Method for carrying out parameter fitting on spectroscopic measurement data, thickness measurement method and equipment

Abstract

The invention provides a method for performing parameter fitting on spectroscopic measurement data, and a thickness measurement method and equipment. The method comprises the following steps: acquiring target spectroscopic measurement data; determining interval nodes based on an independent variable range in the target spectroscopic measurement data; performing curve fitting on a custom function constructed by each interval node to obtain a plurality of first candidate fitting functions; calculating a first root-mean-square error between each first candidate fitting function and the target spectroscopic measurement data; obtaining a current root-mean-square error value of the current optimal fitting function with the minimum first root-mean-square error; and in response to the condition that the current root-mean-square error value meets a preset convergence condition, taking the corresponding first candidate fitting function as a target fitting equation of the target spectroscopic measurement data based on the preset convergence condition. By implementing the method, the to-be-fitted data is approached by gradually adding the peaks at different positions and optimizing the fitting mode, so that efficient and accurate automatic fitting can be realized, and relatively accurate position and intensity information of each peak can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for performing parameter fitting on spectroscopy measurement data, a thickness measurement method and equipment. Background Art

[0002] Accurately fitting spectroscopic measurement data, such as spectra and energy spectra, to interpret information such as position and intensity is crucial for both material science research and everyday detection. However, manual fitting is still the primary method used in related technologies. This approach suffers from inaccuracies, low repeatability, and low reliability, necessitating the development of an automated spectral fitting method. Summary of the Invention

[0003] In view of this, the present invention provides a method for performing parameter fitting on spectroscopy measurement data, a thickness measurement method and an apparatus.

[0004] In a first aspect, an embodiment of the present invention proposes a method for parameter fitting of spectroscopic measurement data, comprising: obtaining target spectroscopic measurement data; determining interval nodes based on the range of independent variables in the target spectroscopic measurement data; performing curve fitting on a custom function constructed with each interval node to obtain a plurality of first candidate fitting functions; calculating a first root mean square error between each first candidate fitting function and the target spectroscopic measurement data; obtaining a current root mean square error value of the current optimal fitting function with the smallest first root mean square error; in response to the current root mean square error value meeting a preset convergence condition, using the corresponding first candidate fitting function as a target fitting equation for the target spectroscopic measurement data based on the preset convergence condition.

[0005] In second aspect, an embodiment of the present invention proposes a thickness measurement method, which includes: obtaining a target fitting function using the method described in any implementation method of the first aspect; obtaining the main peak peak value and secondary peak peak value of the current frame based on the target fitting function; and calculating the wafer thickness of the current frame based on the main peak peak value and the second peak peak value.

[0006] In a third aspect, an embodiment of the present invention provides a thinning control method, which includes: obtaining the wafer thickness of the current frame according to the method described in the second aspect; and stopping wafer thinning when the wafer thickness of the current frame reaches a preset thickness.

[0007] In a fourth aspect, an embodiment of the present invention proposes a device for performing parameter fitting on spectroscopic measurement data, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that when the at least one processor executes, it can implement the method for performing parameter fitting on spectroscopic measurement data as described in any implementation method in the first aspect.

[0008] In a fifth aspect, an embodiment of the present invention provides a thickness measuring device, comprising: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor performs the thickness measurement method described in the second aspect.

[0009] In the sixth aspect, an embodiment of the present invention proposes a thinning control device, which includes: a processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the thinning control method described in the third aspect.

[0010] In a seventh aspect, an embodiment of the present invention provides a thinning machine for thinning a wafer and performing the method described in any implementation of the first aspect, the second aspect or the third aspect during the thinning process.

[0011] The method for parameter fitting of spectroscopic measurement data, thickness measurement method, and apparatus provided in embodiments of the present invention dynamically determine whether further iteration is needed by comparing the ratio of the current RMSE to the historical optimal RMSE. Each iteration only adds a new set of candidate peaks and optimizes their parameters, rather than globally adjusting all parameters. This significantly reduces computational complexity. By gradually adding peaks at different locations and optimizing the fit to approximate the data to be fitted, effective fitting of the spectrum to be fitted can be achieved after convergence without the need for manual input of initial parameters, thereby enabling efficient and accurate automated fitting and obtaining relatively accurate information on the positions and intensities of each peak.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 is an exemplary system architecture in which the present invention may be applied; Figure 2 A flow chart of a method for performing parameter fitting on spectroscopy measurement data provided by an embodiment of the present invention; Figure 3 A flowchart of another method for performing parameter fitting on spectroscopy measurement data provided by an embodiment of the present invention; Figure 4 A flowchart of another method for performing parameter fitting on spectroscopy measurement data provided by an embodiment of the present invention; Figure 5 A flowchart of another method for performing parameter fitting on spectroscopy measurement data provided by an embodiment of the present invention; Figures 6A-6D A schematic diagram of a method for parameter fitting of spectroscopy measurement data in an application scenario provided by an embodiment of the present invention; Figure 7 A structural block diagram of an apparatus for performing parameter fitting on spectroscopy measurement data provided by an embodiment of the present invention; Figure 8 A schematic structural diagram of an electronic device suitable for executing a method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0017] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components; wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0018] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the method and apparatus for parameter fitting of spectroscopic measurement data of the present invention may be applied.

[0020] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0021] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 and server 105 may be installed with various applications for enabling information communication between them, such as instant messaging applications.

[0022] Terminal devices 101, 102, 103 and server 105 can be either hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules, or as a single software or software module, and are not specifically limited here. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules, or as a single software or software module, and are not specifically limited here.

[0023] Server 105 can provide various services through various built-in applications. It should be noted that in addition to being obtained from terminal devices 101, 102, and 103 via network 104, the data or information required to provide these services can also be pre-stored locally on server 105 in various ways. Therefore, when server 105 detects that this data is already stored locally, it can choose to directly obtain it locally. In this case, exemplary system architecture 100 may also not include terminal devices 101, 102, 103 and network 104.

[0024] Because data analysis and processing may require significant computational resources and high computing power, the methods for parameter fitting of spectroscopic measurement data provided in the subsequent embodiments of the present invention are generally performed by a server 105 possessing significant computational power and resources. Accordingly, the apparatus for parameter fitting of spectroscopic measurement data is also generally located within the server 105. However, it should also be noted that, if terminal devices 101, 102, and 103 also possess sufficient computational power and resources, the terminal devices 101, 102, and 103 may also utilize the relevant applications installed thereon to perform the various computations previously assigned to the server 105, thereby outputting the same results as the server 105. In particular, in the presence of multiple terminal devices with varying computational capabilities, if the relevant application determines that the terminal device in question possesses significant computational power and abundant remaining computing resources, the terminal device may be assigned to perform the aforementioned computations, thereby appropriately alleviating the computational burden on the server 105. Accordingly, the apparatus for parameter fitting of spectroscopic measurement data may also be located within the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104 .

[0025] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0026] Please refer to Figure 2 , Figure 2 A flow chart of a method for parameter fitting of spectroscopic measurement data provided by an embodiment of the present invention, wherein process 200 includes the following steps: Step 201: Acquire target spectroscopy measurement data.

[0027] This step is intended to be performed by the execution subject of the method for parameter fitting of spectroscopic measurement data (e.g. Figure 1 The server 105 shown in the figure obtains the corresponding target spectroscopic measurement data. In this embodiment, the target spectroscopic measurement data may include at least one or more of a spectrum, an energy spectrum, a chromatography, and a nuclear magnetic resonance spectrum (for example, ultraviolet-visible-near-infrared spectrum, X-ray photoelectron spectroscopy (XPS spectrum), solid-state nuclear magnetic resonance spectrum, etc.), and measurement data generated by various spectroscopic analysis methods can be obtained. The target spectroscopic measurement data includes data of overlapping peaks, which include a main peak and a secondary peak.

[0028] Step 202: Determine interval nodes based on the range of the independent variable in the target spectroscopy measurement data.

[0029] In this step, the execution subject determines the interval nodes based on the independent variable range of the target spectroscopy measurement data, that is, the interval is divided according to the independent variable range to obtain multiple value intervals, and each value interval is separated by an interval node.

[0030] Step 203: performing curve fitting on the user-defined functions constructed with each interval node to obtain a plurality of first candidate fitting functions.

[0031] In this embodiment, the execution entity may construct a corresponding custom function based on the independent variable of the above-mentioned interval node as the symmetry center, and perform curve fitting on the custom function to obtain multiple first candidate fitting functions.

[0032] Step 204: Calculate a first root mean square error between each first candidate fitting function and the target spectroscopy measurement data.

[0033] In this embodiment, the first root mean square error (RMSE) between each first candidate fitting function and the target spectroscopic measurement data is calculated. The root mean square error (RMSE) is a core indicator for quantifying the deviation between the candidate fitting function and the target spectroscopic measurement data. For example, considering the general distribution patterns of spectra, energy spectra, etc., a pseudo-Voigt function can be used to fit them. The pseudo-Voigt function includes four parameters for peaks and valleys: position, height, width, and Gaussian function proportion. The pseudo-Voigt function equation obtained after fitting is set to F1, and the four parameters are set to p1.

[0034] In some optional implementations of this embodiment, the pseudo Voigt function is in the form of: , Where intensity represents the intensity at any wavelength in the target spectral measurement data, x represents the wavelength independent variable in the target spectral measurement data, amplitude represents the height of the function, frac represents the proportion of the Gaussian function in the pseudo-Voigt function (a pseudo-Voigt function is a Gaussian function with a certain proportion plus a Cauchy function with another proportion, the sum of the two parts is 1, and both coefficients are not less than 0), center1 represents the position or symmetry center of the main peak, and std is the coefficient that determines the width of the Gaussian function or Cauchy function.

[0035] Step 205: Obtain the current root mean square error value of the current best fitting function with the minimum first root mean square error.

[0036] This step aims to enable the execution subject to find the candidate fitting function corresponding to the smallest first root mean square error value as the current optimal fitting function, and record the current root mean square error value rmse_current of the current optimal fitting function.

[0037] Step 206: In response to the current RMS error value meeting the preset convergence condition, the corresponding first candidate fitting function is used as a target fitting equation for the target spectroscopy measurement data based on the preset convergence condition.

[0038] In this embodiment, the execution entity determines whether the current root mean square error value meets the preset convergence conditions. In this embodiment, there are two standards for RMSE convergence judgment: an absolute standard and a relative standard. The absolute standard is, for example, a threshold value of 0.01. If the value is less than this threshold after a certain iteration, the iteration is directly considered complete. The relative standard is a threshold value of the previous RMSE, such as 80%. If the RMSE after a certain iteration is not less than the set threshold value of the previous RMSE, such as 80%, the iteration is stopped, the current iteration data is discarded, and the result of the previous iteration is used as the final result. If the absolute standard cannot be met, the relative standard can be used to avoid infinite iterations and overfitting. New peaks are only recognized when adding new peaks can significantly reduce the RMSE. Exemplarily, the preset convergence conditions may include: the current root mean square error value is greater than or equal to a preset threshold value, which is determined based on the historical optimal root mean square error value rmse_last; or the current root mean square error value is less than the preset absolute standard value. In specific implementation, since the initial value of the preset threshold can be set to infinity, it can be guaranteed that the subsequent iterative optimization process will be entered for the first time. The preset threshold value can be set to the historical optimal RMS error value multiplied by a preset ratio, for example, 0.8*rmse_last. It should be noted that the initial value of the preset threshold value and the proportional relationship with the historical optimal RMS error value are for illustration only and can be adjusted according to actual application scenarios, and the present invention is not limited thereto.

[0039] The method for parameter fitting of spectroscopic measurement data provided in this embodiment approximates the data to be fitted by gradually adding peaks at different positions and optimizing the fitting. After convergence, effective fitting of the spectrum to be fitted can be achieved without the need for manual input of initial parameters, thereby achieving efficient and accurate automated fitting and obtaining relatively accurate peak position and intensity information.

[0040] Please refer to Figure 3 , Figure 3 A flowchart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present disclosure, wherein process 300 includes the following steps: Step 301: Acquire target spectroscopy measurement data.

[0041] Step 302: Determine interval nodes based on the range of the independent variable in the target spectroscopy measurement data.

[0042] Step 303: performing curve fitting on the user-defined functions constructed with each interval node to obtain a plurality of first candidate fitting functions.

[0043] Step 304: Calculate a first root mean square error between each first candidate fitting function and the target spectroscopy measurement data.

[0044] Step 305: Obtain the current root mean square error value of the current best fitting function with the minimum first root mean square error.

[0045] The above steps 301-305 are similar to the following Figure 2 Steps 201-205 shown are consistent. For the same content, please refer to the corresponding part of the previous embodiment and will not be repeated here.

[0046] Step 306: In response to the current RMS error value not meeting the preset convergence condition, the current RMS error is used as a new historical optimal RMS error value. If the current RMS error value meets the preset convergence condition, the current optimal fitting function is determined as the target fitting function for the target spectroscopy measurement data.

[0047] Step 307: Using the target spectroscopy measurement data as target data, curve fitting is performed on the sum function of the current optimal fitting function and each defined function to obtain a plurality of second candidate fitting functions.

[0048] In this step, the execution subject adds the sum function of multiple custom functions obtained in the previous steps to the currently obtained optimal fitting function, performs curve fitting with the target spectroscopy measurement data as the target data, and obtains multiple second candidate fitting functions.

[0049] Step 308: Calculate the second root mean square error between each second candidate fitting function and the target spectroscopy measurement data.

[0050] In this step, the execution entity calculates a second root mean square error between each second candidate fitting function and the target spectroscopy measurement data.

[0051] Step 309: Replace the first root mean square error with the second root mean square error, and return to step 305 to step 308 until the new current root mean square error value meets the preset convergence condition.

[0052] In this embodiment, after calculating the second RMS error, the execution entity may use the second RMS error to replace the first RMS error and return to execute steps 305 to 308 until the calculated new current RMS error value meets the preset convergence condition.

[0053] Step 310: Based on a preset convergence condition, the corresponding candidate fitting function is used as a target fitting function for the target spectroscopy measurement data.

[0054] In this embodiment, the process of determining the target fitting function is configured accordingly for different preset convergence conditions. For example, in response to the current RMS error being greater than or equal to a preset threshold, the candidate fitting function corresponding to the previous RMS error value is used as the target fitting function; or, in response to the current RMS error being less than a preset absolute standard value, the candidate fitting function corresponding to the current RMS error value is used as the target fitting function.

[0055] The method for parameter fitting of spectroscopic measurement data provided in this embodiment dynamically determines whether further iterations are needed by comparing the ratio of the current RMSE to the historical optimal RMSE. Each iteration only adds a set of candidate peaks and optimizes their parameters, rather than globally adjusting all parameters. This significantly reduces computational complexity. By gradually adding peaks at different locations and optimizing the fit to approximate the data to be fitted, effective fitting of the spectrum to be fitted can be achieved after convergence without the need for manual input of initial parameters, thereby achieving efficient and accurate automated fitting and obtaining relatively accurate information on the positions and intensities of each peak.

[0056] Please refer to Figure 4 , Figure 4 A flow chart of a method for parameter fitting of spectroscopy measurement data provided in an embodiment of the present disclosure, namely, Figure 2 Step 202 in the process 200 shown provides a specific implementation method. The other steps in the process 200 are not adjusted. The specific implementation method provided in this embodiment is replaced by step 201 to obtain a new complete embodiment. The process 400 includes the following steps: Step 401: Obtain the maximum value and the minimum value of the independent variable range in the target spectroscopy measurement data.

[0057] In this step, the execution subject obtains the maximum value Xmax and the minimum value Xmin of the independent variable range in the target spectroscopy measurement data.

[0058] Step 402: Determine interval nodes in the independent variable range based on the independent variable range, the maximum value, and the minimum value.

[0059] In this step, the execution entity determines an interval parameter based on the independent variable range, the maximum value Xmax, and the minimum value Xmin. The interval parameter can be the difference between the maximum value Xmax and the minimum value Xmin, divided by a first number. The first number is the preset number of peaks in the target spectroscopic measurement data. For example, for target spectroscopic measurement data with a preset number of 10 peaks, the interval parameter is interval = (Xmax - Xmin) / 10.

[0060] In this embodiment, based on the independent variable range, the maximum value and the minimum value in the independent variable range, the interval nodes are determined by functional distribution, random distribution, or average distribution.

[0061] For example, the independent variable range can be divided into (the first number + 1) intervals, with the length of the first and last intervals being half the length of the remaining intervals, and the nodes that separate the intervals being designated as interval nodes. For example, for target spectroscopic measurement data with a preset number of 10 peaks, the independent variable range can be divided into 11 parts using the formula 1 * 0.5 + 9 * 1 + 1 * 0.5, where the lengths of the first and last segments are 0.5 times the interval, and the lengths of the remaining segments are 1 times the interval. The points between segments are referred to as interval nodes, and the set of points is referred to as the interval node set. Alternatively, the independent variable range can be allocated according to a preset function (e.g., a mathematical function (e.g., a linear function, a probability density function), a custom function (e.g., a rule defined in programming), or a distribution function) to obtain corresponding interval nodes. Alternatively, the independent variable range can be allocated according to a random allocation method to obtain corresponding interval nodes. Alternatively, a specific allocation method can be set according to the needs of the actual application scenario, and the present invention is not limited thereto.

[0062] Furthermore, in step 203, the process of curve fitting the custom functions constructed at each interval node using the target spectral measurement data as the target data to obtain multiple first candidate fitting functions mainly includes: constructing corresponding custom functions using each of the interval nodes as the center of symmetry; and curve fitting each custom function using the target spectral measurement data as the target data to obtain multiple first candidate fitting functions. Exemplarily, the Pseudo Voigt function is used as the custom function for illustration. With the independent variable of each point as the center of symmetry of the Pseudo Voigt function, 10 different Pseudo Voigt functions are constructed, with a Gaussian fraction of 0.5, a peak width of interval, and a height of 0.5. Using the target spectral measurement data as the target data, curve fitting is performed on these 10 Pseudo Voigt functions to obtain multiple first candidate fitting functions.

[0063] Please refer to Figure 5 , Figure 5 A flow chart of a method for parameter fitting of spectroscopy measurement data provided in an embodiment of the present disclosure, namely, Figure 2Step 201 in the process 200 shown provides a specific implementation method. The other steps in the process 200 are not adjusted. The specific implementation method provided in this embodiment is replaced by step 201 to obtain a new complete embodiment. The process 500 includes the following steps: Step 501: Acquire original spectroscopy measurement data.

[0064] The raw spectroscopic measurement data may be raw data of one or more of a spectrum, an energy spectrum, a chromatography, and a nuclear magnetic resonance spectrum. The raw data may contain noise data and background data. In this embodiment, the noise data and background data in the raw data may be processed.

[0065] Step 502: In response to preset background spectroscopy measurement data, background removal processing is performed on the original spectroscopy measurement data based on the background spectroscopy measurement data to obtain background-removed target spectroscopy measurement data.

[0066] In the case where the original spectral measurement data contains background spectral measurement data, the original spectral measurement data may be subjected to background removal processing (eg, subtracting the background spectral measurement data from the original spectral measurement data) to obtain background-removed target spectral measurement data.

[0067] Step 503: In response to the fact that no background spectroscopy measurement data is preset, the original spectroscopy measurement data is used as the target spectroscopy measurement data.

[0068] In the case where the original spectral measurement data center does not contain background spectral measurement data, the original spectral measurement data is directly used as the target spectral measurement data.

[0069] In some optional implementations of this embodiment, the execution entity may further perform low-pass filtering on the target spectroscopy measurement data to obtain the target spectroscopy measurement data after noise reduction and smoothing.

[0070] Through the above process, interference factors (background data, noise data, etc.) in the original spectral measurement data can be eliminated, further improving the accuracy of analysis and identification based on the target spectral measurement data.

[0071] In order to deepen understanding, the present invention also provides a specific implementation scheme in combination with a specific application example, see Figures 6A-6DAs shown. In this specific application example, a spectrum containing a main peak and a secondary peak is used as an example for explanation. However, as mentioned above, the method for parameter fitting of spectroscopic measurement data provided in this embodiment can be used to identify overlapping peaks in spectroscopic measurement data (one or more of a spectrum, an energy spectrum, a chromatogram, and a nuclear magnetic resonance spectrum), and is not limited to the spectrum. For the spectrum, the horizontal axis x in the figure represents the wavelength, and the vertical axis y represents the intensity; for the energy spectrum, the horizontal axis x in the figure represents the energy, and the vertical axis y represents the intensity; for the chromatogram, the horizontal axis x in the figure represents the time, and the vertical axis y represents the intensity / concentration; for nuclear magnetic resonance, the horizontal axis x in the figure represents the nuclear magnetic shift, and the vertical axis y represents the intensity.

[0072] Step 1: Collect spectral data (generate spectrum S1), such as Figure 6A As shown, a peak waveform numbered 1 is obtained. In practical applications, the spectral data can also be normalized so that the height of its highest peak is 1. When spectrum S1 includes a background spectrum, spectrum S2 is obtained by subtracting the background spectrum from spectrum S1; if the background spectrum is not included, spectra S1 and S2 are the same.

[0073] Step 2: Obtain the maximum value Xmax=800 and the minimum value Xmin=300 of the value range of the independent variable of spectrum S1 (S2).

[0074] Step 3: Determine the interval parameter interval=50 based on the range, maximum value, and minimum value of the independent variable, and take a point every 50 from 325 to 775 to obtain the interval node set points.

[0075] Step 4: Based on each interval node, as the symmetry center of the Pseudo Voigt function, construct Pseudo Voigt functions respectively, and perform curve fitting on these functions to obtain multiple first candidate fitting functions.

[0076] Step 5: Calculate the first root mean square error RMSE1 between each first candidate fitting function and the target spectroscopy measurement data, and find the first candidate fitting function corresponding to the smallest first root mean square error RMSE1 as the current optimal candidate fitting function.

[0077] Step 6: According to the first root mean square error value rmse_current of the current optimal candidate fitting function is 0.3277, the initial setting of the historical optimal root mean square error value rmse_last is infinity, such as Figure 6A shown.

[0078] Step 7: Since rmse_current = 0.3277 is less than 0.8*rmse_last and does not meet the convergence requirement, set rmse_last = 0.3277 and continue to add the second peak. Based on the current optimal candidate fitting function obtained in step 5, add 10 Pseudo Voigt functions to obtain multiple second candidate fitting functions.

[0079] Step 8: Calculate the second root mean square error RMSE2 between each second candidate fitting function and the target spectroscopy measurement data, and find the second candidate fitting function corresponding to the minimum second root mean square error value as the current optimal candidate fitting function.

[0080] Step 9: At this time, the second root mean square error value rmse_current of the current optimal candidate fitting function is 0.1754, as shown in Figure 6B As shown, the waveform contains peak number 2. The historical optimal root mean square error value rmse_last = 0.3277, the preset threshold is 0.3277 * 0.8 = 0.26216, and rmse_current = 0.1754 is still less than the preset threshold. Therefore, rmse_last = 0.1754 is set to add a third peak. Based on the current optimal candidate fitting function obtained in step 8, 10 Pseudo Voigt functions are added to obtain multiple third candidate fitting functions.

[0081] Step 10: Calculate the third root mean square error RMSE3 between each third candidate fitting function and the target spectroscopy measurement data, and find the third candidate fitting function corresponding to the smallest third root mean square error value as the current optimal candidate fitting function.

[0082] Step 11: At this time, the third root mean square error value rmse_current of the current optimal candidate fitting function is 0.0130, as shown in Figure 6C As shown, the waveform contains peak number 3. The historical optimal root mean square error value rmse_last = 0.1754, the preset threshold is 0.1754*0.8 = 0.14032, and rmse_current = 0.0130 is still less than the preset threshold. Therefore, rmse_last = 0.0130 is set and a fourth peak is added. Based on the current optimal candidate fitting function obtained in step 10, 10 Pseudo Voigt functions are added to obtain multiple fourth candidate fitting functions.

[0083] Step 12: Calculate the fourth root mean square error RMSE4 between each fourth candidate fitting function and the target spectroscopy measurement data, and find the fourth candidate fitting function corresponding to the smallest fourth root mean square error value as the current optimal candidate fitting function.

[0084] Step 13: At this time, the fourth root mean square error value rmse_current of the current optimal candidate fitting function is 0.0112, as shown in Figure 6D As shown, it contains a peak waveform numbered 4. The historical optimal root mean square error value rmse_last = 0.0130, the preset threshold is 0.0130*0.8=0.0104, and rmse_current = 0.0112 is greater than 0.0104, meeting the convergence condition. The current optimal candidate fitting function can be selected as the final target fitting function.

[0085] Further references Figure 7 As an implementation of the methods shown in the above figures, the present invention provides an embodiment of a device for parameter fitting of spectroscopy measurement data. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0086] like Figure 7 As shown, the device 700 for parameter fitting of spectroscopic measurement data in this embodiment may include: a target data acquisition module 701, an interval node determination module 702, a first candidate fitting function determination module 703, a first root mean square error calculation module 704, a current root mean square error calculation module 705 and a target fitting equation determination module 706. Among them, the target data acquisition module 701 is configured to acquire target spectroscopic measurement data; the interval node determination module 702 is configured to determine the interval nodes based on the range of the independent variables in the target spectroscopic measurement data; the first candidate fitting function determination module 703 is configured to use the target spectroscopic measurement data as the target data, and perform curve fitting on the custom functions constructed with each interval node to obtain multiple first candidate fitting functions; the first root mean square error calculation module 704 is configured to calculate the first root mean square error between each first candidate fitting function and the target spectroscopic measurement data; the current root mean square error calculation module 705 is configured to obtain the current root mean square error value of the current optimal fitting function with the smallest first root mean square error; the target fitting equation determination 706 is configured to respond to the current root mean square error value meeting the preset convergence condition, and based on the preset convergence condition, use the corresponding first candidate fitting function as the target fitting equation of the target spectroscopic measurement data.

[0087] In this embodiment, in the apparatus 700 for parameter fitting of spectral measurement data, the specific processing of the target data acquisition module 701, the interval node determination module 702, the first candidate fitting function determination module 703, the first root mean square error calculation module 704, the current root mean square error calculation module 705 and the target fitting equation determination module 706 and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of steps 201-216 in the corresponding embodiment are not repeated here.

[0088] This embodiment exists as an apparatus embodiment corresponding to the above-described method embodiment. The apparatus provided in this embodiment performs parameter fitting on spectroscopic measurement data. By comparing the ratio of the current RMSE to the historical optimal RMSE, it dynamically determines whether further iteration is necessary. Each iteration only adds a new set of candidate peaks and optimizes their parameters, rather than globally adjusting all parameters. This significantly reduces computational complexity. By gradually adding peaks at different locations and optimizing the fit to approximate the data to be fitted, effective fitting of the spectrum to be fitted can be achieved after convergence without the need for manual input of initial parameters, thereby enabling efficient and accurate automated fitting and obtaining relatively accurate information on the positions and intensities of each peak.

[0089] According to an embodiment of the present invention, the present invention also provides a device for performing parameter fitting on spectroscopic measurement data, and the device for performing parameter fitting on spectroscopic measurement data includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that when the at least one processor executes, it can implement the method for performing parameter fitting on spectroscopic measurement data described in any of the above embodiments.

[0090] According to an embodiment of the present invention, the present invention further provides a readable storage medium storing computer instructions, which are used to enable a computer to implement the method for parameter fitting of spectroscopic measurement data described in any of the above embodiments when executed.

[0091] According to an embodiment of the present invention, the present invention further provides a computer program product, which, when executed by a processor, can implement the method for parameter fitting of spectroscopic measurement data described in any of the above embodiments.

[0092] Figure 8 A schematic block diagram of an apparatus 800 for parameter fitting of spectroscopy measurement data is shown, which may be used to implement an example of an embodiment of the present invention. An electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. An electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0093] like Figure 8As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. Computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0094] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0095] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for parameter fitting of spectroscopic measurement data. For example, in some embodiments, the method for parameter fitting of spectroscopic measurement data can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for parameter fitting of spectroscopic measurement data described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured in any other appropriate manner (eg, by means of firmware) to execute the method for performing parameter fitting on spectroscopic measurement data.

[0096] An embodiment of the present invention also provides a thickness measurement method, which is executed by an electronic device such as a computer or server, and specifically includes: using the method of parameter fitting of spectroscopic measurement data described in any of the above embodiments to obtain the main peak peak value and secondary peak peak value of the current frame, and calculating the wafer thickness of the current frame based on the main peak peak value and secondary peak peak value.

[0097] An embodiment of the present invention further provides a thinning control method, which is executed by an electronic device such as a computer or server and specifically comprises: obtaining a wafer thickness in a current frame according to the thickness measurement method described in any of the above embodiments; and stopping wafer thinning when the wafer thickness in the current frame reaches a preset thickness. The preset thickness is a thickness value set according to thinning requirements.

[0098] This embodiment utilizes a precise thickness measurement method to obtain the wafer thickness of the current frame, providing real-time, accurate thickness data for the thinning process. Automatically stopping the thinning operation when the wafer thickness reaches a preset value effectively avoids over-thinning or under-thinning, ensuring that the final wafer thickness precisely meets the expected standard, thereby improving the quality and consistency of the wafer thinning process.

[0099] An embodiment of the present invention further provides a thinning machine for performing thinning processing on a wafer and executing the method described in any of the above embodiments during the thinning process.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for parameter fitting of spectroscopy measurement data, characterized in that: include: Acquiring target spectroscopy measurement data; determining interval nodes based on the range of the independent variable in the target spectroscopy measurement data; Performing curve fitting on the user-defined function constructed with each of the interval nodes to obtain a plurality of first candidate fitting functions; calculating a first root mean square error between each of the first candidate fitting functions and the target spectroscopy measurement data; Obtaining a current root mean square error value of the current best fitting function with the minimum first root mean square error; In response to the current root mean square error value meeting a preset convergence condition, a corresponding first candidate fitting function is used as a target fitting equation for the target spectroscopy measurement data based on the preset convergence condition.

2. The method according to claim 1, characterized in that Also includes: In response to the current root mean square error value not meeting a preset convergence condition, taking the current root mean square error value as a new historical optimal root mean square error value; Taking the target spectroscopy measurement data as target data, curve fitting is performed on the current optimal fitting function and the sum function of each of the user-defined functions to obtain a plurality of second candidate fitting functions; calculating a second root mean square error between each of the second candidate fitting functions and the target spectroscopy measurement data; Replacing the first root mean square error with the second root mean square error, and returning to the step of obtaining the current root mean square error value of the current optimal fitting function with the minimum root mean square error to the step of calculating the second root mean square error between each of the second candidate fitting functions and the target spectroscopy measurement data, until the new current root mean square error value meets the preset convergence condition; Based on the preset convergence condition, the corresponding candidate fitting function is used as the target fitting function of the target spectroscopy measurement data.

3. The method according to claim 1, characterized in that The preset convergence conditions include: The current root mean square error value is greater than or equal to a preset threshold, where the preset threshold is determined based on a historical optimal root mean square error value; or the current root mean square error value is less than a preset absolute standard value.

4. The method according to claim 3, characterized in that The step of using the corresponding candidate fitting function as the target fitting function of the target spectroscopy measurement data based on the preset convergence condition includes: In response to the current root mean square error value being greater than or equal to the preset threshold, taking the candidate fitting function corresponding to the previous root mean square error value as the target fitting function; or, In response to the current root mean square error value being smaller than a preset absolute standard value, the candidate fitting function corresponding to the current root mean square error value is used as the target fitting function.

5. The method according to claim 1, wherein The determining of interval nodes based on the independent variable range in the target spectroscopy measurement data includes: Obtaining the maximum and minimum values of the independent variable range in the target spectroscopy measurement data; The interval node is determined in the independent variable range based on the independent variable range, a maximum value, and a minimum value.

6. The method according to claim 5, characterized in that The determining the interval node in the independent variable range based on the independent variable range, the maximum value, and the minimum value includes: Based on the independent variable range, the maximum value and the minimum value in the independent variable range, the interval nodes therein are determined in a functional distribution, random distribution or average distribution manner.

7. The method according to claim 1, characterized in that The step of performing curve fitting on the user-defined functions constructed using the interval nodes to obtain a plurality of first candidate fitting functions includes: Taking each of the interval nodes as the symmetry center, construct a corresponding custom function; The target spectroscopy measurement data is used as target data, and curve fitting is performed on each of the user-defined functions to obtain the plurality of first candidate fitting functions.

8. The method according to claim 1, characterized in that The obtaining of target spectroscopy measurement data includes: Obtaining raw spectroscopic measurement data; In response to preset background spectroscopy measurement data, performing background removal processing on the original spectroscopy measurement data based on the background spectroscopy measurement data to obtain the target spectroscopy measurement data after background removal; In response to no background spectroscopy measurement data being preset, the original spectroscopy measurement data is used as the target spectroscopy measurement data.

9. A thickness measurement method, characterized in that: include: Obtaining a target fitting function using the method according to any one of claims 1 to 8; Obtaining the main peak value and the secondary peak value of the current frame based on the target fitting function; The wafer thickness of the current frame is calculated according to the main peak-to-peak value and the secondary peak-to-peak value.

10. A thinning control method, characterized in that: include: Obtaining the wafer thickness of the current frame according to the method of claim 9; When the wafer thickness of the current frame reaches a preset thickness, wafer thinning is stopped.

11. A device for performing parameter fitting on spectroscopy measurement data, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the method for parameter fitting of spectroscopic measurement data as described in any one of claims 1 to 8.

12. A thickness measuring device, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the thickness measurement method according to claim 9.

13. A thinning control device, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the thinning control method according to claim 10.

14. A thinning machine, characterized in that: Used to perform thinning processing on a wafer, and perform the method according to any one of claims 1 to 10 during the thinning process.

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