perform alternating iterative optimization of the link equation based on the corrected radiance until a preset iteration condition is met to obtain temperature information; wherein, the link equation is used to characterize the correspondence between the predicted spectral signal and the pre-constructed emissivity model and blackbody radiance function; the temperature information includes the target temperature and its corresponding target emissivity model parameters.

7. A temperature measuring device, characterized in that, Partially located within the wafer reaction chamber; the temperature measuring device includes: A reference light source is used to output a reference light signal to the wafer surface during wafer fabrication. Target light source, used to output target light signals to the wafer surface during wafer fabrication; A baffle mechanism is used to switch between the measurement position and the calibration position; A light guide is used to receive and transmit the reference light signal when the baffle mechanism is in the calibration position; and to receive and transmit the target light signal when the baffle mechanism is in the measurement position. A detector is used to convert a reflected reference light signal into a reference spectral signal, or a reflected target light signal into a target spectral signal; wherein the reflected reference light signal is generated by the reflection of the reference light signal from the wafer surface, and the reflected target light signal is generated by the reflection of the target light signal from the wafer surface; The temperature measurement system as described in claim 6 is used to perform alternating iterative optimization of the link equation based on the reference spectral signal and the target spectral signal until a preset iteration condition is met, thereby obtaining temperature information, wherein the temperature information includes the target temperature and its corresponding target emissivity model parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

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