Method for identifying overlapping peaks in spectroscopy measurement data, thickness measurement method and device

Abstract

The present invention provides a method for identifying overlapping peaks in spectral measurement data, a thickness measurement method, and an apparatus, and relates to the field of data processing technology. The method comprises: acquiring target spectral measurement data; performing peak detection on the target spectral measurement data to obtain a main peak identification result; identifying the relative position of a second peak to the main peak peak based on the main peak identification result; determining an independent variable range based on the target spectral measurement data and the relative position, and generating a half-edge measurement result including the main peak peak; fitting the half-edge measurement result using a custom function to obtain a first fitting function, wherein the first fitting function includes the main peak position; substituting a global independent variable in the target spectral measurement data into the first fitting function to obtain a first fitting result; obtaining second peak data based on the target spectral measurement data and the first fitting result; fitting the second peak data using the custom function to obtain a second fitting function, wherein the second fitting function includes the second peak position.

Description

Technical Field

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

[0002] Spectroscopy, energy spectrum analysis, and other measurement and analysis techniques are crucial in scientific research and industrial production. By analyzing spectral and energy spectrum data, useful information can be extracted, such as peak position, which can provide physical or chemical intensity properties, and peak area, which can provide physical or chemical breadth properties. However, in actual measured spectra and other analytical data, various broadenings may occur due to thermodynamic fluctuations in the physical properties of the system's particles. Consequently, actual analytical data often contain peaks of a certain width, and this peak width can sometimes affect the precise attribution of peak positions. Summary of the Invention

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

[0004] In a first aspect, an embodiment of the present invention proposes a method for identifying overlapping peaks in spectroscopic measurement data, comprising: acquiring target spectroscopic measurement data; performing peak detection on the target spectroscopic measurement data to obtain a main peak identification result; identifying the relative position of a second peak to the peak value of the main peak based on the main peak identification result; determining the range of independent variables based on the target spectroscopic measurement data and the relative position, and generating a half-edge measurement result including the peak value of the main peak; fitting the half-edge measurement result using a custom function to obtain a first fitting function, wherein the first fitting function includes the main peak position; substituting the global independent variables in the target spectroscopic measurement data into the first fitting function to obtain a first fitting result; obtaining second peak data based on the target spectroscopic measurement data and the first fitting result; fitting the second peak data using a custom function to obtain a second fitting function, wherein the second fitting function includes the second peak position.

[0005] In the second aspect, an embodiment of the present invention proposes a thickness measurement method, which includes: using the method for identifying overlapping peaks in spectral measurement data described in any implementation method of the first aspect to obtain the main peak peak value and the second peak peak value of the current frame; calculating the wafer thickness of the current frame based on the main peak peak value and the second peak peak value.

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

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

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

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

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

[0011] The method and device for identifying overlapping peaks in spectroscopic measurement data provided by the embodiments of the present invention can analyze and process spectroscopic measurement data containing overlapping peaks, and accurately identify peak values such as main peaks and secondary peaks with relatively small data volume and computational complexity, thereby improving the usability of spectroscopic measurement data.

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

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

[0014] Figure 1 is an exemplary system architecture in which the present invention may be applied;

[0015] Figure 2 A flowchart of a method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention;

[0016] Figure 3 A flowchart of another method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention;

[0017] Figure 4 A flowchart of another method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention;

[0018] Figures 5A-5E A schematic diagram of a method for identifying overlapping peaks in spectroscopy measurement data in an application scenario provided by an embodiment of the present invention;

[0019] Figure 6 A structural block diagram of a device for identifying overlapping peaks in spectral measurement data provided by an embodiment of the present invention;

[0020] Figure 7 A schematic structural diagram of an electronic device suitable for executing a method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components; wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0024] 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.

[0025] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the method and apparatus for identifying overlapping peaks in spectroscopic measurement data of the present invention may be applied.

[0026] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0027] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 and server 105 may be installed with various applications for enabling information communication between them, such as instant messaging applications.

[0028] Terminal devices 101, 102, 103 and server 105 can be either hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules, or as a single software or software module, and are not specifically limited here. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules, or as a single software or software module, and are not specifically limited here.

[0029] Server 105 can provide various services through various built-in applications. It should be noted that in addition to being obtained from terminal devices 101, 102, and 103 via network 104, the data or information required to provide these services can also be pre-stored locally on server 105 in various ways. Therefore, when server 105 detects that this data is already stored locally, it can choose to directly obtain it locally. In this case, exemplary system architecture 100 may also not include terminal devices 101, 102, 103 and network 104.

[0030] Because data analysis and processing may require significant computational resources and high computing power, the methods for identifying overlapping peaks in spectroscopic measurement data provided in the subsequent embodiments of the present invention are generally performed by a server 105 possessing significant computational power and resources. Accordingly, the apparatus for identifying overlapping peaks in spectroscopic measurement data is also generally located within server 105. However, it should also be noted that, if terminal devices 101, 102, and 103 also possess sufficient computational power and resources, terminal devices 101, 102, and 103 may also utilize the relevant applications installed thereon to perform the various computations previously assigned to server 105, thereby outputting the same results as server 105. In particular, in the presence of multiple terminal devices with varying computational capabilities, if the relevant application determines that the terminal device in question possesses significant computational power and abundant remaining computational resources, the aforementioned computations may be performed by the terminal device itself, thereby appropriately alleviating the computational burden on server 105. Accordingly, the apparatus for identifying overlapping peaks in spectroscopic measurement data may also be located within terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104 .

[0031] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0032] Please refer to Figure 2 , Figure 2 A flowchart of a method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention, wherein process 200 includes the following steps:

[0033] Step 201: Acquire target spectroscopy measurement data.

[0034] This step is intended to be performed by the subject of the method for identifying overlapping peaks in spectroscopic measurement data (e.g. Figure 1The server 105 shown in the figure obtains the corresponding target spectroscopic measurement data. In this embodiment, the target spectroscopic measurement data may include at least one or more of a spectrum, an energy spectrum, a chromatography, and a nuclear magnetic resonance spectrum (for example, ultraviolet-visible-near-infrared spectrum, X-ray photoelectron spectroscopy (XPS spectrum), solid-state nuclear magnetic resonance spectrum, etc.), and measurement data generated by various spectroscopic analysis methods can be obtained. The target spectroscopic measurement data includes data of overlapping peaks, which include a main peak and a secondary peak.

[0035] Step 202: Perform peak detection on the target spectral measurement data to obtain a main peak identification result.

[0036] This step is intended to have the aforementioned execution subject perform peak detection on the target spectroscopic measurement data, thereby obtaining a main peak identification result including the main peak. Exemplarily, peak detection may be performed on the spectrum or energy spectrum (e.g., peak detection may be performed through find_peak processing) to obtain the main peak identification result. Exemplarily, when the distance between two peaks of the measurement data is small (e.g., less than 2.3 times the standard deviation of the two peaks, i.e., the distance between the two peaks is less than the Rayleigh criterion), peak detection may only yield a main peak identification result L1 containing a single peak position.

[0037] In some optional implementations of this embodiment, in the spectral measurement data of overlapping peaks, the positional relationship between the main peak and the second peak may be known or preset. In this case, the relative position of the second peak to the main peak may be determined directly based on the positional relationship and the main peak identification result.

[0038] In other optional implementations of this embodiment, for spectroscopic measurement data containing overlapping peaks, the execution entity may identify the relative position of a secondary peak relative to the peak value of the primary peak based on the primary peak identification result. For example, this may be a case of a super-close peak containing two peaks, namely, a primary peak and a secondary peak. In this case, the relative position of the peak value of the secondary peak relative to the peak value of the primary peak is determined.

[0039] In some optional implementations of this embodiment, the execution entity may identify the position of the second peak using an integration window, and determine the relative position based on the position of the second peak and the position of the main peak in the main peak identification result. The process of identifying the peak position using an integration window is a relatively mature technology and will not be further described here.

[0040] Step 203: Determine the independent variable range based on the target spectral measurement data and the main peak identification result, and generate a half-edge measurement result including the main peak peak value.

[0041] In this embodiment, the target spectral measurement data includes independent variables and dependent variables. The execution entity can determine the range of the independent variable based on the target spectral measurement data and the main peak identification result, and further generate a half-edge measurement result including the main peak peak value.

[0042] Step 204: Use a user-defined function to fit the half-edge measurement result to obtain a first fitting function, which includes the main peak position.

[0043] This step allows the executing entity to perform a fitting using a custom function to obtain a first fitting function, which includes parameters such as the main peak position. For example, considering the general distribution patterns of spectra and energy spectra, a pseudo-Voigt function can be used to fit them. The pseudo-Voigt function includes four parameters of the main peak: position, height, width, and Gaussian function ratio. The pseudo-Voigt function equation obtained after fitting is set as F1, and the four parameters are set as p1.

[0044] In some optional implementations of this embodiment, the pseudo Voigt function is in the form of:

[0045] ,

[0046] Where amplitude represents the height of the function, frac represents the proportion of the Gaussian function in the pseudo-Voigt function (a pseudo-Voigt function is a Gaussian function with a certain proportion added to a Cauchy function with another proportion, the sum of the two parts is 1, and both coefficients are not less than 0), center1 represents the position or symmetry center of the main peak, and std is the coefficient that determines the width of the Gaussian function or Cauchy function.

[0047] Step 205: Substitute the global independent variables in the target spectroscopy measurement data into the first fitting function to obtain a first fitting result.

[0048] This step involves the execution subject substituting the global independent variables in the target spectroscopic measurement data into the first fitting function to obtain a first fitting result. Since this first fitting function is based on the half-edge measurement results, the first fitting result obtained by substituting the global independent variables in the target spectroscopic measurement data into this first fitting function is a complete fitting curve containing the main peak, obtained by fitting the half-edge measurement data.

[0049] Step 206: Obtain second peak data based on the target spectral measurement data and the first fitting result.

[0050] In this embodiment, the target spectral measurement data includes main peak data and second peak data, and the execution subject can obtain the second peak data based on the target spectral measurement data and the first fitting result.

[0051] In some optional implementations of this embodiment, the first fitting result mainly includes data of the peak value of the main peak. Therefore, the execution entity can use the target spectral measurement data to subtract the first fitting result to obtain the second peak data.

[0052] Step 207: Fit the second peak data using a user-defined function to obtain a second fitting function, where the second fitting function includes the second peak position.

[0053] This step is intended to have the executing entity use a custom function to fit the second peak data, and the obtained second fitting function contains the position information of the second peak, thereby confirming the main peak position and the second peak position in the target spectral measurement data containing overlapping peaks. Exemplarily, for data such as spectra and energy spectra, a pseudo Voigt function can be used to fit them. The pseudo Voigt function contains four parameters of the secondary peak: position, height, width, and Gaussian function ratio. The pseudo Voigt function equation obtained after fitting is set to F2, and the four parameters are set to p2. Therefore, through the above process, p1 containing the main peak position and p2 containing the second peak position have been obtained.

[0054] The method for identifying overlapping peaks in spectroscopic measurement data provided by the embodiment of the present invention can analyze and process spectroscopic measurement data containing overlapping peaks, and accurately identify peak values such as main peaks and secondary peaks with relatively small data volume and computational complexity, thereby improving the usability of spectroscopic measurement data.

[0055] Please refer to Figure 3 , Figure 3 A flowchart of a method for identifying overlapping peaks in spectral measurement data provided by an embodiment of the present disclosure, namely, for Figure 2 Step 201 in the process 200 shown provides a specific implementation method. The other steps in the process 200 are not adjusted. The specific implementation method provided in this embodiment is replaced by step 201 to obtain a new complete embodiment. The process 300 includes the following steps:

[0056] Step 301: Obtain original spectroscopy measurement data.

[0057] The raw spectroscopic measurement data may be raw data of one or more of a spectrum, an energy spectrum, a chromatography, and a nuclear magnetic resonance spectrum. The raw data may contain noise data and background data. In this embodiment, the noise data and background data in the raw data may be processed.

[0058] Step 302: in response to preset background spectroscopy measurement data, background removal processing is performed on the original spectroscopy measurement data based on the background spectroscopy measurement data to obtain background-removed target spectroscopy measurement data.

[0059] In the case where the original spectral measurement data contains background spectral measurement data, the original spectral measurement data may be subjected to background removal processing (eg, subtracting the background spectral measurement data from the original spectral measurement data) to obtain background-removed target spectral measurement data.

[0060] Step 303: In response to the lack of preset background spectroscopy measurement data, the original spectroscopy measurement data is used as target spectroscopy measurement data.

[0061] In the case where the original spectral measurement data center does not contain background spectral measurement data, the original spectral measurement data is directly used as the target spectral measurement data.

[0062] In some optional implementations of this embodiment, the execution entity may further perform low-pass filtering on the target spectroscopy measurement data to obtain the target spectroscopy measurement data after noise reduction and smoothing.

[0063] Through the above process, interference factors (background data, noise data, etc.) in the original spectral measurement data can be eliminated, further improving the accuracy of analysis and identification based on the target spectral measurement data.

[0064] In some optional implementations of this embodiment, in any of the above embodiments, the process of determining the independent variable range based on the target spectral measurement data and the relative position is mainly based on the target spectral measurement data, and the range between the independent variable limit value on the side opposite to the relative position and the independent variable value of the main peak is determined as the independent variable range. Further, the process includes: if the second peak is between the main peak and the minimum value of the independent variable, then the independent variable value from the peak of the main peak to the maximum value of the independent variable is determined as the independent variable range; if the second peak is between the main peak and the maximum value of the independent variable, then the independent variable value from the minimum value of the independent variable to the peak of the main peak is determined as the independent variable range. For example, if the secondary peak is located on the right side of the main peak (i.e., between the main peak and the maximum value of the independent variable), then the independent variable range is taken as X min ~L1; if the secondary peak is located on the left side of the main peak (i.e., between the main peak and the minimum value of the independent variable), the independent variable range is L1~X max Furthermore, the execution entity may construct the half-edge measurement result based on the data in the target spectroscopy measurement data that conforms to the range of the independent variable.

[0065] Please refer to Figure 4 , Figure 4 A flowchart of another method for identifying overlapping peaks in spectroscopy measurement data provided by an embodiment of the present invention, wherein process 400 includes the following steps:

[0066] Step 401: Acquire target spectroscopy measurement data.

[0067] Step 402: Perform peak detection on the target spectral measurement data to obtain a main peak identification result.

[0068] Step 403: Determine the independent variable range based on the target spectral measurement data and the main peak identification result, and generate a half-edge measurement result including the main peak peak value.

[0069] Step 404: Use a user-defined function to fit the half-edge measurement result to obtain a first fitting function, which includes the main peak position.

[0070] Step 405: Substitute the global independent variables in the target spectroscopy measurement data into the first fitting function to obtain a first fitting result.

[0071] Step 406: Obtain second peak data based on the target spectral measurement data and the first fitting result.

[0072] Step 407: Fit the second peak data using a user-defined function to obtain a second fitting function, where the second fitting function includes the second peak position.

[0073] The above steps 401-407 are similar to Figure 2 Steps 201-207 shown are consistent. For the same contents, please refer to the corresponding parts of the previous embodiment and will not be repeated here.

[0074] Step 408: Substitute the global variables in the target spectroscopy measurement data into the sum function of the first fitting function and the second fitting function to obtain a third fitting result, which includes the main peak position and the second peak position.

[0075] In some optional implementations of this embodiment, the sum function is in the form of:

[0076] ,

[0077] There are 4 parameters in total , frac, center and std, where intensity represents the intensity of the signal, number 1 represents the main peak signal, number 2 represents the second peak signal (such as the secondary peak), amplitude represents the maximum value of the signal intensity, frac represents the proportion of Gaussian signals in the signal, center represents the symmetry center of the signal, and std represents the width parameter of the signal.

[0078] Through the above process, the positions of the main peak and the second peak are obtained by fitting using the sum function, and the obtained results are more accurate.

[0079] In order to deepen understanding, the present invention also provides a specific implementation scheme in combination with a specific application example, see Figures 5A-5E As shown. In this specific application example, a spectrum containing a main peak and a secondary peak is used as an example for explanation. However, as mentioned above, the method for identifying overlapping peaks in spectroscopic measurement data provided by this embodiment can be used to identify overlapping peaks in spectroscopic measurement data (one or more of a spectrum, an energy spectrum, a chromatogram, and a nuclear magnetic resonance spectrum), and is not limited to the spectrum. For the spectrum, the horizontal axis x in the figure represents the wavelength, and the vertical axis y represents the intensity; for the energy spectrum, the horizontal axis x in the figure represents the energy, and the vertical axis y represents the intensity; for the chromatogram, the horizontal axis x in the figure represents the time, and the vertical axis y represents the intensity / concentration; for the nuclear magnetic resonance, the horizontal axis x in the figure represents the nuclear magnetic shift, and the vertical axis y represents the intensity.

[0080] Step 1: Collect spectral data (generate spectrum S1). The generated spectrum takes an example where the distance between the two peaks is just on the limit of the Rayleigh criterion (the standard deviations of the two peaks are the same, and the peak distance is 2.35482 times the standard deviation). Figure 5A When the background spectrum is included, spectrum S2 is obtained by subtracting the background spectrum from spectrum S1; if the background spectrum is not included, spectra S1 and S2 are the same.

[0081] Step 2: Perform low-pass filtering on spectrum S1 (S2) to obtain spectrum S3, with the cutoff frequency set to 0.06, as shown in Figure 5B shown.

[0082] Step 3: Low-pass filter the spectrum S3 to obtain the peak position 500.4. Identify the relative position of the secondary peak and the main peak, and find that the secondary peak is located to the right of the main peak. At this time, select the minimum value of the independent variable until the peak position to construct the half-spectrum S4, as shown in Figure 5C shown.

[0083] Step 4: Select the minimum value of the independent variable until the peak position, perform pseudo Voigt fitting on this interval, and obtain the fitting equation F1 and parameter p1. The peak position corresponding to p1 is 500.0829, which is the main peak position.

[0084] Step 5: Substitute all the independent variables of spectrum S1 into equation F1 to obtain the half-edge fitting spectrum S5, as shown Figure 5D shown.

[0085] Step 6: Subtract the half-edge fitting spectrum S5 from the spectrum S1 (S2) to obtain the spectrum S6 consisting of the secondary peak and noise, as shown in Figure 5E shown.

[0086] Step 7: Perform pseudo Voigt function fitting on spectrum S6 to obtain equation F2 and parameter p2. The peak position corresponding to p2 is 524.1294, which is the secondary peak position.

[0087] Step 8: Substitute p1 and p2 as initial guesses into a two-peak pseudo-Voigt function (the sum of two pseudo-Voigt functions) and fit spectrum S2. This yields fitting equation F3 and parameter p3, which corresponds to the peak positions of 499.8704 and 523.5098. The resulting function yields peak positions of 500.0000 and 523.5482, respectively, which closely match the fitting result. In particular, the error for the secondary peak is reduced from +0.5812 to -0.0384, a mere -6.6% of the error in step 7.

[0088] Further references Figure 6 As an implementation of the methods shown in the above figures, the present invention provides an embodiment of a device for identifying overlapping peaks in spectral measurement data. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0089] like Figure 6 As shown, the device 600 for identifying overlapping peaks in spectral measurement data of this embodiment may include: a target data acquisition module 601, a main peak identification result generation module 602, a half-edge measurement result generation module 603, a first fitting module 604, a first fitting result generation module 605, a second peak data generation module 606 and a second fitting module 607. Among them, the target data acquisition module 601 is configured to acquire target spectroscopic measurement data; the main peak identification result generation module 602 is configured to perform peak detection on the target spectroscopic measurement data to obtain a main peak identification result; the half-edge measurement result generation module 603 is configured to determine the independent variable range based on the target spectroscopic measurement data and the relative position, and generate a half-edge measurement result including the peak value of the main peak; the first fitting module 604 is configured to use a custom function to fit the half-edge measurement result to obtain a first fitting function, which includes the main peak position; the first fitting result generation module 605 is configured to substitute the global independent variable in the target spectroscopic measurement data into the first fitting function to obtain a first fitting result; the second peak data generation module 606 is configured to obtain second peak data based on the target spectroscopic measurement data and the first fitting result; the second fitting module 607 is configured to use a custom function to fit the second peak data to obtain a second fitting function, which includes the second peak position.

[0090] In this embodiment, the specific processing and technical effects of the target data acquisition module 601, the main peak recognition result generation module 602, the half-edge measurement result generation module 603, the first fitting module 604, the first fitting result generation module 605, the second peak data generation module 606 and the second fitting module 607 in the device for identifying overlapping peaks in spectral measurement data 600 can be referred to respectively. Figure 2 The relevant descriptions of steps 201-207 in the corresponding embodiment are not repeated here.

[0091] This embodiment exists as an apparatus embodiment corresponding to the above-mentioned method embodiment. The apparatus for identifying overlapping peaks in spectroscopic measurement data provided by this embodiment can analyze and process spectroscopic measurement data containing overlapping peaks, and accurately identify peak values such as the main peak and secondary peak through relatively small data volume and computational complexity, thereby improving the availability of spectroscopic measurement data.

[0092] According to an embodiment of the present invention, the present invention also provides a device for identifying overlapping peaks in spectroscopic measurement data, and the device for identifying overlapping peaks in spectroscopic measurement data includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that when the at least one processor executes, it can implement the method for identifying overlapping peaks in spectroscopic measurement data described in any of the above embodiments.

[0093] According to an embodiment of the present invention, the present invention further provides a readable storage medium storing computer instructions, which are used to enable a computer to implement the method for identifying overlapping peaks in spectral measurement data described in any of the above embodiments when executed.

[0094] According to an embodiment of the present invention, the present invention further provides a computer program product, which, when executed by a processor, can implement the method for identifying overlapping peaks in spectroscopy measurement data described in any of the above embodiments.

[0095] Figure 7 A schematic block diagram of an apparatus 700 for identifying overlapping peaks in example spectroscopy measurement data that can be used to implement an embodiment of the present invention is shown. Electronic equipment 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. Electronic equipment can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0096] like Figure 7 As shown, device 700 includes a computing unit 701, which 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. RAM 703 may also store various programs and data required for the operation of device 700. Computing unit 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0097] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0098] The computing unit 701 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the method for identifying overlapping peaks in spectroscopic measurement data. For example, in some embodiments, the method for identifying overlapping peaks in spectroscopic measurement data can be implemented as a computer software program tangibly embodied 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 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 for identifying overlapping peaks in spectroscopic measurement data described above can be performed. Alternatively, in other embodiments, the computing unit 701 may be configured in any other appropriate manner (eg, by means of firmware) to execute the method for identifying overlapping peaks in spectroscopic measurement data.

[0099] An embodiment of the present invention further 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 using the overlapping peak recognition method described in any of the above embodiments.

[0100] An embodiment of the present invention further provides a thinning control method, which is executed by an electronic device such as a computer or server and specifically comprises: obtaining a wafer thickness in a current frame according to the thickness measurement method described in any of the above embodiments; and stopping wafer thinning when the wafer thickness in the current frame reaches a preset thickness. The preset thickness is a thickness value set according to thinning requirements.

[0101] This embodiment utilizes a precise thickness measurement method to obtain the wafer thickness of the current frame, providing real-time, accurate thickness data for the thinning process. Automatically stopping the thinning operation when the wafer thickness reaches a preset value effectively avoids over-thinning or under-thinning, ensuring that the final wafer thickness precisely meets the expected standard, thereby improving the quality and consistency of the wafer thinning process.

[0102] An embodiment of the present invention further provides a thinning machine for performing thinning processing on a wafer and executing the method described in any of the above embodiments during the thinning process.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for identifying overlapping peaks in spectral measurement data, characterized in that: include: Acquiring target spectroscopy measurement data; wherein the target spectroscopy measurement data includes data of the overlapping peaks, and the overlapping peaks include a main peak and a second peak; Performing peak detection on the target spectroscopy measurement data to obtain a main peak identification result; Determining an independent variable range based on the target spectral measurement data and the main peak identification result, and generating a half-edge measurement result including the main peak peak value; Fitting the half-edge measurement result using a custom function to obtain a first fitting function, wherein the first fitting function includes a main peak position; Substituting the global independent variables in the target spectroscopy measurement data into the first fitting function to obtain a first fitting result; Obtaining second peak data based on the target spectral measurement data and the first fitting result; The second peak data is fitted using the user-defined function to obtain a second fitting function, wherein the second fitting function includes the second peak position.

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

3. The method according to claim 2, characterized in that The acquiring of target spectroscopy measurement data further includes: The target spectroscopy measurement data is subjected to low-pass filtering to obtain denoised and smoothed target spectroscopy measurement data.

4. The method according to claim 1, wherein The determining of the independent variable range based on the target spectral measurement data and the main peak identification result, and generating a half-edge measurement result including the main peak peak value, comprises: Determining the relative position of the second peak to the main peak based on the preset positional relationship between the main peak and the second peak and the main peak identification result; The independent variable range is determined based on the target spectral measurement data and the relative position, and a half-edge measurement result including the peak value of the main peak is generated.

5. The method according to claim 1, wherein The determining of the independent variable range based on the target spectral measurement data and the main peak identification result, and generating a half-edge measurement result including the main peak peak value, comprises: Identifying the relative position of the second peak to the main peak based on the main peak identification result; The independent variable range is determined based on the target spectral measurement data and the relative position, and a half-edge measurement result including the peak value of the main peak is generated.

6. The method according to claim 5, characterized in that The identifying the relative position of the second peak and the main peak based on the main peak identification result includes: identifying a position of the second peak using an integration window; The relative position is determined based on the position of the second peak and the main peak position in the main peak identification result.

7. The method according to claim 4, characterized in that The determining of the independent variable range based on the target spectroscopy measurement data and the relative position includes: Based on the target spectroscopic measurement data, a range between an independent variable limit value on the side opposite to the relative position and the independent variable value of the main peak peak is determined as the independent variable range.

8. The method according to claim 7, characterized in that Determining the range between the independent variable limit value on the side opposite to the relative position and the independent variable value of the main peak peak as the independent variable range includes: In response to the second peak being located between the main peak and the minimum value of the independent variable, determining the independent variable range to be the independent variable value of the main peak to the maximum value of the independent variable; In response to the second peak being located between the main peak and the maximum value of the independent variable, the independent variable range is determined to be the independent variable values from the minimum value of the independent variable to the peak value of the main peak.

9. The method according to claim 7, characterized in that Generate a half-edge measurement result containing the peak value of the main peak, including: The half-edge measurement result is constructed based on data in the target spectroscopy measurement data that meets the range of the independent variable.

10. The method according to claim 1, characterized in that The obtaining of second peak data based on the target spectral measurement data and the first fitting result includes: The target spectral measurement data is used to subtract the first fitting result to obtain the second peak data.

11. The method according to any one of claims 1 to 10, characterized in that: Also includes: The global variables in the target spectroscopy measurement data are substituted into the sum function of the first fitting function and the second fitting function to obtain a third fitting result, wherein the third fitting result includes a main peak position and a second peak position.

12. A thickness measurement method, characterized in that: include: Obtaining the primary peak value and the secondary peak value of the current frame using the method described in any one of claims 1 to 11; The wafer thickness of the current frame is calculated according to the main peak-to-peak value and the second peak-to-peak value.

13. A thinning control method, characterized in that: include: Obtaining the wafer thickness of the current frame according to the method of claim 12; When the wafer thickness of the current frame reaches a preset thickness, wafer thinning is stopped.

14. A device for identifying overlapping peaks in spectral measurement data, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the method for identifying overlapping peaks in spectroscopic measurement data as described in any one of claims 1 to 11.

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

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

17. A thinning machine, characterized in that: Used to perform thinning processing on a wafer, and perform the method according to any one of claims 1 to 13 during the thinning process.

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