Method for parameter fitting of spectroscopy measurement data, thickness measurement method and apparatus

Abstract

The present invention provides a method for parameter fitting of spectroscopic measurement data, a thickness measurement method, and an apparatus. The method comprises: 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 using each interval node to obtain multiple 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; and, 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. By implementing the present invention, the data to be fitted is approximated by gradually adding peaks at different positions and optimizing the fitting, thereby achieving efficient and accurate automated fitting and obtaining relatively accurate peak position and intensity information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a method for parameter fitting of spectroscopy measurement data, a thickness measurement method and equipment. BACKGROUND

[0002] The accurate fitting of spectroscopy measurement data such as spectrum and energy spectrum is very important for the analysis of position and intensity information of the spectroscopy measurement data in scientific research and detection in production and life, but in related technologies, manual fitting is still the main method. This method has problems such as inaccuracy, low repeatability and low reliability, so it is necessary to develop an automatic spectrum fitting method. SUMMARY

[0003] Therefore, the present application provides a method for parameter fitting of spectroscopy measurement data, a thickness measurement method and equipment.

[0004] In a first aspect, the present application provides a method for parameter fitting of spectroscopy measurement data, comprising: obtaining target spectroscopy measurement data; determining interval nodes based on the range of independent variables in the target spectroscopy measurement data; performing curve fitting on a self-defined function constructed with each interval node to obtain a plurality of first candidate fitting functions; calculating the first root mean square error of each first candidate fitting function and the target spectroscopy measurement data; obtaining the 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 current root mean square error value meeting a preset convergence condition, taking the corresponding first candidate fitting function as the target fitting equation of the target spectroscopy measurement data based on the preset convergence condition.

[0005] In a second aspect, the present application provides a thickness measurement method, which comprises: obtaining a target fitting function by using the method described in any of the implementation manners of the first aspect; obtaining the main peak value and the secondary peak value of the current frame based on the target fitting function; and calculating the wafer thickness of the current frame according to the main peak value and the secondary peak value.

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

[0007] In a fourth aspect, the present application provides a device for parameter fitting of spectroscopy measurement data, which comprises: 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 to enable the at least one processor to implement the method for parameter fitting of spectroscopy measurement data described in any of the implementation manners of the first aspect.

[0008] In a fifth aspect, an embodiment of the present application provides a thickness measurement 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 to enable the processor to perform the thickness measurement method according to the second aspect.

[0009] In a sixth aspect, an embodiment of the present application provides a thinning control 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 to enable the processor to perform the thinning control method according to the third aspect.

[0010] In a seventh aspect, an embodiment of the present application provides a thinning machine for wafer thinning, and performs the method according to any implementation manner of the first aspect, the second aspect or the third aspect during the thinning process.

[0011] The method for fitting parameters of spectroscopy measurement data, the thickness measurement method and the device provided by the embodiments of the present application can significantly reduce the calculation complexity by comparing the ratio of the current RMSE and the historical optimal RMSE, dynamically determining whether to continue iteration, and adding only one set of candidate peaks and optimizing the parameters of the candidate peaks each time instead of globally adjusting all parameters. By gradually adding peaks at different positions and optimizing the fitting, the effective fitting of the to-be-fitted spectrum can be realized after convergence, and the initial parameters do not need to be manually input, so that efficient and accurate automatic fitting can be realized, and more accurate peak position and intensity information can be obtained.

[0012] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0014] Figure 1 is an exemplary system architecture to which the present application can be applied;

[0015] Figure 2 is a flowchart of a method for fitting parameters of spectroscopy measurement data provided by an embodiment of the present application.

[0016] Figure 3 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6;

[0017] Figure 4 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6;

[0018] Figure 5 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6;

[0019] Figures 6A-6D A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6;

[0020] Figure 7 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6;

[0021] Figure 8 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 6; DETAILED DESCRIPTION

[0022] The technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0023] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0024] In the description of the present application, it should be noted that unless specifically defined and limited otherwise, the terms "mount", "connected", "connection" should be interpreted broadly, for example, can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, can be electrically connected; can be directly connected, can be indirectly connected through an intermediate medium, can be internal communication of two elements, can be wireless connection, or can be wired connection. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0025] In addition, the technical features involved in the different embodiments of the application described below can be combined with each other as long as there is no conflict.

[0026] Figure 1 An exemplary system architecture 100 is shown, which can apply the embodiments of the method and device for parameter fitting of spectroscopy measurement data of the present application.

[0027] As Figure 1 shown, the system architecture 100 can include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a communication link medium between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0028] The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. The terminal devices 101, 102, 103 and the server 105 can be installed with various applications for realizing information communication between them, such as instant messaging applications, etc.

[0029] The terminal devices 101, 102, 103 and the server 105 can be hardware or software. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the above-mentioned electronic devices, which can be implemented as multiple software or software modules, or as a single software or software module, which is not specifically limited here. When the server 105 is hardware, it can be implemented as a distributed server cluster composed 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, which is not specifically limited here.

[0030] The server 105 can provide various services through various applications built-in. It should be noted that the data or information required to provide the services can be obtained from the terminal devices 101, 102, 103 through the network 104, or can be pre-stored in the server 105 locally through various ways. Therefore, when the server 105 detects that the data is already stored locally, the server 105 can choose to obtain the data directly from the local storage. In this case, the exemplary system architecture 100 can also not include the terminal devices 101, 102, 103 and the network 104.

[0031] Since the data analysis process and the like can require a large amount of computing resources and strong computing power, the method for fitting parameters to spectroscopy measurement data provided by the subsequent embodiments of the present application is generally executed by the server 105 with strong computing power and a large amount of computing resources. Correspondingly, the device for fitting parameters to spectroscopy measurement data is generally provided in the server 105. However, it should also be noted that when the terminal devices 101, 102, 103 also have computing power and computing resources that meet the requirements, the terminal devices 101, 102, 103 can also complete the above-mentioned operations by the server 105 through the related applications installed thereon, and then output the same results as the server 105. Especially in the case where there are multiple terminal devices with different computing power, but the related application determines that the terminal device has strong computing power and a large amount of remaining computing resources, the terminal device can be allowed to execute the above-mentioned operations, thereby appropriately reducing the computing pressure of the server 105. Correspondingly, the device for fitting parameters to spectroscopy measurement data can also be provided in the terminal devices 101, 102, 103. In this case, the exemplary system architecture 100 can also not include the server 105 and the network 104.

[0032] It should be understood that Figure 1 The number of terminal devices, networks and servers in the exemplary system architecture 100 is only illustrative. According to the needs of implementation, there can be any number of terminal devices, networks and servers.

[0033] Reference can be made to Figure 2 , Figure 2 A flowchart of a method for fitting parameters to spectroscopy measurement data provided by an embodiment of the present application is shown in FIG. 2, wherein the flow 200 includes the following steps:

[0034] Step 201: Obtain target spectroscopy measurement data.

[0035] This step is intended to obtain the target spectroscopy measurement data by the execution subject of the method for fitting parameters to spectroscopy measurement data (for example, the server 105 or the terminal device 101, 102, 103). Figure 1The server 105 shown) obtains corresponding target spectroscopy measurement data. In this embodiment, the target spectroscopy measurement data can include at least one or more of a spectrum, an energy spectrum, a color spectrum, and a nuclear magnetic resonance spectrum (for example, ultraviolet-visible-near infrared spectrum, X-ray photoelectron spectroscopy (XPS spectrum), solid nuclear magnetic resonance spectrum, and the like), and measurement data generated by various spectroscopy analysis methods can be obtained. Among them, the target spectroscopy measurement data contains data of overlapping peaks, including a main peak and a second peak.

[0036] Step 202: Determine interval nodes based on the independent variable range of the target spectroscopy measurement data.

[0037] 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 independent variable range is divided into multiple value intervals, and each value interval is separated by an interval node.

[0038] Step 203: Curve fitting is performed on the custom function constructed by each interval node to obtain multiple first candidate fitting functions.

[0039] In this embodiment, the execution subject can construct a corresponding custom function based on the independent variable of the above interval node as the symmetry center, and perform curve fitting on the custom function to obtain multiple first candidate fitting functions.

[0040] Step 204: Calculate the first root mean square error of each first candidate fitting function and the target spectroscopy measurement data.

[0041] In this embodiment, the first root mean square error of each first candidate fitting function and the target spectroscopy measurement data is calculated. Root mean square error (RMSE) is a core index for quantifying the deviation of the candidate fitting function from the target spectroscopy measurement data. For example, considering the distribution rule of the spectrum and the energy spectrum under normal circumstances, a pseudo Voigt function can be used for fitting, which contains four parameters of the peak and the valley: position, height, width, and Gaussian function ratio. The pseudo Voigt function equation obtained after fitting is set as F1, and the four parameters are set as p1.

[0042] In some optional embodiments of this embodiment, the pseudo Voigt function has the following form:

[0043] ,

[0044] wherein Intensity represents the intensity at any wavelength in the target spectroscopy measurement data, x represents the wavelength independent variable in the target spectroscopy measurement data, amplitude represents the height of the function, frac represents the proportion of the Gaussian function in the pseudo Voigt function (the 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 the coefficients are not less than 0), center1 represents the position of the main peak or the center of symmetry, and std is the coefficient determining the width of the Gaussian function or the Cauchy function.

[0045] Step 205: obtaining the current root mean square error value of the current optimal fitting function with the minimum first root mean square error.

[0046] This step aims to find the candidate fitting function corresponding to the minimum first root mean square error value by the above execution subject as the current optimal fitting function, and record the current root mean square error value rmse_current of the current optimal fitting function.

[0047] Step 206: in response to the current root mean square error value meeting the preset convergence condition, taking the corresponding first candidate fitting function as the target fitting equation of the target spectroscopy measurement data based on the preset convergence condition.

[0048] In this embodiment, the execution subject judges whether the current root mean square error value meets the preset convergence condition. In this embodiment, there are two standards for the convergence judgment of RMSE, one absolute standard and one relative standard; the absolute standard is that a threshold value of 0.01 is set, and the iteration is directly considered to be completed after a certain iteration if the threshold value is less than the threshold value; the relative standard is a certain threshold value of the last RMSE, such as 80%, if the RMSE after a certain iteration is not lower than the set threshold value of the last RMSE, such as 80%, the iteration is stopped, the current iteration data is discarded, and the result of the last iteration is used as the final result. If the absolute standard cannot be met all the time, the relative standard can also be used to avoid infinite iteration, and the overfitting situation is also avoided, and a new peak is only recognized when the new peak can obviously reduce the RMSE. Exemplarily, the preset convergence condition can include: the current root mean square error value is greater than or equal to a preset threshold value, and the preset threshold value 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 a preset absolute standard value. In specific implementation, since the initial value of the preset threshold value can be set to infinity, it can be ensured that the first time will enter the subsequent iteration optimization process. The preset threshold value can be set to the historical optimal root mean square error value multiplied by a preset proportion, 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 root mean square error value are both for illustration, and can be adjusted according to actual application scenarios, and the present application is not limited thereto.

[0049] The method for parameter fitting of spectroscopy measurement data provided by the embodiment can effectively fit the to-be-fitted spectrum after convergence, without the need for manual input of initial parameters, thereby enabling efficient and accurate automatic fitting and obtaining more accurate peak position and intensity information.

[0050] Reference is made to Figure 3 , Figure 3 The flowchart of another method for parameter fitting of spectroscopy measurement data provided by the embodiment of the present disclosure, wherein the flowchart 300 includes the following steps:

[0051] Step 301: Obtain target spectroscopy measurement data.

[0052] Step 302: Determine interval nodes based on the independent variable range in the target spectroscopy measurement data.

[0053] Step 303: Perform curve fitting on the custom functions constructed with the interval nodes to obtain a plurality of first candidate fitting functions.

[0054] Step 304: Calculate the first root mean square error of each first candidate fitting function and the target spectroscopy measurement data.

[0055] Step 305: Obtain the current root mean square error value of the current optimal fitting function with the minimum first root mean square error.

[0056] The above steps 301-305 are consistent with steps 201-205 as shown in Figure 2 The same part of the content is described in the corresponding part of the previous embodiment, and will not be described here.

[0057] Step 306: In response to the current root mean square error value not meeting the preset convergence condition, the current root mean square error is taken as a new historical optimal root mean square error value. If the current root mean square error value meets the preset convergence condition, the current optimal fitting function is determined as the target fitting function of the target spectroscopy measurement data.

[0058] Step 307: Perform curve fitting on the sum function of the current optimal fitting function and each custom function with the target spectroscopy measurement data as the target data to obtain a plurality of second candidate fitting functions.

[0059] In this step, the execution subject adds the sum function of the plurality of custom functions obtained in the previous step to the current optimal fitting function obtained to perform curve fitting with the target spectroscopy measurement data as the target data to obtain a plurality of second candidate fitting functions.

[0060] Step 308: Calculate the second root mean square error between each second candidate fitting function and the target spectroscopy measurement data.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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:

[0068] Step 401: Obtain the maximum value and the minimum value of the independent variable range in the target spectroscopy measurement data.

[0069] 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.

[0070] Step 402: Determine interval nodes in the independent variable range based on the independent variable range, the maximum value and the minimum value.

[0071] In this step, the execution subject determines interval parameters based on the independent variable range, the maximum value Xmax and the minimum value Xmin. The interval parameters can be the difference between the maximum value Xmax and the minimum value Xmin, and then divided by a first number. The first number is a preset peak number of the target spectroscopy measurement data. For example, for the target spectroscopy measurement data with a preset peak number of 10, the interval parameter interval=(Xmax-Xmin) / 10.

[0072] In this embodiment, interval nodes in the independent variable range are determined in a manner of function distribution, random distribution or average distribution based on the independent variable range, the maximum value and the minimum value.

[0073] For example, the independent variable range can be divided into (first number+1) interval intervals, the length of the first interval interval and the last interval interval is half of the length of the remaining interval intervals, and the nodes of the divided interval intervals are interval nodes. For example, for the target spectroscopy measurement data with a preset peak number of 10, the independent variable range can be divided into 11 parts in a manner of 1*0.5+9*1+1*0.5, wherein the length of the first part and the length of the last part is 0.5 times interval, and the length of the other parts is 1 times interval. The points between the parts are interval nodes point, and the set of points is interval node set points. Alternatively, the independent variable range can be distributed according to a preset function (such as a mathematical function (such as a linear function, a probability density function), a user-defined function (such as a rule defined in programming) or a distribution function) to obtain corresponding interval nodes. Alternatively, the independent variable range can be distributed in a random distribution manner to obtain corresponding interval nodes. Alternatively, the specific distribution manner can be set according to the actual application scene, and the present application is not limited thereto.

[0074] 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.

[0075] 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 2 Step 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:

[0076] Step 501: Acquire original spectroscopy measurement data.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] In order to deepen understanding, the present invention also provides a specific implementation scheme in combination with a specific application example, see Figures 6A-6D As 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] Step five: calculate the first root mean square error value RMSE1 of each first candidate fitting function and the target spectroscopy measurement data, and find the first candidate fitting function corresponding to the minimum first root mean square error value RMSE1 as the current optimal candidate fitting function.

[0090] Step six: 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 infinite, as shown in the following formula: Figure 6A

[0091] Step seven: since the rmse_current = 0.3277 is less than 0.8*rmse_last, the convergence requirement is not met, and rmse_last = 0.3277 is set to continue adding the second peak. On the basis of the current optimal candidate fitting function obtained in step five, 10 Pseudo Voigt functions are added respectively to obtain a plurality of second candidate fitting functions.

[0092] Step eight: calculate the second root mean square error value RMSE2 of 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.

[0093] Step nine: 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 the following formula: Figure 6B , which contains the peak waveform numbered 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, so rmse_last = 0.1754 is set to add the third peak, and find the current optimal candidate fitting function obtained in step eight, add 10 Pseudo Voigt functions respectively to obtain a plurality of third candidate fitting functions.

[0094] Step ten: calculate the third root mean square error value RMSE3 of each third candidate fitting function and the target spectroscopy measurement data, and find the third candidate fitting function corresponding to the minimum third root mean square error value as the current optimal candidate fitting function.

[0095] Step eleven: 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 the following formula: Figure 6C ​As shown in FIG. 10, the peak waveform containing the fourth peak is added. 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, satisfying the convergence condition. Therefore, the current optimal pseudo Voigt function can be selected as the final target fitting function.

[0096] Step twelve: calculate the fourth root mean square error value RMSE4 of each fourth candidate fitting function and the target spectroscopy measurement data, and find the fourth candidate fitting function corresponding to the minimum fourth root mean square error value as the current optimal candidate fitting function.

[0097] Step thirteen: 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 FIG. 11. Figure 6D As shown in FIG. 11, the peak waveform containing the fourth peak is added. 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, satisfying the convergence condition. Therefore, the current optimal pseudo Voigt function can be selected as the final target fitting function.

[0098] Further referring to Figure 7 , as an implementation of the method shown in the above figures, the present application provides an embodiment of a device for parameter fitting of spectroscopy measurement data. The device embodiment corresponds to the method embodiment shown in Figure 2 , and the device can be specifically applied to various electronic devices.

[0099] As shown in FIG. 12, the peak waveform containing the fourth peak is added. 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, satisfying the convergence condition. Therefore, the current optimal pseudo Voigt function can be selected as the final target fitting function. Figure 7As shown, the device 700 for parameter fitting of spectroscopy measurement data in this embodiment can 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. The target data acquisition module 701 is configured to acquire target spectroscopy measurement data. The interval node determination module 702 is configured to determine interval nodes based on the range of independent variables in the target spectroscopy measurement data. The first candidate fitting function determination module 703 is configured to perform curve fitting on self-defined functions constructed with each interval node, respectively, with the target spectroscopy measurement data as target data, to obtain a plurality of first candidate fitting functions. The first root mean square error calculation module 704 is configured to calculate the first root mean square error of each first candidate fitting function and the target spectroscopy measurement data. The current root mean square error calculation module 705 is configured to acquire 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 module 706 is configured to, in response to the current root mean square error value meeting a preset convergence condition, determine the corresponding first candidate fitting function as the target fitting equation of the target spectroscopy measurement data based on the preset convergence condition.

[0100] In this embodiment, in the device 700 for parameter fitting of spectroscopy 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 brought by the specific processing can be respectively referred to Figure 2 The related description of steps 201-216 in the corresponding embodiment will not be repeated here.

[0101] This embodiment exists as a device embodiment corresponding to the above-mentioned method embodiment. The device for parameter fitting of spectroscopy measurement data provided in this embodiment can dynamically determine whether iteration needs to be continued by comparing the ratio of the current RMSE and the historical optimal RMSE, and can significantly reduce the computational complexity by only adding one group of candidate peaks and optimizing the parameters of the group of candidate peaks each time instead of globally adjusting all parameters. The effective fitting of the to-be-fitted spectrum can be realized after convergence by gradually adding peaks at different positions and optimizing the fitting, and the initial parameters do not need to be manually input, so that efficient, accurate and automated fitting can be realized, and more accurate peak position and intensity information can be obtained.

[0102] According to an embodiment of the present application, the present application further provides an apparatus for parameter fitting of spectroscopy measurement data, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to implement the method for parameter fitting of spectroscopy measurement data described in any of the above embodiments.

[0103] According to an embodiment of the present application, the present application further provides a readable storage medium storing computer instructions for enabling a computer to implement the method for parameter fitting of spectroscopy measurement data described in any of the above embodiments when the computer executes the computer instructions.

[0104] According to an embodiment of the present application, the present application further provides a computer program product, which, when executed by a processor, can implement the method for parameter fitting of spectroscopy measurement data described in any of the above embodiments.

[0105] Figure 8 An illustrative block diagram of an example apparatus 800 for parameter fitting of spectroscopy measurement data that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0106] As shown in Figure 8 The apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with 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. Various programs and data required for the operation of the apparatus 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0107] A plurality of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0108] The computing unit 801 can be various general-purpose and / or special-purpose processing components 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 special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs various methods and processes described above, such as the method of parameter fitting of spectroscopy measurement data. For example, in some embodiments, the method of parameter fitting of spectroscopy measurement data can be implemented as a computer software program, which is tangibly contained 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 on 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 of parameter fitting of spectroscopy measurement data described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the method of parameter fitting of spectroscopy measurement data by any other appropriate means, such as by means of firmware.

[0109] The embodiments of the present application also provide a thickness measurement method, which is performed by an electronic device such as a computer or a server, and specifically includes: obtaining a main peak peak value and a secondary peak peak value of a current frame by using the method of parameter fitting of spectroscopy measurement data described in any of the above embodiments, and calculating a wafer thickness of the current frame according to the main peak peak value and the secondary peak peak value.

[0110] The embodiments of the present application also provide a thinning control method, which is performed by an electronic device such as a computer or a server, and specifically includes: obtaining a wafer thickness of a current frame according to the thickness measurement method described in any of the above embodiments; and stopping wafer thinning when the wafer thickness of the current frame reaches a preset thickness. The preset thickness is a thickness value set according to a thinning requirement.

[0111] The embodiment obtains the wafer thickness of the current frame by means of the accurate thickness measurement method, can provide real-time and accurate thickness data for the thinning process, automatically stops the thinning operation when the wafer thickness reaches the preset value, can effectively avoid the problems of excessive thinning or insufficient thinning, ensures that the final wafer thickness is accurate and meets the expected standard, and improves the quality and consistency of wafer thinning processing.

[0112] The embodiment of the present application also provides a thinning machine for performing the method described in any of the above embodiments during wafer thinning processing.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0114] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for performing the functions specified in one or more blocks.

[0115] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The means for performing the functions specified in one or more blocks.

[0116] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 Figure 1steps of the functions specified in the one or more blocks.

[0117] Obviously, the above-mentioned embodiments are only examples for clearly illustrating the present application, but not limitation on the embodiments. Based on the above-mentioned description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be enumerated. The changes or variations derived from the above-mentioned embodiments are still within the protection scope of the present application.

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 a range of independent variables 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, using the corresponding first candidate fitting function as a target fitting equation for the target spectroscopy measurement data based on the preset convergence condition; The step of performing curve fitting on the user-defined functions constructed with the interval nodes to obtain a plurality of first candidate fitting functions includes: Taking each of the interval nodes as the symmetry center, a corresponding custom function is constructed; the custom function is a Pseudo Voigt 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.

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 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.

8. A thickness measurement method, characterized in that: include: Obtaining a target fitting function using the method according to any one of claims 1 to 7; 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.

9. A thinning control method, characterized in that: include: Obtaining the wafer thickness of the current frame according to the method of claim 8; When the wafer thickness of the current frame reaches a preset thickness, wafer thinning is stopped.

10. 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 7.

11. 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 8.

12. 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 9.

13. 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 9 during the thinning process.

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