Method for identifying multiple peaks in spectroscopic measurement data, thickness measurement method and equipment

Abstract

The invention provides an identification method for multiple peaks in spectroscopic measurement data, a thickness measurement method and equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring target spectroscopic measurement data; performing differential processing on the target spectroscopic measurement data to obtain differential spectroscopic measurement data; fitting according to a multimodal derivative equation based on the differential spectroscopy measurement data to obtain a first fitting result; the multimodal derivative equation is obtained based on a multimodal parameter equation of the target spectroscopic measurement data; the parameter of the first fitting result is taken as initial guess, fitting is carried out according to a multi-peak parameter equation based on the target spectroscopic measurement data, a second fitting result is obtained, and the second fitting result comprises multi-peak position information. By implementing the method, the baseline interference can be eliminated, and the peak feature recognition accuracy is enhanced. And moreover, failure of a fitting algorithm caused by a random initial value can be avoided, the convergence speed and stability are improved, and the recognition accuracy of the multi-peak features is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method for identifying multiple peaks in spectroscopy measurement data, a thickness measurement method, and a device therefor. Background Art

[0002] Spectroscopy measurements such as spectroscopy and energy spectroscopy are important steps in scientific research and industrial production. By analyzing spectroscopy data such as spectra and energy spectra, effective information can be extracted. For example, the position of a peak can provide physical or chemical intensity attributes, and the area of a peak can provide physical or chemical breadth attributes. However, due to the thermodynamic fluctuations in the physical properties of the system particles in the actually measured data, there are various broadenings. Therefore, the actually measured data often contains peaks with a certain width, and sometimes the width of the peak will affect the accurate attribution of the peak position. Summary of the Invention

[0003] In view of this, the present invention provides a method for identifying multiple peaks in spectroscopy measurement data, a thickness measurement method, and a device therefor.

[0004] In a first aspect, an embodiment of the present invention provides a method for identifying multiple peaks in spectroscopy measurement data, including: obtaining target spectroscopy measurement data; performing differential processing on the target spectroscopy measurement data to obtain differential spectroscopy measurement data; performing fitting on the differential spectroscopy measurement data according to a multiple-peak derivative equation to obtain a first fitting result; the multiple-peak derivative equation is obtained based on a multiple-peak parameter equation of the target spectroscopy measurement data; using the parameters of the first fitting result as an initial guess, and performing fitting on the target spectroscopy measurement data according to the multiple-peak parameter equation to obtain a second fitting result, where the second fitting result includes the position information of multiple peaks.

[0005] In a second aspect, an embodiment of the present invention provides a thickness measurement method, which includes: obtaining the peak value of the main peak and the peak value of the secondary peak of the current frame by using the method for identifying multiple peaks in spectroscopy measurement data described in any implementation manner of the first aspect; calculating the wafer thickness of the current frame according to the peak value of the main peak and the peak value of the secondary peak.

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

[0007] Fourth aspect, an embodiment of the present invention provides a multi-peak identification device for spectroscopic measurement data. The multi-peak identification device for spectroscopic measurement data includes: 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 when the instructions are executed by the at least one processor, the at least one processor is enabled to implement the multi-peak identification method for spectroscopic measurement data described in any implementation manner of the first aspect.

[0008] Fifth aspect, an embodiment of the present invention proposes a thickness measurement device, which includes: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor is enabled to implement the thickness measurement method described in the second aspect.

[0009] Sixth aspect, an embodiment of the present invention proposes a thinning control device, which includes: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor is enabled to implement the thinning control method described in the third aspect.

[0010] Seventh aspect, an embodiment of the present invention proposes a thinning machine for thinning a wafer and executing the method described in any implementation manner of the first aspect during the thinning process.

[0011] The multi-peak identification method, thickness measurement method and device provided by the embodiments of the present invention can eliminate baseline interference and enhance the accuracy of peak feature recognition by performing differential processing on the target spectroscopic measurement data. Moreover, the parameters obtained from the first fitting are used as the initial guess for the second fitting, making the initial parameters close to the true values, avoiding the failure of the fitting algorithm due to random initial values, improving the convergence speed and stability, and further enhancing the accuracy of multi-peak feature recognition.

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

[0013] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0014] Figure 1 is an exemplary system architecture in which the present invention can be applied; Figure 2 is a flowchart of a method for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention; Figure 3 is a flowchart of another method for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention; Figure 4 is a flowchart of another method for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention; Figures 5A - 5C is a schematic diagram of a method for identifying multiple peaks in spectroscopic measurement data in an application scenario provided by an embodiment of the present invention; Figure 6 is a block diagram of a structure of an apparatus for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention; Figure 7 is a schematic diagram of a structure of an electronic device suitable for executing a method for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention. Detailed implementation manners

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0017] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can also be the communication inside two elements. It can be a wireless connection or a wired connection. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0018] In addition, the technical features involved in 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 of an embodiment of a method, apparatus, electronic device, and computer-readable storage medium for identifying multiple peaks in spectroscopic measurement data to which the present invention can be applied is shown.

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

[0021] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various applications for implementing information communication between the two may be installed on the terminal devices 101, 102, 103 and the server 105, such as instant messaging applications, etc.

[0022] The terminal devices 101, 102, 103 and the server 105 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptop portable computers, and desktop computers, etc.; when the terminal devices 101, 102, 103 are software, they may be installed in the above-listed electronic devices, and they may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not specifically limited herein. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server; when the server is software, it may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not specifically limited herein.

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

[0024] Since the process of analyzing and processing data may require a large amount of computing resources and strong computing power, the method for identifying multiple peaks in the spectroscopic measurement data provided in the subsequent embodiments of the present invention is generally executed by the server 105 with strong computing power and a large amount of computing resources. Correspondingly, the device for identifying multiple peaks in the spectroscopic measurement data is generally also set in the server 105. However, it should also be noted that when the terminal devices 101, 102, and 103 also have computing power and computing resources that meet the requirements, the terminal devices 101, 102, and 103 can also complete the above operations that were originally performed by the server 105 through the relevant applications installed on them, and then output the same results as the server 105. Especially in the case where there are multiple terminal devices with different computing capabilities at the same time, when the relevant application determines that the terminal device where it is located has strong computing power and a large amount of remaining computing resources, the terminal device can be allowed to execute the above operations, thereby appropriately reducing the computing pressure on the server 105. Correspondingly, the device for identifying multiple peaks in the spectroscopic measurement data can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may not include the server 105 and the network 104 either.

[0025] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0026] Please refer to Figure 2 , Figure 2 which is a flowchart of a method for identifying multiple peaks in spectroscopic measurement data provided by an embodiment of the present invention. The process 200 includes the following steps: Step 201: Obtain target spectroscopic measurement data.

[0027] This step aims to obtain the corresponding target spectroscopic measurement data by the execution entity of the method for identifying multiple peaks in spectroscopic measurement data (such as Figure 1 the server 105 shown). In this embodiment, the target spectroscopic measurement data may at least include one or more of spectrum, energy spectrum, chromatogram, and mass spectrum (for example, types such as ultraviolet-visible-near-infrared spectrum, X-ray photoelectron spectroscopy (XPS spectrum), solid nuclear magnetic resonance spectrum, etc.), and the measurement data generated by various spectroscopic analysis methods can be obtained.

[0028] Step 202: Perform differential processing on the target spectroscopic measurement data to obtain differential spectroscopic measurement data.

[0029] This step aims to perform differential processing on the target spectroscopic measurement data by the above-mentioned execution entity to obtain differential spectroscopic measurement data. In this embodiment, through the differential processing method, the change trend of the signal can be amplified, so it is easier to fit compared with the original data.

[0030] Step 203: Fit according to the multi-peak derivative equation based on the differential spectroscopic measurement data to obtain the first fitting result.

[0031] This step aims to perform fitting according to the multi-peak derivative equation based on the differential spectroscopic measurement data by the above-mentioned execution entity to obtain the first fitting result. Among them, the multi-peak derivative equation is obtained based on the multi-peak parameter equation of the target spectroscopic measurement data. The multi-peak parameter equation generates a curve with multiple extreme points by parameterizing the non-linear relationship between variables.

[0032] Exemplarily, when the target spectroscopic measurement data is a spectrum, the multi-peak parameter equation for a double Gaussian peak is: , where A1 represents the amplitude of the first peak, μ1 represents the symmetry center of the first peak, and σ1 represents the standard deviation of the first peak; A2 represents the amplitude of the second peak, μ2 represents the symmetry center of the second peak, and σ2 represents the standard deviation of the second peak. The double Gaussian peak refers to a composite structure formed by the superposition of two Gaussian distributions (normal distributions), which is common in data analysis, signal processing, and statistics.

[0033] Step 204: Use the parameters of the first fitting result as the initial guess, and perform fitting according to the multi-peak parameter equation based on the target spectroscopic measurement data to obtain the second fitting result.

[0034] This step aims to use the parameters of the first fitting result as the initial guess for fitting by the above-mentioned execution entity, and perform fitting according to the multi-peak parameter equation based on the target spectroscopic measurement data, so as to obtain the second fitting result. In this second fitting result, it contains the position information of the multi-peaks, that is, the specific positions of the multi-peaks in the target spectroscopic measurement data can be determined.

[0035] When fitting a multi-peak equation, the Initial Guess refers to the preliminary estimated values of the equation parameters (such as mean, standard deviation, amplitude, etc.). A suitable Initial Guess can significantly improve the convergence speed and accuracy of the fitting algorithm, especially when dealing with complex data (such as overlapping peaks, noise interference). In this embodiment, the parameters of the first fitting result determined through the above steps 201-203 are used as the Initial Guess for the second fitting. When performing the second fitting using the original data, only the result of the first time is used as the Initial Guess, without introducing the additional errors brought by the smoothing and differencing processes, realizing the efficient processing and analysis of the original data. At the same time, the two-step fitting method does not require manual input of the Initial Guess. The solution of the first step is used as the Initial Guess for the second fitting, which can effectively improve the fitting success rate and accuracy.

[0036] The multi-peak identification method for spectroscopic measurement data provided by the embodiments of the present invention can enhance the accuracy of peak feature identification by performing differencing processing on the target spectroscopic measurement data. Moreover, using the parameters obtained from the first fitting as the Initial Guess for the second fitting makes the initial parameters close to the true values, avoiding the failure of the fitting algorithm due to random initial values, improving the convergence speed and stability, and further enhancing the accuracy of multi-peak feature identification.

[0037] Please refer to Figure 3 , Figure 3 which is a flowchart of a multi-peak identification method for spectroscopic measurement data provided by an embodiment of the present disclosure. That is, a specific implementation manner is provided for step 201 in the process 200 shown in Figure 2 . Other steps in the process 200 are not adjusted, and a new complete embodiment is obtained by replacing step 201 with the specific implementation manner provided in this embodiment. The process 300 includes the following steps: Step 301: Obtain the measured spectroscopic measurement data.

[0038] The measured spectroscopic measurement data can be the measured data of one or more of spectroscopy, energy spectroscopy, chromatography, and mass spectrometry. The measured data may include noise data and background data, etc. In this embodiment, the noise data and background data in the measured data can be processed.

[0039] Step 302: In response to the preset background spectroscopic measurement data, perform background removal processing on the measured spectroscopic measurement data based on the background spectroscopic measurement data to obtain the target spectroscopic measurement data after background removal.

[0040] For the case where the measured spectroscopic measurement data contains background spectroscopic measurement data, the measured spectroscopic measurement data can be subjected to background removal processing (for example, subtracting the background spectroscopic measurement data from the measured spectroscopic measurement data) to obtain the target spectroscopic measurement data after background removal.

[0041] Step 303: In response to the absence of pre-set background spectroscopy measurement data, use the measured spectroscopy measurement data as the target spectroscopy measurement data.

[0042] For the case where the center of the measured spectroscopy measurement data does not contain background spectroscopy measurement data, directly use the measured spectroscopy measurement data as the target spectroscopy measurement data.

[0043] In some alternative embodiments of this embodiment, the execution subject may further perform smoothing processing on the target spectroscopy measurement data to obtain the smoothed target spectroscopy measurement data. Exemplarily, this smoothing process can be implemented through the selected smoothing algorithm. In this embodiment, the process of selecting the smoothing algorithm mainly includes: Step 1: Use a variety of candidate smoothing algorithms to perform smoothing processing on the pre-set sample measurement data to obtain the smoothing result; the sample measurement data includes standard measurement data and noise data.

[0044] In this embodiment, the smoothing algorithm is selected through the pre-set sample measurement data. The pre-set sample measurement data includes standard measurement data and noise data, which are used to compare with the data after smoothing processing by a variety of candidate smoothing algorithms.

[0045] Step 2: Based on the smoothing result, standard measurement data, and noise data, select the optimal smoothing algorithm from a variety of candidate smoothing algorithms.

[0046] In this embodiment, after performing smoothing processing using a variety of candidate smoothing algorithms, the corresponding smoothed measurement data can be obtained. By comparing the smoothed measurement data with the standard measurement data and noise data, the smoothing effect of a variety of candidate smoothing algorithms can be analyzed and ranked, so as to select the optimal smoothing algorithm.

[0047] Step 3: Use the optimal smoothing algorithm to perform smoothing processing on the target spectroscopy measurement data to obtain the smoothed target spectroscopy measurement data.

[0048] After screening and determining the optimal smoothing algorithm, in specific implementation, the target spectroscopy measurement data can be smoothed through the optimal smoothing algorithm to obtain the smoothed target spectroscopy measurement data.

[0049] Through the above process, interference factors (background data, noise data, etc.) in the measured spectroscopy measurement data can be excluded, further improving the accuracy of analysis and recognition based on the target spectroscopy measurement data.

[0050] In some alternative embodiments of this embodiment, in step 202, the process of performing differential processing on the target spectroscopic measurement data to obtain the differential spectroscopic measurement data mainly includes: performing forward difference on the target spectroscopic measurement data and assigning 0 to the last point to obtain the differential spectroscopic measurement data. Exemplarily, for the forward difference of the target spectroscopic measurement data S1 to obtain the differential spectroscopic measurement data S2, the process can be expressed as: S2n = S1(n + 1) - S1n, where n is a positive integer greater than or equal to 1, and 0 is assigned to the last point, thereby obtaining the differential spectroscopic measurement data S2. Through this process, the peak top can be converted into the zero crossing point of the difference (the crossing point from positive to negative), improving the accuracy of subsequent peak recognition. It should be noted that in this embodiment, forward difference is used as an example for illustration, but in specific implementation, other differential processing methods can also be used, such as backward difference, difference between the previous and the next points, etc. Moreover, the assignment of the last point (i.e., the filling at the edge) can also be implemented by other methods. Besides filling with 0, it can also be linearly extrapolated from nearby values or fitted with a custom equation using a global function. The present invention is not limited thereto.

[0051] In some alternative embodiments of this embodiment, as Figure 4 shown, in step 203, the process of obtaining the first fitting result by fitting the differential spectroscopic measurement data according to the multi-peak derivative equation mainly includes: Step 401: Obtain the multi-peak parameter equation corresponding to the target spectroscopic measurement data.

[0052] The purpose of this step is for the above-mentioned execution entity to obtain the multi-peak parameter equation corresponding to the target spectroscopic measurement data. In specific implementation, for different spectroscopic measurement data (such as spectra, energy spectra, etc.) and different multi-peak states, there are corresponding multi-peak parameter equations. Exemplarily, for spectra, the multi-peak parameter equation for a double Gaussian peak is: , where A1 represents the amplitude of the first peak, μ1 represents the symmetry center of the first peak, σ1 represents the standard deviation of the first peak; A2 represents the amplitude of the second peak, μ2 represents the symmetry center of the second peak, and σ2 represents the standard deviation of the second peak.

[0053] Step 402: Take the derivative of the multi-peak parameter equation to obtain the multi-peak derivative equation.

[0054] In this embodiment, the process by which the execution entity obtains the multi-peak derivative equation based on the multi-peak parameter equation can, for example, be taking the derivative of the multi-peak parameter equation to obtain the multi-peak derivative equation. Exemplarily, taking the first derivative of the multi-peak parameter equation for the double Gaussian peak in the above spectra, the obtained first derivative equation is: ,

[0055] Among them, A1 represents the amplitude of the first peak, μ1 represents the symmetry center of the first peak, and σ1 represents the standard deviation of the first peak; A2 represents the amplitude of the second peak, μ2 represents the symmetry center of the second peak, and σ2 represents the standard deviation of the second peak.

[0056] It should be noted that the first derivative described in this embodiment is only an example to illustrate the derivative process and is not intended to limit the present invention. In practical applications, taking the first derivative, second derivative,..., nth derivative of the multi-peak parametric equation can all be used to obtain the above multi-peak derivative equation. That is to say, the form of the multi-peak parametric equation and the specific derivative process can be set according to different spectroscopic measurement data. The present invention is not limited thereto.

[0057] Step 403: Based on the differential spectroscopic measurement data, perform fitting according to the multi-peak derivative equation to obtain a first fitting result.

[0058] After obtaining this multi-peak derivative equation, the executor can perform fitting based on the differential spectroscopic measurement data according to this multi-peak derivative equation, thereby obtaining a first fitting result, which contains position parameters. In this embodiment, using the first derivative of the double Gaussian peak function instead of the original function for parameter fitting can expand the gap between the two peaks and make it easier to identify the positions of the two peaks.

[0059] For better understanding, the present invention also combines a specific application example and gives a specific implementation scheme. Please refer to Figures 5A - 5C as shown. In this specific application example, a spectrum containing a main peak and a secondary peak is used as an example for illustration. However, as mentioned above, the multi-peak identification method for spectroscopic measurement data provided in this embodiment can be used to identify multi-peaks in spectroscopic measurement data (one or more of spectra, energy spectra, chromatograms, and mass spectra), and is not limited to spectra. For spectra, the abscissa x in the figure represents wavelength, and the ordinate y represents intensity; for energy spectra, the abscissa x in the figure represents energy, and the ordinate y represents intensity; for chromatograms, the abscissa x in the figure represents time, and the ordinate y represents intensity / concentration; for nuclear magnetic resonance, the abscissa x in the figure represents nuclear magnetic shift, and the ordinate y represents intensity.

[0060] Step 1: Obtain spectrum S1 (here, a generated spectrum is used, with the height of the first peak being 1, the height of the second peak being 0.2, the peak width of both being 10, the position of the first peak being 500, the position of the second peak being 523, the peak spacing being 23, close to the Rayleigh criterion limit value, plus Gaussian-distributed random noise with a standard deviation of 0.01, the signal-to-noise ratio of the first peak being 100, and the second peak being 20), as Figure 5A shown. When a background spectrum is included, spectrum S2 is obtained by subtracting the background spectrum from spectrum S1; if there is no background spectrum, spectra S1 and S2 are the same.

[0061] Step 2: Use the selected optimal smoothing algorithm to smooth the spectrum S1 (S2) to obtain the spectrum S3, as Figure 5B shown.

[0062] Step 3: Perform a difference operation on the spectrum S3 to obtain the difference spectrum S4. Each point S4n of S4 = S3(n + 1) - S3n, and the last point is assigned 0, as Figure 5C shown, which also includes the spectral curve after smoothing the spectrum S4.

[0063] Step 4: Fit the S4 according to the multi-peak derivative equation with parametric equations (exemplarily, the curve_fit function in the scipy library of python can be used for fitting), and obtain a set of 6 parameters p0, where the parameters of the peak positions are 499.5257 and 523.0476 respectively.

[0064] Step 5: Use p0 as the initial guess, and fit the spectrum S1 (S2) according to the multi-peak parametric equation with parametric equations to obtain a set of 6 parameters p1. p1 contains the position, width, and height information of two peaks. The position parameters of the peaks in p1 are 499.9950 and 523.0468 respectively, and the differences from the set values are 0.0050 and 0.0468 respectively, which can solve the positions of the two peaks with high precision and are not significantly affected by the overlap of the two peaks.

[0065] Further referring to Figure 6 , as an implementation of the methods shown in the above figures, the present invention provides an embodiment of a multi-peak identification device for spectroscopic measurement data. This device embodiment corresponds to the Figure 2 shown method embodiment, and this device can be specifically applied to various electronic devices.

[0066] As Figure 6 shown, the multi-peak identification device 600 for spectroscopic measurement data in this embodiment may include: a measurement data acquisition module 601, a difference processing module 602, a first fitting module 603, and a second fitting module 604. Among them, the measurement data acquisition module 601 is configured to acquire target spectroscopic measurement data; the difference processing module 602 is configured to perform a difference operation on the target spectroscopic measurement data to obtain difference spectroscopic measurement data; the first fitting module 603 is configured to perform fitting based on the difference spectroscopic measurement data according to the multi-peak derivative equation to obtain a first fitting result; the multi-peak derivative equation is obtained based on the multi-peak parametric equation of the target spectroscopic measurement data; the second fitting module 604 is configured to use the parameters of the first fitting result as the initial guess, and perform fitting based on the target spectroscopic measurement data according to the multi-peak parametric equation to obtain a second fitting result, and the second fitting result contains the position information of the multi-peaks.

[0067] In this embodiment, in the multi-peak recognition device 600 for spectroscopic measurement data, for the specific processing of the measurement data acquisition module 601, the differential processing module 602, the first fitting module 603, and the second fitting module 604 and the technical effects brought thereby, reference can be respectively made to Figure 2 the relevant descriptions of steps 201-204 in the corresponding embodiment, which will not be elaborated herein.

[0068] This embodiment exists as a device embodiment corresponding to the above method embodiment. The multi-peak recognition device for spectroscopic measurement data provided in this embodiment can eliminate baseline interference and enhance the recognition accuracy of peak features by performing differential processing on the target spectroscopic measurement data. Moreover, the parameters obtained from the first fitting are used as the initial guess for the second fitting, making the initial parameters close to the true values, avoiding the failure of the fitting algorithm due to random initial values, improving the convergence speed and stability, and further enhancing the recognition accuracy of multi-peak features.

[0069] According to an embodiment of the present invention, the present invention also provides a multi-peak recognition device for spectroscopic measurement data, which includes: 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 when the instructions are executed by the at least one processor, the at least one processor can implement the multi-peak recognition method for spectroscopic measurement data described in any of the above embodiments.

[0070] According to an embodiment of the present invention, the present invention also provides a readable storage medium storing computer instructions for enabling a computer to implement the multi-peak recognition method for spectroscopic measurement data described in any of the above embodiments when executed.

[0071] According to an embodiment of the present invention, the present invention also provides a computer program product, which can implement the multi-peak recognition method for spectroscopic measurement data described in any of the above embodiments when executed by a processor.

[0072] Figure 7 A schematic block diagram of an exemplary multi-peak recognition device 700 for spectroscopic measurement data that can be used to implement the embodiments of the present invention is shown. The multi-peak recognition device for spectroscopic measurement data is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0073] As shown Figure 7 in FIG. 1, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to 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. In the RAM 703, various programs and data required for the operation of the apparatus 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0074] A plurality of components in the apparatus 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 apparatus 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0075] 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 dedicated 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 executes the various methods and processes described above, such as the method for identifying multiple peaks in spectroscopic measurement data. For example, in some embodiments, the method for identifying multiple peaks in spectroscopic measurement data can be implemented as a computer software program that 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 onto the apparatus 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 for identifying multiple peaks in spectroscopic measurement data described above can be executed. Alternatively, in other embodiments, the computing unit 701 can be configured to execute the method for identifying multiple peaks in spectroscopic measurement data by any other appropriate means (e.g., by means of firmware).

[0076] An embodiment of the present invention also provides a thickness measurement method, which is executed by an electronic device such as a computer or a server, and specifically includes: obtaining the main peak peak value and the secondary peak peak value of the current frame by using the method for identifying multiple peaks in spectroscopic measurement data described in any of the above embodiments.

[0077] An embodiment of the present invention also provides a thinning control method, which is executed by an electronic device such as a computer or a server. Specifically, it includes: obtaining the wafer thickness of the current frame according to the thickness measurement method described in any of the above embodiments; stopping the thinning of the wafer when the wafer thickness of the current frame reaches a preset thickness. The preset thickness is the thickness value set according to the thinning requirement.

[0078] This embodiment obtains the wafer thickness of the current frame by means of an accurate thickness measurement method, which can provide real-time and accurate thickness data for the thinning process. When the wafer thickness reaches the preset value, the thinning operation is automatically stopped, which can effectively avoid the problems of over-thinning or under-thinning, ensure that the final thickness of the wafer accurately meets the expected standard, and improve the quality and consistency of wafer thinning processing.

[0079] An embodiment of the present invention also provides a thinning machine for thinning the wafer and executing the method described in any of the above embodiments during the thinning process.

[0080] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of 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 code.

[0081] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented 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 devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 or steps of the function specified in a plurality of blocks.

[0084] Obviously, the above-described embodiments are merely examples for clear illustration and are not intended to limit the implementation. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to exhaustively list all implementations here. The obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A method for identifying multiple peaks in spectroscopic measurement data, characterized in that, Including: Obtain target spectroscopy measurement data; Perform differential processing on the target spectroscopy measurement data to obtain differential spectroscopy measurement data; Based on the differential spectroscopy measurement data, perform fitting according to a multi-peak derivative equation to obtain a first fitting result; the multi-peak derivative equation is obtained based on a multi-peak parameter equation of the target spectroscopy measurement data; Use the parameters of the first fitting result as initial guesses, and based on the target spectroscopy measurement data, perform fitting according to the multi-peak parameter equation to obtain a second fitting result, where the second fitting result includes the position information of multiple peaks.

2. The method according to claim 1, wherein The obtaining of the target spectroscopy measurement data includes: Obtain measured spectroscopy measurement data; In response to the pre-existence of background spectroscopy measurement data, perform background removal processing on the measured spectroscopy measurement data based on the background spectroscopy measurement data to obtain the background-removed target spectroscopy measurement data; In response to the non-pre-existence of background spectroscopy measurement data, use the measured spectroscopy measurement data as the target spectroscopy measurement data.

3. The method according to claim 2, characterized in that, The obtaining of the target spectroscopy measurement data further includes: Perform smoothing processing on the target spectroscopy measurement data to obtain smoothed target spectroscopy measurement data.

4. The method according to claim 3, wherein The performing of the smoothing processing on the target spectroscopy measurement data to obtain smoothed target spectroscopy measurement data includes: Use multiple candidate smoothing algorithms to perform smoothing processing on preset sample measurement data to obtain smoothing processing results; the sample measurement data includes standard measurement data and noise data; Based on the smoothing processing results, standard measurement data, and noise data, screen for the optimal smoothing algorithm among the multiple candidate smoothing algorithms; Use the optimal smoothing algorithm to perform smoothing processing on the target spectroscopy measurement data to obtain the smoothed target spectroscopy measurement data.

5. The method according to claim 1, wherein The performing of the fitting based on the differential spectroscopy measurement data according to the multi-peak derivative equation to obtain a first fitting result includes: Obtain the multi-peak parameter equation corresponding to the target spectroscopy measurement data; Differentiate the multi-peak parameter equation to obtain the multi-peak derivative equation; Based on the differential spectroscopy measurement data, perform fitting according to the multi-peak derivative equation to obtain the first fitting result.

6. A thickness measurement method, characterized in that, Including: Use the method according to any one of claims 1-5 to obtain the main peak peak value and the secondary peak peak value of the current frame; Calculate the wafer thickness of the current frame according to the main peak peak value and the secondary peak peak value.

7. A thinning control method, characterized in that, Including: Obtain the wafer thickness of the current frame according to the method of claim 6; When the wafer thickness of the current frame reaches a preset thickness, stop thinning the wafer.

8. An apparatus for identifying multiple peaks in spectroscopic 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 execute the method for identifying multiple peaks in spectroscopy measurement data according to any one of claims 1-5.

9. A thickness measuring device, 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 execute the thickness measurement method according to claim 6.

10. A thinning machine, characterized in that, For thinning a wafer and performing the method according to any one of claims 1 to 7 during the thinning process.

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