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

Abstract

The invention provides a method for performing parameter fitting on spectroscopic measurement data, and a wafer data processing method and equipment. The method comprises the following steps: acquiring target spectroscopic measurement data; performing curve fitting based on the target spectroscopic measurement data to obtain a first fitting equation; calculating a residual error between the first fitting equation and the target spectroscopic measurement data; judging whether a trough exists in the residual error or not; if a trough exists in the residual error, adding a first preset peak to the first fitting equation to obtain a new first fitting equation, and returning to carry out iteration to obtain a candidate fitting equation; calculating a first root mean square error of the candidate fitting equation and the target spectroscopic measurement data; judging whether the first root mean square error meets a preset convergence condition or not; and if the first root mean square error does not meet the preset convergence condition, adding a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation, returning to carry out iteration, and taking the corresponding candidate fitting equation as a target fitting equation of the target spectroscopic measurement data based on the preset convergence condition.

Description

TECHNICAL FIELD

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

[0002] In the related art, it is very important to accurately analyze spectroscopy measurement data such as spectrum and energy spectrum collected in scientific research and production. Accurate analysis can extract useful information to guide subsequent scientific research and production. However, in the related art, manual fitting of multi-peak spectrum has problems such as high subjectivity, high influence of random factors, and low success rate. SUMMARY

[0003] Therefore, the present application provides a method for parameter fitting of spectroscopy measurement data, a wafer data processing 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; performing curve fitting based on the target spectroscopy measurement data to obtain a first fitting equation; calculating the residual error of the first fitting equation and the target spectroscopy measurement data; adding a first preset peak to the first fitting equation according to the position and amplitude of the trough in the residual error to obtain a candidate fitting equation; calculating the first root mean square error of the candidate fitting equation and the target spectroscopy measurement data; determining whether the first root mean square error meets a preset convergence condition; if the first root mean square error does not meet the preset convergence condition, adding a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation; returning to the step of calculating the first root mean square error of the candidate fitting equation and the target spectroscopy measurement data to the step of determining whether the first root mean square error meets the preset convergence condition until the last obtained first root mean square error meets the preset convergence condition; and taking the corresponding candidate fitting equation as a 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 wafer data processing method, comprising: obtaining a target fitting equation by using the method described in any of the implementations of the first aspect; obtaining the main peak value and the secondary peak value of the current frame based on the target fitting equation; 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, comprising: 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 application provides a device for parameter fitting of spectroscopy 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 to enable the at least one processor to implement the method for parameter fitting of spectroscopy measurement data as described in any implementation manner of the first aspect.

[0008] In a fifth aspect, an embodiment of the present application provides a wafer data processing device, the 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 implement the wafer data processing method as described in the second aspect.

[0009] In a sixth aspect, an embodiment of the present application provides a thinning control device, the 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 implement the thinning control method as described in the third aspect.

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

[0011] The method for parameter fitting of spectroscopy measurement data, the wafer data processing method and the device provided by the embodiments of the present application can effectively avoid random errors and subjective bias caused by manual intervention through automatic implementation of the overall process. Through iterative optimization and dynamic peak adding process, the accuracy can be significantly higher than that of traditional manual fitting, 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 is it used to limit the scope of the present application. Other features of the present application will become apparent through 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 in the following description 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 1is an exemplary system architecture to which the present application can be applied; Figure 2 A flow chart of a method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application; Figure 3 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application; Figure 4 A flow chart of another method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application; Figures 5A-5C A schematic diagram of a method for parameter fitting of spectroscopy measurement data in an application scenario provided by an embodiment of the present application; Figure 6 A structural block diagram of an apparatus for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application; Figure 7 A structural schematic diagram of an electronic device suitable for executing a method for parameter fitting of spectroscopy measurement data provided by an embodiment of the present application. DETAILED DESCRIPTION

[0015] 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 a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in 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.

[0016] In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the orientations or positional relationships 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 devices or elements 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.

[0017] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium, or internal communication of two elements, it can be wireless connection, or 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.

[0018] Furthermore, 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, illustrating an embodiment of the method and apparatus for parameter fitting of spectroscopic measurement data to which the present invention can be applied.

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

[0021] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include instant messaging applications.

[0022] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

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

[0024] Since data analysis and processing may require significant computing resources and capabilities, the parameter fitting methods for spectroscopic measurement data provided in subsequent embodiments of this invention are generally executed by a server 105 with strong computing power and abundant computing resources. Correspondingly, the device for parameter fitting of spectroscopic measurement data is also generally located in the server 105. However, it should also be noted that when terminal devices 101, 102, and 103 also possess sufficient computing power and resources, they can also perform the aforementioned calculations performed by the server 105 through their installed applications, thereby outputting the same results as the server 105. Especially when multiple terminal devices with different computing capabilities exist simultaneously, but the relevant application determines that the terminal device has strong computing power and abundant remaining computing resources, the terminal device can perform the aforementioned calculations, thereby appropriately reducing the computing load on the server 105. Accordingly, the device for parameter fitting of spectroscopic measurement data can also be located in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also exclude the server 105 and the network 104.

[0025] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

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

[0027] This step aims to be performed by the entity that performs the parameter fitting method on the spectroscopic measurement data (e.g., Figure 1 The server 105 shown acquires the corresponding target spectroscopic measurement data. In this embodiment, the target spectroscopic measurement data may include at least one or more of the following: spectra, energy spectra, chromatograms, and nuclear magnetic resonance spectra (e.g., ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy (XPS spectroscopy), solid-state nuclear magnetic resonance spectroscopy, etc.), and can acquire measurement data generated by various spectroscopic analysis methods. The target spectroscopic measurement data includes data on overlapping peaks, which include a main peak and a second peak.

[0028] Step 202: Perform curve fitting based on the target spectroscopic measurement data to obtain the first fitting equation.

[0029] In this step, the execution subject constructs a self-defined function equation based on the target spectroscopy measurement data, and performs curve fitting based on the self-defined function equation and the target spectroscopy measurement data to obtain a first fitting equation.

[0030] Exemplarily, considering the distribution rule of spectrum, energy spectrum and the like in general cases, a Pseudo Voigt function can be used as the self-defined function equation, and curve fitting is performed. The Pseudo Voigt function contains four parameters of peaks and valleys: position, height, width and Gaussian function proportion. The Pseudo Voigt function equation obtained after fitting is set as F1, and the four parameters are set as p1.

[0031] In some optional embodiments of the present embodiment, the Pseudo Voigt function has the form of: , wherein 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 a coefficient that determines the width of the Gaussian function or the Cauchy function.

[0032] Step 203: Calculate the residual error of the first fitting equation and the target spectroscopy measurement data.

[0033] In the present embodiment, the residual error quantifies the deviation of the first fitting equation from the target spectroscopy measurement data at each data point.

[0034] Step 204: Add a first preset peak to the first fitting equation according to the position and amplitude of the trough in the residual error to obtain a candidate fitting equation.

[0035] Step 205: Calculate the first root mean square error of the candidate fitting equation and the target spectroscopy measurement data.

[0036] In the present embodiment, the first root mean square error of the first fitting equation and the target spectroscopy measurement data is calculated. The root mean square error (RMSE) is a core index for quantifying the deviation of the candidate fitting equation from the target spectroscopy measurement data.

[0037] Step 206: Determine whether the first root mean square error meets a preset convergence condition.

[0038] In this embodiment, the convergence judgment of the RMSE has two criteria, an absolute criterion and a relative criterion; the absolute criterion may be, for example, a threshold value of 0.01, and after a certain iteration, if the threshold value is less than this threshold value, it is directly considered that the iteration is completed; the relative criterion 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 criterion cannot be met all the time, the relative criterion can also be used to avoid infinite iteration, and the overfitting situation is also avoided, and a new peak is only recognized when it can significantly reduce the RMSE.

[0039] Step 207: If the first root mean square error does not meet the preset convergence condition, a second preset peak is added to the candidate fitting equation to obtain a new candidate fitting equation, and steps 206-207 are executed until the first root mean square error obtained last time meets the preset convergence condition.

[0040] For example, the current first root mean square error and the last first root mean square error can be compared by the above execution subject to determine whether the ratio of the current first root mean square error to the last first root mean square error is less than a first preset threshold value. If the ratio of the current first root mean square error to the last first root mean square error is less than the first preset threshold value, a second preset peak is added to the candidate fitting equation, and steps 205-206 are iterated until the ratio of the first root mean square error obtained last time to the last first root mean square error is greater than or equal to the first preset threshold value.

[0041] Alternatively, the execution subject can also determine whether the first root mean square error is less than a preset absolute standard value, and if the first root mean square error is less than the preset absolute standard value, it is considered that the convergence condition is met, and the iteration is ended.

[0042] Step 208: Based on the preset convergence condition, the corresponding candidate fitting equation is taken as the target fitting equation of the target spectroscopy measurement data.

[0043] In this embodiment, the process of determining the target fitting equation is correspondingly set for different preset convergence conditions. For example, in response to the first root mean square error obtained last time being greater than or equal to the preset threshold value, the candidate fitting equation corresponding to the last first root mean square error is taken as the target fitting equation of the target spectroscopy measurement data; or, in response to the first root mean square error obtained last time being less than the preset absolute standard value, the candidate fitting equation corresponding to the first root mean square error obtained last time is taken as the target fitting equation.

[0044] It should be noted that the specific values of the preset threshold value and the preset absolute standard value can be adjusted according to the actual application scene, and the present application does not make special limitations.

[0045] The method for parameter fitting of spectroscopic measurement data provided in this embodiment is fully automated, effectively avoiding random errors and subjective biases introduced by manual intervention. Through iterative optimization and dynamic peak addition, it achieves significantly higher accuracy than traditional manual fitting, thus enabling efficient and accurate automated fitting and obtaining more accurate peak position and intensity information.

[0046] Please refer to Figure 3 , Figure 3 A flowchart of another method for parameter fitting of spectroscopic measurement data provided in an embodiment of the present invention, wherein process 300 includes the following steps: Step 301: Obtain target spectroscopic measurement data.

[0047] Step 302: Perform curve fitting based on the target spectroscopic measurement data to obtain the first fitting equation.

[0048] Step 303: Calculate the residual between the first fitting equation and the target spectroscopic measurement data.

[0049] Step 304: Add a first preset peak to the first fitting equation based on the position and amplitude of the trough in the residual to obtain the candidate fitting equation.

[0050] Step 305: Calculate the first root mean square error between the candidate fitting equation and the target spectroscopic measurement data.

[0051] Step 306: Determine whether the first root mean square error meets the preset convergence condition.

[0052] Step 307: If the first root mean square error does not meet the preset convergence condition, add a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation, and return to execute steps 305-306 until the last obtained first root mean square error meets the preset convergence condition.

[0053] Steps 301-307 above are similar to... Figure 2 The steps 201-207 shown are the same. For the same parts, please refer to the corresponding parts of the previous embodiment. They will not be repeated here.

[0054] Step 308: In response to the first root mean square error obtained last time being greater than or equal to the first preset threshold, the range of independent variables of the target spectroscopic measurement data is divided to obtain multiple division intervals and division boundary points.

[0055] If the last obtained first root mean square error is greater than or equal to the first preset threshold, the independent variable range of the target spectroscopy measurement data is divided to obtain a plurality of division intervals and division junctions. For example, the independent variable range can be divided into 11 parts according to the minimum value Xmin and the maximum value Xmax of the independent variable, wherein the interval width of the first part and the last part is (Xmax-Xmin) / 20; and the interval width of the other parts is (Xmax-Xmin) / 10.

[0056] Step 309: For each division junction, a third preset peak is added to the last obtained first root mean square error to obtain a junction equation.

[0057] For each division junction, a third preset peak is added to the last obtained first root mean square error to obtain a junction equation. For example, in the above process, a small peak is added to each of the 10 junctions of the 11 divided intervals on the basis of the equation with the smallest RMSE obtained before, the position parameter is the x value of the junction, the height parameter is set to 10% of the height of the maximum peak, and the width and shape parameters are determined by the peak width parameter pwidth and the Gaussian proportion gfrac. The peak width parameter and the Gaussian proportion can be determined by the self-defined parameter equation.

[0058] Step 310: Calculate the second root mean square error of each junction equation and the target spectroscopy measurement data, and determine the minimum second root mean square error.

[0059] The second root mean square error of the junction equation and the target spectroscopy measurement data is calculated, and the minimum second root mean square error is found.

[0060] Step 311: Return to execute the step of dividing the independent variable range of the target spectroscopy measurement data to obtain a plurality of division intervals and division junctions, and the step of calculating the second root mean square error of each junction equation and the target spectroscopy measurement data, and determining the minimum second root mean square error, until the new minimum second root mean square error is reduced by less than a preset proportion compared with the last minimum second root mean square error.

[0061] Steps 309-311 are iteratively executed until the newly calculated minimum second root mean square error no longer significantly decreases (for example, the minimum second root mean square error calculated each time is less than 80% of the minimum second root mean square error calculated last time).

[0062] Step 312: The junction equation corresponding to the last minimum second root mean square error with a reduction greater than or equal to the preset proportion is determined as the target fitting equation of the target spectroscopy measurement data.

[0063] The boundary point equation corresponding to the minimum second root mean square error whose last reduction magnitude is greater than or equal to the preset ratio is determined as the target fitting equation for the target spectroscopic measurement data.

[0064] The method for parameter fitting of spectroscopic measurement data provided in this embodiment is fully automated, effectively avoiding random errors and subjective biases introduced by manual intervention. Through two iterative optimizations and a dynamic peak-addition process, it achieves significantly higher accuracy than traditional manual fitting, thus enabling efficient and accurate automated fitting and obtaining more accurate peak position and intensity information.

[0065] Please refer to Figure 4 , Figure 4 A flowchart illustrating a method for parameter fitting of spectroscopic measurement data provided in this disclosure embodiment, namely for... Figure 2 Step 201 in process 200 provided a specific implementation. Other steps in process 200 are not adjusted; a new complete embodiment is obtained by replacing step 201 with the specific implementation provided in this embodiment. Process 400 includes the following steps: Step 401: Obtain raw spectroscopic measurement data.

[0066] The raw spectroscopic measurement data can be one or more of the following: spectral, energy-dispersive X-ray spectroscopy, chromatographic, and nuclear magnetic resonance spectroscopy. This raw data may contain noise and background data. In this embodiment, the noise and background data in the raw data can be processed.

[0067] Step 402: In response to the preset background spectroscopic measurement data, the original spectroscopic measurement data is processed to remove the background based on the background spectroscopic measurement data to obtain the target spectroscopic measurement data after background removal.

[0068] If the original spectroscopic measurement data contains background spectroscopic measurement data, the original spectroscopic measurement data can be processed to remove the background (for example, by subtracting the background spectroscopic measurement data from the original spectroscopic measurement data) to obtain the target spectroscopic measurement data after background removal.

[0069] Step 403: In response to the absence of preset background spectroscopic measurement data, the original spectroscopic measurement data is used as the target spectroscopic measurement data.

[0070] If the original spectroscopic measurement data center does not contain background spectroscopic measurement data, then the original spectroscopic measurement data is directly used as the target spectroscopic measurement data.

[0071] In some optional embodiments of this example, the executing entity may further perform low-pass filtering on the target spectroscopic measurement data to obtain noise-reduced and smoothed target spectroscopic measurement data.

[0072] Through the above process, interference factors (background data, noise data, etc.) in the original spectroscopy measurement data can be excluded, and the accuracy of analysis and identification based on the target spectroscopy measurement data is further improved.

[0073] In some optional embodiments of the present application, the process of step 202, curve fitting based on the target spectroscopy measurement data to obtain the first fitting equation, mainly includes: constructing a user-defined function equation based on the target spectroscopy measurement data, and performing curve fitting based on the user-defined function equation and the target spectroscopy measurement data to obtain the first fitting equation. Wherein, constructing a user-defined parameter equation based on the target spectroscopy measurement data includes: Step one: peak detection is performed on the target spectroscopy measurement data to obtain a first peak set, and the first peak set includes: a number set, a position set and a height set. Peak detection (find_peak) is performed on the target spectroscopy measurement data to obtain a series of peaks, the number of obtained peaks is set as the number set npeaks, the position set of the peaks is set as listX, and the height set of the peaks is set as listY.

[0074] Step two: determine the peak width parameter and the Gaussian proportion based on the independent variable range of the target spectroscopy measurement data.

[0075] Exemplarily, the maximum value of the independent variable range of the target spectroscopy measurement data is Xmax, and the minimum value is Xmin, then the default peak width parameter pwidth=0.05*(Xmax-Xmin), and the default Gaussian proportion gfrac is 0.5.

[0076] Step three: construct a user-defined parameter equation based on the number set, the position set, the height set, the peak width parameter and the Gaussian proportion.

[0077] Exemplarily, a user-defined parameter equation f1 can be constructed according to npeaks, listX, listY, pwidth and gfrac, which is the sum of npeaks Pseudo Voigt functions, wherein the four parameters of Pseudo Voigt are determined by the position value of listX, the height value of listY, pwidth and gfrac.

[0078] In some optional embodiments of the present application, the process of step 207, adding a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation, mainly includes: determining the maximum valley value and its position information and amplitude information based on the candidate fitting equation and the target spectroscopy measurement data; determining the position and height of the second preset peak based on the position information and the amplitude information; adding the second preset peak to the candidate fitting equation based on the position information and the amplitude information to obtain a new candidate fitting equation.

[0079] The process of determining the maximum valley and its position information and amplitude information based on the candidate fitting equation and the target spectroscopy measurement data mainly includes: Step one: calculating the residual sequence of the candidate equation and the target spectroscopy measurement data.

[0080] Step two: performing peak detection on the residual sequence to determine the maximum valley and its position information and amplitude information. The peak detection (find_peak) on the residual sequence only identifies valleys but not peaks, finds the largest valley therein, and records the position pl1 and the amplitude h1 of the valley. Correspondingly, the position and height of the second preset peak mentioned in the above embodiment can be set as pl1 and h1, and the width and shape parameters can be determined by pwidth and gfrac.

[0081] In actual applications, before performing peak detection on the residual sequence in step two, the residual sequence can also be smoothed to obtain a smoothed residual sequence, and then peak detection is performed, so as to further improve the accuracy of peak detection.

[0082] To deepen the understanding, the present application also gives a specific implementation scheme in combination with a specific application example, please refer to the specific implementation scheme shown in Figures 5A-5C In this specific application example, a randomly generated Pseudo Voigt multi-peak spectrum plus Gaussian noise is taken as an example to simulate the measured spectrum with noise. However, as described above, the method for fitting parameters of spectroscopy measurement data provided by the present embodiment can be used for identifying overlapping peaks in spectroscopy measurement data (one or more of spectrum, energy spectrum, chromatogram and nuclear magnetic resonance spectrum), and is not limited to spectrum.

[0083] Step one: collecting spectrum data (generating spectrum S1). Wherein, if the spectrum S1 contains a background spectrum, then the spectrum S1 is subtracted from the background spectrum to obtain the spectrum S2; if it contains no background spectrum, then the spectrum S1 is the same as S2. In actual applications, a low-pass filtering method can also be used as a smoothing method to smooth S1 (S2) to obtain the smoothed S1 (S2), as shown in Figure 5A In the present embodiment, for the convenience of description, the background-free spectrum S2 is mainly described in the following.

[0084] Step two: performing find_peak processing on S2, which obtains three peaks at approximately x=480, x=590 and x=690.

[0085] Step three: constructing a 12-parameter 3-peak Pseudo Voigt function f1 for the obtained three peaks, and the function parameters are not fixed.

[0086] Step four: bring the spectrum S2 into f1 fitting to obtain the first fitting equation f2.

[0087] Step five: calculate the curve S3 according to the first fitting equation f2, calculate the first root mean square error RMSE of the curve S3 and the spectrum S2, and obtain the first root mean square error value E1=0.0381 at this time, as shown in Figure 5B

[0088] Step six: calculate the residual sequence S4 of the curve S3 and the spectrum S2, S4=S3-S2.

[0089] Step seven: smooth the residual sequence S4 to obtain the smoothed data S5.

[0090] Step eight: find the maximum valley and its position information and amplitude information by find_peaks of the smoothed data S5.

[0091] Step nine: take the position of the maximum valley as the position of the newly added Pseudo Voigt peak, and add the original first fitting equation f2 to obtain the second fitting equation f3.

[0092] Step ten: substitute the spectrum S2 into the second fitting equation f3 to perform parameter fitting to obtain the third fitting equation f4.

[0093] Step eleven: calculate the curve S6 according to the third fitting equation f4, calculate the second root mean square error RMSE of the curve S6 and the spectrum S2, and obtain the second root mean square error value E2=0.0057 at this time, as shown in Figure 5C

[0094] Step twelve: at this time, E2<0.8*E1, which satisfies the convergence condition; and E2<0.015 (second preset threshold), so the third fitting equation f4 is taken as the final fitting equation of the spectrum S2.

[0095] Further referring to Figure 6 , as an implementation of the method shown in the above figures, the application provides an embodiment of a device for parameter fitting of spectroscopy measurement data, which corresponds to the method embodiment shown in Figure 2 , and the device can be applied in various electronic devices.

[0096] As Figure 6 ​​As shown, the device 600 for performing parameter fitting on spectroscopy measurement data in this embodiment can include: a target data acquisition module 601, a first fitting equation determination module 602, a residual calculation module 603, a candidate fitting equation generation module 604, a first root mean square error calculation module 605, a preset convergence condition judgment module 606, an iterative execution module 607, and a target fitting equation determination module 608. Among them, the target data acquisition module 601 is configured to acquire target spectroscopy measurement data; the first fitting equation determination module 602 is configured to perform curve fitting based on the target spectroscopy measurement data to obtain a first fitting equation; the residual calculation module 603 is configured to calculate the residual of the first fitting equation and the target spectroscopy measurement data; the candidate fitting equation generation module 604 is configured to add a first preset peak to the first fitting equation according to the position and amplitude of the trough in the residual to obtain a candidate fitting equation; the first root mean square error calculation module 605 is configured to calculate the first root mean square error of the candidate fitting equation and the target spectroscopy measurement data; the preset convergence condition judgment module 606 is configured to judge whether the first root mean square error meets the preset convergence condition; the iterative execution module 607 is configured to add a second preset peak to the candidate fitting equation if the first root mean square error does not meet the preset convergence condition, to obtain a new candidate fitting equation, and trigger the first root mean square error calculation module 605 and the preset convergence condition judgment module 606 to perform corresponding processes until the last obtained first root mean square error meets the preset convergence condition; and the target fitting equation determination module 608 is configured to take the corresponding candidate fitting equation as the target fitting equation of the target spectroscopy measurement data based on the preset convergence condition.

[0097] In this embodiment, in the device 600 for performing parameter fitting on spectroscopy measurement data: the specific processing of the target data acquisition module 601, the first fitting equation determination module 602, the residual calculation module 603, the candidate fitting equation generation module 604, the first root mean square error calculation module 605, the preset convergence condition judgment module 606, the iterative execution module 607, and the target fitting equation determination module 608 and the technical effects brought by them can be respectively referred to Figure 2 The related description of steps 201-208 in the corresponding embodiment will not be repeated here.

[0098] This embodiment exists as a device embodiment corresponding to the above-mentioned method embodiment. The device for performing parameter fitting on spectroscopy measurement data provided in this embodiment can realize the overall process through automation, which can effectively avoid random errors and subjective biases introduced by manual intervention. Through the iterative optimization and dynamic peak adding process, the accuracy can be significantly higher than that of traditional manual fitting, so as to realize efficient and accurate automatic fitting and obtain more accurate peak position and intensity information.

[0099] 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.

[0100] 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 instructions are executed by the computer.

[0101] 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.

[0102] Figure 7 A schematic block diagram of an example apparatus 700 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.

[0103] As shown in Figure 7 The apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the apparatus 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0104] A plurality of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0105] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 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 701 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 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, 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 701 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.

[0106] The embodiments of the present application also provide a wafer data processing method, which is executed by a computer or a server or the like electronic device, 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.

[0107] The embodiments of the present application also provide a thinning control method, which is executed by a computer or a server or the like electronic device, and specifically includes: obtaining a wafer thickness of a current frame according to the wafer data processing 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 thinning requirements.

[0108] The embodiment obtains the wafer thickness of the current frame by means of the accurate wafer data processing 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.

[0109] 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.

[0110] 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 the form of a computer program product implemented on one or more computer usable storage media containing computer usable program codes, including but not limited to disk memory, CD-ROM, optical memory, etc.

[0111] 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 the 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 produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0112] These computer program instructions can also be stored in a computer readable memory that can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0113] 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 one or more flows and / or blocks Figure 1 Figure 1steps of the functions specified in the one or more blocks.

[0114] Obviously, the above-described embodiments are only examples for the purpose of clarity and are not intended to limit the implementation. Based on the above description, other different forms of changes or variations can also be made by those of ordinary skill in the art. It is not necessary or possible to exhaust all the implementations. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method of parameter fitting of spectroscopy measurement data, characterized in that, The method comprises: obtaining target spectroscopy measurement data; performing curve fitting based on the target spectroscopy measurement data to obtain a first fitting equation; calculating residuals of the first fitting equation and the target spectroscopy measurement data; adding a first preset peak to the first fitting equation according to positions and amplitudes of valleys in the residuals to obtain a candidate fitting equation; calculating a first root mean square error of the candidate fitting equation and the target spectroscopy measurement data; determining whether the first root mean square error meets a preset convergence condition; if the first root mean square error does not meet the preset convergence condition, adding a second preset peak to the candidate fitting equation to obtain a new candidate fitting equation, and returning to the step of calculating the first root mean square error of the candidate fitting equation and the target spectroscopy measurement data until the last obtained first root mean square error meets the preset convergence condition; based on the preset convergence condition, taking the corresponding candidate fitting equation as a target fitting equation of the target spectroscopy measurement data.

2. The method of claim 1, wherein, The step of adding the second preset peak to the candidate fitting equation to obtain a new candidate fitting equation comprises: determining a maximum valley value and position information and amplitude information thereof based on the candidate fitting equation and the target spectroscopy measurement data; determining a position and height of the second preset peak based on the position information and amplitude information; adding the second preset peak to the candidate fitting equation based on the position and height of the second preset peak to obtain a new candidate fitting equation.

3. The method of claim 2, wherein, The step of determining a maximum valley value and position information and amplitude information thereof based on the candidate fitting equation and the target spectroscopy measurement data comprises: calculating a residual sequence of the candidate fitting equation and the target spectroscopy measurement data; performing peak value detection on the residual sequence to determine the maximum valley value and the position information and the amplitude information thereof.

4. The method of claim 1, wherein, The method further comprises: in response to the last obtained first root mean square error being greater than or equal to a first preset threshold, dividing a range of independent variables of the target spectroscopy measurement data to obtain a plurality of division intervals and division junction points; for each of the division junction points, adding a third preset peak to the last obtained first root mean square error to obtain a junction point equation; a position parameter of the third preset peak is determined based on the division junction point, and a height parameter is determined based on a maximum peak value; calculating a second root mean square error of each of the junction point equations and the target spectroscopy measurement data, and determining a minimum second root mean square error; returning to the step of dividing the range of independent variables of the target spectroscopy measurement data to obtain a plurality of division intervals and division junction points until a new minimum second root mean square error is less than a preset proportion compared with a previous minimum second root mean square error; determining a junction point equation corresponding to a minimum second root mean square error greater than or equal to the preset proportion in the last reduction amplitude as a target fitting equation of the target spectroscopy measurement data.

5. The method of claim 1, wherein, The step of obtaining target spectroscopy measurement data comprises: obtaining original spectroscopy measurement data; in response to presetting 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 not presetting background spectroscopy measurement data, taking the original spectroscopy measurement data as the target spectroscopy measurement data.

6. The method of claim 1, wherein, The curve fitting based on the target spectroscopy measurement data includes: constructing a user-defined parameter equation based on the target spectroscopy measurement data; performing curve fitting based on the target spectroscopy measurement data and the user-defined parameter equation to obtain the first fitting equation.

7. The method of claim 6, wherein, The construction of the user-defined parameter equation based on the target spectroscopy measurement data includes: performing peak detection on the target spectroscopy measurement data to obtain a first peak set, the first peak set including a quantity set, a position set, and a height set; determining a peak width parameter and a Gaussian proportion based on a range of independent variables of the target spectroscopy measurement data; constructing the user-defined parameter equation based on the quantity set, the position set, the height set, the peak width parameter, and the Gaussian proportion.

8. The method of claim 7, wherein, The user-defined parameter equation is a sum of a plurality of user-defined functions corresponding to the number of the quantity set, and parameters of the user-defined functions are determined based on the position set, the height set, the peak width parameter, and the Gaussian proportion.

9. The method of claim 1, wherein, The preset convergence condition includes: The first root mean square error is greater than or equal to a preset threshold value, and the preset threshold value is determined based on a previous first root mean square error value; or the first root mean square error is less than a preset absolute standard value.

10. The method of claim 9, wherein, The corresponding candidate fitting equation based on the preset convergence condition is taken as the target fitting equation of the target spectroscopy measurement data, including: in response to the first root mean square error obtained last time being greater than or equal to the preset threshold value, taking the candidate fitting equation corresponding to the previous first root mean square error as the target fitting equation; or in response to the first root mean square error obtained last time being less than a preset absolute standard value, taking the candidate fitting equation corresponding to the first root mean square error obtained last time as the target fitting equation.

11. A wafer data processing method, characterized by, including: obtaining a target fitting equation by using the method of any one of claims 1-10; obtaining a main peak peak value and a secondary peak peak value of a current frame based on the target fitting equation; calculating a wafer thickness of the current frame according to the main peak peak value and the secondary peak peak value.

12. A method of reducing the control of thinning, characterized by, including: obtaining a wafer thickness of a current frame according to the method of claim 11; stopping wafer thinning when the wafer thickness of the current frame reaches a preset thickness.

13. An apparatus for parameter fitting of spectroscopy measurement data, characterized in that, including: 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 method of parameter fitting for spectroscopy measurement data according to any one of claims 1-10.

14. A wafer data processing apparatus, characterized by comprising: including: 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 wafer data processing method according to claim 11.

15. A thinning control apparatus characterized by comprising: including: 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 cause the processor to perform the thinning control method of claim 12.

16. A reduction machine characterized by, A method for thinning a wafer and performing the method of any one of claims 1-12 during the thinning process.

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