Background signal extraction method and device

Abstract

The invention provides a background signal extraction method and equipment. The method is applied to the high-precision measurement field. The method comprises the following steps: acquiring a plurality of groups of actually measured signal data of a plurality of measured samples, wherein each group of actually measured signal data is acquired based on the same measurement condition; parameter equation fitting is carried out on each group of actually measured signal data, a parameter numerical solution of each group of actually measured signal data is obtained, and the parameter numerical solution comprises height information, width information, position information and shape information of each peak; determining a peak of a background signal originating from the measurement condition according to each set of the parameter numerical solutions; and obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition. According to the invention, the accuracy of background signal extraction is improved.

Description

Technical Field

[0001] The present invention relates to the field of high-precision measurement, and in particular to a background signal extraction method and device. Background Art

[0002] Signal analysis is an important method in modern scientific research and industrial production, and is widely used in chemical analysis, material science, environmental monitoring and other fields. The measured signal plays a vital role in signal analysis. As the basic data for analysis, it usually covers three components: the true signal of the sample, background error and random error. The true signal of the sample reflects the unique signal characteristics of the substance and is the main target of signal analysis. However, in the actual measurement process, the introduction of background error and random error will affect the accuracy and reliability of the signal data, and will also affect the extraction and fitting of the true signal.

[0003] In the prior art, although the important influence of background error on the accuracy of signal analysis has been recognized, in actual operation, the extraction of the signal distribution pattern of background error still faces many challenges. Traditional processing methods often have difficulty in accurately distinguishing between real signals and background errors, resulting in the loss of some real signal information while eliminating the error, thereby reducing the accuracy and reliability of signal analysis. Therefore, developing a method that can accurately extract the background error signal distribution pattern is of urgent need and great significance for improving the overall accuracy and reliability of signal analysis. Summary of the invention

[0004] In view of this, the present invention provides a background signal extraction method, comprising: Acquire multiple groups of measured signal data of multiple samples, each group of measured signal data is collected based on the same measurement conditions; Performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak; Determining the peak of the background signal originating from the measurement conditions according to the numerical solution of each group of the parameters; Based on the determined peak of the background signal originating from the measurement condition, the background signal of the measurement condition is obtained.

[0005] Optionally, determining the peak of the background signal originating from the measurement condition according to the numerical solution of each group of the parameters comprises: For each group of measured signal data, determine whether any peak in the measured signal data has a similar peak in other measured signal data; If similar peaks exist in other measured signal data, it is determined that the peak is a peak of a background signal originating from the measurement condition.

[0006] Optionally, determining the peak of the background signal originating from the measurement condition according to the numerical solution of each group of the parameters comprises: Performing cluster analysis on the parameter numerical solution of each peak to determine whether at least one set of peaks with similar parameters is obtained; If a collection of peaks with similar parameters is not obtained, the background signal is not identified; If at least one set of peaks with similar parameters is obtained, each peak in each set originates from the background signal of the measurement condition.

[0007] Optionally, obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: Select any one of the multiple groups of measured signal data; The peak of the background signal originating from the measurement condition is determined in the selected measured signal data, and the background signal of the measurement condition is obtained by using a corresponding parameter numerical solution.

[0008] Optionally, obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: Based on the peak of the background signal originating from the measurement condition, respectively extracting background signals from a plurality of groups of measured signal data; A comprehensive operation is performed on multiple groups of background signals to obtain the background signals of the measurement conditions, wherein the comprehensive operation includes taking an average or a median.

[0009] Optionally, obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: A cluster center calculation is performed on the peak of at least one frame of the background signal originating from the measurement condition to obtain the background signal of the measurement condition.

[0010] The second aspect of the present invention also provides a background signal extraction method, comprising: Acquire at least one set of measured signal data without placing the measured sample, each set of the measured signal data is collected based on the same measurement conditions; Performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak; Based on the parameter numerical solution of each set of measured signal data, the background signal of the measurement condition is obtained.

[0011] Optionally, the obtaining of the background signal of the measurement condition by solving the parameter numerical solution of each group of measured signal data comprises: Select any one of the multiple groups of measured signal data; The background signal of the measurement condition is obtained by using the parameter numerical solution of the selected measured signal data.

[0012] Optionally, the obtaining of the background signal of the measurement condition by solving the parameter numerical solution of each group of measured signal data comprises: Using the parameter numerical solutions of the multiple groups of measured signal data, the background signals corresponding to the measurement conditions are obtained; A comprehensive operation is performed on multiple groups of background signals to obtain the background signals of the measurement conditions, wherein the comprehensive operation includes taking an average or a median.

[0013] Optionally, the obtaining of the background signal of the measurement condition by solving the parameter numerical solution of each group of measured signal data comprises: Cluster center calculation is performed on parameter numerical solutions of multiple groups of measured signal data to obtain background signals under the measurement conditions.

[0014] Optionally, parameter equation fitting is performed for each set of measured signal data, including: Acquire a mathematical model adapted to the measured signal data, wherein the mathematical model includes a multi-pseudo Viogt model or a multi-pseudo Viogt and straight line combined mathematical model; The mathematical model and the measured signal data are used to obtain a fitting equation for the measured signal data.

[0015] Optionally, if the mathematical model adapted to the measured signal data is a mathematical model combining multiple pseudo Viogts and straight lines, a straight line segment and a first peak segment and a second peak segment located closest to both sides of the straight line segment are intercepted from the measured signal data; Performing single pseudo Viogt peak fitting on the first peak segment and the second peak segment respectively to obtain a first pseudo Viogt function and a second pseudo Viogt function; Obtaining the vertices of the first pseudo Viogt function and the second pseudo Viogt function, and connecting the two vertices; Calculating a sum function according to the first pseudo Viogt function and the second pseudo Viogt function; Using the coordinates of the connecting line and the multiple intersection points of the sum function to connect the line again, a target straight line is obtained, and it is determined whether the target straight line is a tangent line of the peak point of the sum function; If the target straight line is a tangent line of the peak point of the sum function, two peak segments of the sum function are intercepted using target horizontal coordinates of multiple intersection points, where the target horizontal coordinates are calculated by averaging the horizontal coordinates of multiple intersection points; The fitting equation of the background signal is obtained by calculation based on the intercepted data and the target straight line.

[0016] Optionally, using the coordinates of the connecting line and the plurality of intersection points of the sum function to connect the line again to obtain a target straight line, and determining whether the target straight line is a tangent to the peak point of the sum function, comprises: According to the target abscissa and its corresponding ordinate, a plurality of target intersection points are obtained; Connecting a plurality of target intersection points to obtain a target straight line; Calculate the error between the target straight line and the ordinate of the sum function at a certain abscissa and make an error judgment. If the error is less than a preset error, the target straight line is regarded as a tangent to the peak point of the sum function. If the error is greater than the preset error, continue to iterate the connection based on the intersection of the target straight line and the sum function until the error between the vertical coordinate of the iterative target straight line and the sum function at a certain horizontal coordinate is less than the preset error, and the target straight line is regarded as the tangent of the peak point of the sum function.

[0017] A third aspect of the present invention provides a signal processing method, comprising: Obtaining a background signal according to any of the above background signal extraction methods; The background signal is subtracted from the actual measured signal corresponding to the sample being tested to obtain the real signal of the sample being tested.

[0018] The fourth aspect of the present invention provides a background signal extraction 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 so that the processor performs the above-mentioned background signal extraction method.

[0019] A fifth aspect of the present invention provides a signal processing device, which includes: 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 above-mentioned signal processing method.

[0020] The present invention obtains multiple groups of measured signal data of different samples under the same measurement conditions in the data acquisition stage, which ensures that the data is both diverse and representative, and provides a rich and reliable data basis for subsequent analysis. Since the measured signal data contains the real signal of the sample, background error and random error, it is necessary to extract the background signal later. In the parameter equation fitting link, by fitting each group of measured signal data, a parameter numerical solution containing the height, width, position and shape information of each peak is obtained, which can express the signal characteristics in detail. Then, according to the parameter numerical solution, the peak information of the background signal corresponding to the error that does not change with the sample can be found, and the characteristics of the background error can be accurately located. Finally, the background signal is obtained according to the peak information of the background signal, and the accuracy of the background error extraction is achieved, which creates conditions for the subsequent elimination of background errors and more accurately identifying and processing the real signal of the sample, thereby significantly improving the accuracy and reliability of signal analysis.

[0021] The present invention directly obtains background signal data without placing a sample, collects at least one set of signal data under the same measurement conditions, and then performs parameter equation fitting, and obtains the background signal based on the solution of the parameter equation. Directly obtaining signal data without placing a sample can eliminate the interference of the sample signal on the background signal. The obtained signal data purely reflects the background information generated by the measurement conditions. Moreover, directly obtaining the signal data without placing a sample only requires performing parameter equation fitting on these data to obtain the background signal, which greatly simplifies the extraction process of the background signal and improves work efficiency. In addition, obtaining at least one set of measured signal data ensures that the background signal can be effectively obtained under limited data, and when obtaining multiple sets of measured signal data, the obtained background signal can be made smoother. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of a background signal extraction method in an embodiment of the present invention; Figure 2 A spectrum is obtained by fitting a parameter equation for each set of measured spectrum data in the embodiment of the present invention; Figure 3 It is a peak center extraction diagram in an embodiment of the present invention; Figure 4 is a clustering set diagram in an embodiment of the present invention; Figure 5It is a three-pseudo Viogt model diagram in an embodiment of the present invention; Figure 6 It is a measured signal diagram of double pseudo Viogt peaks plus a straight line equation in an embodiment of the present invention; Figure 7 is a flow chart of a signal processing method in an embodiment of the present invention; Figure 8 The figure is a flow chart of another background signal extraction method in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. Embodiment 1

[0026] like Figure 1 As shown, an embodiment of the present invention provides a background signal extraction method. The method extracts the background signal by means of a sample. The method is executed by an electronic device such as a computer or a server, and specifically includes: S11, obtaining multiple groups of measured signal data of multiple samples, each group of measured signal data is collected based on the same measurement conditions.

[0027] The sample to be tested can be any substance, and the signal can be any one-dimensional, single-peak or multi-peak data such as spectrum, chromatography, energy spectrum, nuclear magnetic resonance, etc. The measuring equipment is used to collect the signal of the sample to be tested placed on the measuring table. Specifically, different samples are placed separately under the same measurement conditions to collect multiple sets of measured signal data. For example, when the signal is a spectrum, the measuring device is a spectrometer that can collect spectra. The measurement conditions cover all customizable input parameters such as specific light source intensity, wavelength range, measurement angle, ambient temperature and humidity, resolution, etc. to ensure the diversity and representativeness of the data. The measured signal data is a signal with wavelength or pixel as the horizontal axis and light intensity as the vertical axis.

[0028] The measured signal data contains random errors, background errors and the real signals of the samples. Background errors refer to errors that occur in every measurement and do not change with the measurement conditions and samples; random errors refer to errors that occur accidentally during the measurement process.

[0029] S12, performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak.

[0030] Parametric equation fitting can be implemented using existing mathematical processing programs, such as Python's scipy library and Originlab, which have parametric equation fitting functions. By fitting each set of measured signal data with parametric equations, the numerical solution of a set of measured signal data can contain a variety of information about multiple peaks, such as the height, width, position and shape of the peaks. Since random errors are usually high-frequency signals, they are clearly distinguished from background errors and real signals. Based on the characteristics of parametric equation fitting, certain random errors can be eliminated in this step.

[0031] S13, determining the peak of the background signal originating from the measurement conditions according to the numerical solutions of each set of parameters.

[0032] Since the parameter numerical solution includes the height information, width information, position information and shape information of each peak, and each set of measured signal data is collected based on the same measurement conditions, this ensures the relative stability of the background error, that is, the background signal belonging to the background error is relatively stable. If the measured samples are different, the parameter numerical solutions corresponding to the real signals belonging to different measured samples will be quite different. Therefore, based on the parameter numerical solutions, it is possible to analyze which parameter numerical solutions belonging to the background signals are quite stable, and which parameter numerical solution signals do not belong to the background signals.

[0033] S14, obtaining a background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition.

[0034] Since the background signal is composed of peaks belonging to background errors in the signal, once the peaks of the background signal originating from the measurement conditions are obtained, the background signal can be obtained based on the peak information belonging to the background signal.

[0035] This embodiment obtains multiple groups of measured signal data of different samples under the same measurement conditions in the data acquisition stage, which ensures that the data is both diverse and representative, and provides a rich and reliable data basis for subsequent analysis. Since the measured signal data contains the real signal of the sample, background error and random error, it is necessary to extract the background signal later. In the parameter equation fitting link, by fitting each group of measured signal data, a parameter numerical solution containing each peak height, width, position and shape information is obtained, which can express the signal characteristics in detail. Then, according to the parameter numerical solution, the peak information of the background signal corresponding to the error that does not change with the sample can be found, and the characteristics of the background error can be accurately located. Finally, the background signal is obtained according to the peak information of the background signal, and the accuracy of the background error extraction is achieved, which creates conditions for the subsequent elimination of background errors and more accurately identifying and processing the real signal of the sample, thereby significantly improving the accuracy and reliability of signal analysis.

[0036] In one embodiment, step S13 includes: S131a, for each group of measured signal data, determine whether any peak in the measured signal data has a similar peak in other measured signal data.

[0037] S132a: If similar peaks exist in other measured signal data, it is determined that the peak is a peak of a background signal originating from the measurement conditions.

[0038] Since the background error has the characteristic of not changing with the measurement conditions and samples, this means that its corresponding signal will show consistency in multiple sets of measured signal data, that is, it will appear as the same peak. Therefore, by finding the unchanging peak in all measured signals, the peak of the background signal originating from the measurement conditions can be accurately located.

[0039] This embodiment makes full use of the characteristic that the background error does not change with the measurement conditions and samples. By judging the similarity of the peaks in each group of measured signal data in other measured signal data, the peaks corresponding to the background error can be accurately screened out, which helps to accurately distinguish the true signal of the sample from the background error and avoid the interference of the background error on the true signal of the sample, thereby significantly improving the accuracy of the subsequent analysis of the true signal of the sample.

[0040] In another embodiment, step S13 further includes: S131b, performing cluster analysis on the parameter numerical solution of each peak to determine whether at least one set of peaks with similar parameters is obtained.

[0041] S132b, if a set of peaks with similar parameters is not obtained, the background signal is not identified.

[0042] S133b, if at least one set of peaks with similar parameters is obtained, each peak in each set originates from the background signal of the measurement condition.

[0043] There is another method to extract the peaks of the background signal, which is to perform cluster analysis on the numerical solution of the parameters of each peak and use the clustering characteristics of the data to identify the peaks of the background signal. Since the characteristics of the peaks in the actual signal data are often complex and changeable, there may be certain similarities between different peaks but they are not exactly the same. Cluster analysis can comprehensively consider the similarities between peaks based on multiple parameters (such as peak height, peak width, peak position, peak shape, etc.), and classify peaks with similar characteristics into one category to form a set. Even if the parameters of the peaks are not exactly the same, as long as they are similar within a certain range, they can be clustered together, thereby more comprehensively and accurately identifying a set of peaks with similar characteristics.

[0044] like Figure 2 After fitting the parameter equation for each set of measured spectral data, a spectrum is obtained, with the horizontal axis being the wavelength and the vertical axis being the maximum signal intensity. Cluster analysis is then performed based on the spectral information, that is, the peak center is first extracted based on the parameter numerical solution (e.g. Figure 3 As shown in Figure 2), we then analyze the similarity between the extracted peak centers and group the peaks with similar features into one category, and we can get the following: Figure 4 Three sets in total.

[0045] This embodiment can deeply explore the relationship between peaks in signal data by clustering analysis of the parameter numerical solution of each peak. If there is a collection of peaks with similar parameters, it means that these peaks have similar characteristics and are most likely generated by the same background error factors. Cluster analysis has good adaptability. It can dynamically cluster according to the actual distribution of data. Even if there are certain fluctuations in the parameters of the background signal peaks, they can be effectively clustered together to improve the stability and comprehensiveness of background signal recognition.

[0046] Furthermore, the background signal of the measurement condition in step S14 may be obtained in a variety of ways, as follows: As a first implementation, step S14 includes: S141a, selecting any one of the multiple groups of measured signal data.

[0047] S142a, determining the peak of the background signal originating from the measurement condition in the selected measured signal data, and obtaining the background signal of the measurement condition using a corresponding parameter numerical solution.

[0048] Since multiple groups of measured signals contain background signals, through the method of this embodiment, it is convenient and effective to arbitrarily select a measured signal from multiple groups of measured signal data, and obtain the background signal according to the parameter numerical solution of the group of measured signals. In this process, it is only necessary to select one group of measured signal data from multiple groups of measured signal data, and then based on the peak of the background signal derived from the measurement conditions and its parameter numerical solution, the background signal can be successfully obtained. In multiple groups of measured signal data, there is no need to process the peak of the background signal of each group of measured signals. It is only necessary to arbitrarily select one measured signal data, and the background signal can be determined based on the peak of the identified background signal and its parameter numerical solution. This greatly reduces the workload and time cost of data processing, especially when processing a large amount of signal data, it can significantly improve the efficiency of obtaining background signals.

[0049] As a second implementation, step S14 includes: S141b, based on the peak of the background signal originating from the measurement condition, respectively extract the background signal from the plurality of groups of measured signal data.

[0050] S142b, performing comprehensive operations on multiple groups of background signals to obtain background signals under measurement conditions, wherein the comprehensive operations include taking an average or a median.

[0051] Specifically, multiple groups of background signals are obtained based on the peak data of the background signal. For all background signals, all light intensity values ​​corresponding to the same wavelength are comprehensively calculated, and the average light intensity value or median light intensity of multiple groups of background signals at each wavelength point is obtained by taking the average and median. The final background signal is obtained based on the average light intensity value or median light intensity and the corresponding wavelength.

[0052] In this embodiment, the background signal extracted from the measured signal data of different samples under the same measurement conditions may have random errors caused by differences in sample characteristics, accidental measurement factors, etc. Each set of data may be interfered by random errors to varying degrees during the measurement process. By performing comprehensive operations on the peaks of multiple sets of background signals (taking the average or median), these random errors will offset each other to a certain extent. The quality of the background signal can be significantly improved, random noise can be effectively reduced, measurement errors can be balanced, and interference from outliers can be weakened, making the background signal closer to the actual situation.

[0053] As a third implementation, step S14 includes: S141c, performing cluster center calculation on the peak of at least one frame of background signal originating from the measurement condition to obtain the background signal of the measurement condition.

[0054] This embodiment only processes the peak data of one or more frames of background signals, and classifies peaks with similar characteristics into different clusters through cluster analysis, finds the typical characteristics of each cluster (cluster center), and thus fits the background signal more accurately. This embodiment only selects part of the peak data, which reduces the complexity and improves the processing rate. In addition, compared with the single processing of a frame of data, cluster analysis can mine the inherent structure and pattern of the data. Single processing is easily affected by accidental factors in the data, and this embodiment can integrate multiple groups of data information through clustering, improve the anti-interference ability and stability of the results, and obtain a more accurate background signal.

[0055] In one embodiment, in step S12, parameter equation fitting is performed for each set of measured signal data, specifically including: S121, obtaining a mathematical model adapted to the measured signal data, wherein the mathematical model includes a multi-pseudoViogt model or a multi-pseudoViogt and straight line combined mathematical model; S122, using a mathematical model and the measured signal data to obtain a fitting equation for the measured signal data.

[0056] Since the measured signal data obtained may contain random noise, the existing technology usually adopts methods such as multi-frame averaging, multi-frame median, moving average smoothing, filtering and noise reduction to remove random noise. However, considering the influence of intermediate frequency information such as power supply ripple, the existing technology is unable to eliminate the intermediate frequency noise in the measured signal. Although smoothing and denoising it can reduce the noise of the data, it will also cause the smoothed result to deviate from the original data.

[0057] Therefore, in this embodiment, the signal is matched by fitting a parameter equation, and a suitable model is selected according to the specific presentation form of the measured signal. For example, if the measured signal contains multiple pseudo Viogt peaks and random noise, a multi-pseudo Viogt model is selected; if the measured signal contains multiple pseudo Viogt peaks and a signal of a straight line equation, a multi-pseudo Viogt combined with a straight line mathematical model is selected. In this way, high-frequency noise and intermediate-frequency noise are completely screened out.

[0058] Among them, each pseudo Viogt expressed a peak, and the expression of each pseudo Viogt was: ; in, Indicates the strength of the signal. Indicates the maximum value of signal strength. Indicates the proportion of Gaussian signals in the signal, represents the wavelength of the signal, represents the symmetry center of the signal, Represents the width parameter of the signal.

[0059] Multi-pseudo Viogt is the superposition of the expressions of multiple pseudo Viogt.

[0060] The equation of a straight line is: Y=a*X+b; Among them, Y represents the intensity of the signal, X represents the wavelength of the signal, and a and b are constants.

[0061] For the multi-pseudo Viogt model, it achieves a lower root mean square error by fitting the parametric equation to the measured signal data, and appropriately adjusts the parameter range and the number of peaks to improve the accuracy of the fitting equation and better reflect the laws of the measured signal data. Considering the robustness, versatility and overfitting risk of the model, the number of peaks should not be too many. Select different numbers of peaks to fit the parametric equation and solve the RMSE. If the number of peaks reaches a certain value and then increases the number of peaks, the RMSE does not increase significantly, then the number of peaks should be stopped at this time. It can be seen from experiments that, for example, Figure 5 As shown, when the number of peaks = 3, the fitting equation (red curve) can already better reflect the law of the spectral data (blue curve), while the number of peaks = 4 has only a slight improvement. At this time, the three-peak equation can be selected for fitting, that is, the three-pseudo Viogt model is the best.

[0062] Furthermore, if the mathematical model adapted to the measured signal data is a mathematical model combining multiple pseudo Viogts and straight lines, a straight line segment and a first peak segment and a second peak segment located closest to both sides of the straight line segment are intercepted from the measured signal data; Performing single pseudo Viogt peak fitting on the first peak segment and the second peak segment respectively to obtain a first pseudo Viogt function and a second pseudo Viogt function; Get the vertices of the first pseudo Viogt function and the second pseudo Viogt function, and connect the two vertices; Calculate a sum function based on the first pseudo Viogt function and the second pseudo Viogt function; Use the coordinates of the connecting line and the multiple intersection points of the sum function to connect the line again to obtain the target straight line, and determine whether the target straight line is the tangent line of the peak point of the sum function; If the target straight line is the tangent line of the peak point of the sum function, the target horizontal coordinates of multiple intersection points are used to intercept the two peak segments of the sum function, and the target horizontal coordinates are calculated by averaging the horizontal coordinates of multiple intersection points; The fitting equation of the background signal is calculated based on the intercepted data and the target straight line.

[0063] Specifically, taking the measured signal in the form of a double pseudo-Viogt peak plus a linear equation as an example, as Figure 6 shown by the blue curve in the figure, first intercept the first peak segment A, the linear segment B, and the second peak segment C, then perform single pseudo-Viogt peak fitting on the first peak segment A and the second peak segment C respectively to obtain the first pseudo-Viogt function and the second pseudo-Viogt function, calculate the sum function of the two functions, and connect the vertices of the two pseudo-Viogt functions. As long as there is a difference in the height of the pseudo-Viogt functions, the connection line will surely pass through the sum function of the two pseudo-Viogt functions 4 times. Connect the line again according to the abscissa of the intersection point and the corresponding ordinate of the sum function to obtain the target connection line. Only need to judge whether the target connection line is the tangent line of the peak point of the sum function. If it is the tangent line, take the abscissas of the 4 intersection points, which are x1, x2, x3, and x4 respectively, take x5 = (x1 + x2) / 2, x6 = (x3 + x4) / 2 to obtain the target abscissas x5 and x6. Intercept the peak segment of the sum function according to x5 and x6, that is, take a peak segment in the range of x ≤ x5 and take a peak segment in the range of x ≥ x6, and the target straight line range is x5 < x < x6. Finally, process the two peak segments and the target straight line to obtain the fitting equation of the background signal. The actual background signal is shown as Figure 6 the orange curve in the figure.

[0064] Furthermore, using the coordinates of multiple intersection points of the connection line and the sum function to connect the line again to obtain the target straight line, and judging whether the target straight line is the tangent line of the peak point of the sum function includes: Obtain multiple target intersection points according to the target abscissas and their corresponding ordinates; Connect the multiple target intersection points to obtain the target straight line; Calculate the error between the ordinate of the target straight line and the sum function at a certain abscissa and perform error judgment. If the error is less than the preset error, regard the target straight line as the tangent line of the peak point of the sum function; If the error is greater than the preset error, continue to connect the line iteratively according to the intersection points of the target straight line and the sum function until the error between the ordinate of the target straight line after iteration and the sum function at a certain abscissa is less than the preset error, and regard the target straight line as the tangent line of the peak point of the sum function.

[0065] Specifically, when judging whether the target straight line is the tangent of the peak point of the sum function, take the corresponding ordinate y value in the sum function as y1 and y2 according to the target horizontal coordinates x5 and x6, and connect (x5, y1) and (x6, y2). The new straight line still has 4 intersections with the sum function, but the straight line is closer to the tangent of the double pseudo Viogt function. Repeat this process until the absolute error between the y obtained by the straight line equation and the y obtained by the sum function is less than the preset error, then stop the iteration and get the final target straight line. The horizontal coordinates of the two points of the final target straight line are x5' and x6'. Take a peak segment in the range of x≤x5' and a peak segment in the range of x≥x6'. The target straight line range is x5'<x<x6'. Finally, the two peak segments and the target straight line are processed to get the fitting equation of the background signal. The actual background signal is expressed as Figure 6 The orange curve in the

[0066] This embodiment performs equation fitting for the measured signal combining multiple pseudo Viogts with straight lines. By segmenting the measured signal and fitting the peak segment and the straight line segment respectively, it is possible to focus on extracting the features of the peak, thereby improving the accuracy of the entire signal fitting. In the process of finding the target tangent, an iterative method is used to update the straight line equation multiple times until it becomes a common tangent of two or more peaks and meets the accuracy requirements. This iterative process can effectively overcome the error of the initial estimate and make the final target straight line more in line with the actual situation. Even when there is a certain fluctuation or noise in the measured signal data, a relatively stable and accurate result can be obtained through multiple iterations, thereby improving the accuracy of the numerical solution of the parameters.

[0067] like Figure 7 As shown, an embodiment of the present invention further provides a signal processing method, which is executed by an electronic device such as a computer or a server, and specifically includes: S21, obtaining a background signal according to any of the above background signal extraction methods.

[0068] S22, subtracting the background signal from the actual signal corresponding to the sample to be tested to obtain a true signal of the sample to be tested.

[0069] After obtaining the accurate background signal when no sample is placed, it also includes a signal processing method, which eliminates the background signal in the measured signal to obtain the real signal of the measured sample, greatly improving the signal data quality and providing accurate data for subsequent sample analysis.

[0070] The background signal extraction method and signal processing method provided in this embodiment can be applied to the field of semiconductor material analysis, for example, it can be specifically used to measure the thickness of wafer films. The thickness measurement system collects the reflected light beam of the wafer film to the multi-wavelength light source, and obtains the corresponding relationship spectrum between the wavelength and reflectivity of the reflected light beam (the measured sample is the wafer film, and the measured signal data is the spectrum of the reflected light beam). This method can be used to obtain the background spectrum (background signal) of the measurement conditions of this system. The spectrum of the reflected light beam of the wafer film is subtracted from the background spectrum to obtain the true spectrum of the wafer film, and then the thickness information of the wafer film is obtained based on the true spectrum, which can improve the accuracy of the wafer film thickness measurement.

[0071] Embodiment 2 like Figure 8 As shown, an embodiment of the present invention further provides a background signal extraction method, which extracts background signals in the absence of a sample. The method is executed by an electronic device such as a computer or a server, and specifically includes: S31, obtaining at least one set of measured signal data without placing the measured sample, each set of measured signal data is collected based on the same measurement conditions.

[0072] S32, performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak.

[0073] S33, obtaining a background signal of the measurement condition based on a parameter numerical solution of each set of measured signal data.

[0074] This method is to directly obtain background signal data without placing a sample, collect at least one set of signal data under the same measurement conditions, and then perform parameter equation fitting, and obtain the background signal based on the obtained parameter equation solution. Directly obtaining signal data without placing a sample can eliminate the interference of the sample signal on the background signal. The obtained signal data reflects the background information generated by the measurement conditions. Moreover, directly obtaining the signal data without placing the sample, only the parameter equation fitting of these data is required to obtain the background signal, which greatly simplifies the extraction process of the background signal and improves work efficiency. In addition, obtaining at least one set of measured signal data ensures that the background signal can be effectively obtained under limited data, and when obtaining multiple sets of measured signal data, the obtained background signal can be made smoother.

[0075] Furthermore, there are multiple ways to obtain the background signal of the measurement condition in step S33, which are as follows: As a first implementation, step S33 includes: S331a, selecting any one of the multiple groups of measured signal data.

[0076] S332a, using the parameter numerical solution of the selected measured signal data to obtain the background signal of the measurement condition.

[0077] By means of this embodiment, it is convenient and effective to arbitrarily select a measured signal from multiple groups of measured signal data, and obtain a background signal based on the parameter numerical solution of the group of measured signals. In this process, it is only necessary to select a group of measured signal data from multiple groups of measured signal data for parameter equation fitting, obtain the parameter numerical solution, and then obtain the background signal. There is no need to process each group of measured signals, only to arbitrarily select one measured signal data. This greatly reduces the workload and time cost of data processing, especially when processing a large amount of signal data, it can significantly improve the efficiency of obtaining background signals.

[0078] As a second implementation, step S33 includes: S331b, using the parameter numerical solutions of multiple groups of measured signal data, obtain the background signal of the corresponding measurement conditions.

[0079] S332b, performing comprehensive operation on multiple groups of background signals to obtain background signals of measurement conditions, wherein the comprehensive operation includes taking an average or a median.

[0080] The specific comprehensive calculation method is the same as the steps of placing the sample to be tested, and will not be repeated here.

[0081] Since each set of data may be interfered by random errors to varying degrees during the measurement process, it is necessary to process the background signals obtained from the numerical solutions of the parameters of all groups, that is, by performing comprehensive operations on the background signals (taking the average or median), these random errors will offset each other to a certain extent. This can significantly improve the quality of the background signal, effectively reduce random noise, balance measurement errors, weaken the interference of outliers, and make the background signal closer to the actual situation.

[0082] As a third implementation, step S33 includes: S331c, performing cluster center calculation on parameter numerical solutions of multiple groups of measured signal data to obtain background signals of measurement conditions.

[0083] This embodiment performs cluster analysis on the parameter numerical solutions of multiple groups of background signals, classifies peaks with similar characteristics into different clusters, finds the typical characteristics (cluster center) of each cluster, and thus fits the background signal more accurately. This embodiment can integrate multiple groups of data information through clustering, improve the anti-interference ability and stability of the results, and obtain a more accurate background signal.

[0084] In one embodiment, in step S32, parameter equation fitting is performed for each set of measured signal data, specifically including: S321, obtaining a mathematical model adapted to the measured signal data, wherein the mathematical model includes a multi-pseudoViogt model or a multi-pseudoViogt and straight line combined mathematical model; S322, using a mathematical model and the measured signal data to obtain a fitting equation for the measured signal data.

[0085] Furthermore, if the mathematical model adapted to the background signal is a mathematical model combining multiple pseudo Viogts and straight lines, a straight line segment and a first peak segment and a second peak segment located closest to both sides of the straight line segment are intercepted from the measured signal data; Performing single pseudo Viogt peak fitting on the first peak segment and the second peak segment respectively to obtain a first pseudo Viogt function and a second pseudo Viogt function; Get the vertices of the first pseudo Viogt function and the second pseudo Viogt function, and connect the two vertices; Calculate a sum function based on the first pseudo Viogt function and the second pseudo Viogt function; Use the coordinates of the connecting line and the multiple intersection points of the sum function to connect the line again to obtain the target straight line, and determine whether the target straight line is the tangent line of the peak point of the sum function; If the target straight line is the tangent line of the peak point of the sum function, the target horizontal coordinates of multiple intersection points are used to intercept the two peak segments of the sum function, and the target horizontal coordinates are calculated by averaging the horizontal coordinates of multiple intersection points; The fitting equation of the background signal is calculated based on the intercepted data and the target straight line.

[0086] Furthermore, the coordinates of the multiple intersection points of the connecting line and the sum function are used to connect the line again to obtain the target straight line, and it is determined whether the target straight line is a tangent line to the peak point of the sum function, including: According to the target abscissa and its corresponding ordinate, a plurality of target intersection points are obtained; Connect multiple target intersection points to obtain the target straight line; Calculate the error between the target straight line and the vertical coordinate of the sum function at a certain horizontal coordinate and make an error judgment. If the error is less than a preset error, the target straight line is regarded as a tangent line of the peak point of the sum function. If the error is greater than the preset error, continue to iterate the connection line according to the intersection of the target straight line and the sum function until the error between the vertical coordinates of the iterative target straight line and the sum function at a certain horizontal coordinate is less than the preset error, and the target straight line is regarded as the tangent of the peak point of the sum function.

[0087] The above-mentioned specific method for fitting the parameter equation for each group of measured signal data is the same as the parameter fitting equation fitting steps when placing the measured sample, and will not be repeated here.

[0088] The embodiment of the present invention further provides a signal processing method, which is executed by an electronic device such as a computer or a server, and specifically includes: S41, obtaining a background signal according to any of the background signal extraction methods described above.

[0089] S42, subtracting the background signal from the actual signal corresponding to the sample to be tested to obtain a true signal of the sample to be tested.

[0090] After obtaining the accurate background signal when no sample is placed, it also includes a signal processing method, which eliminates the background signal in the measured signal to obtain the real signal of the measured sample, greatly improving the signal data quality and providing accurate data for subsequent sample analysis.

[0091] The background signal extraction method and signal processing method provided in this embodiment can be applied to the field of semiconductor material analysis, for example, it can be specifically used to measure the thickness of wafer films. Without placing a wafer film (the sample to be measured), the spectrum of the corresponding relationship between the wavelength and the reflectivity under this measurement condition is collected (which can be understood as the spectrum of the ambient light), and the background spectrum (background signal) of the measurement condition of this system can be obtained by using this method. The spectrum of the reflected light beam of the wafer film is subtracted from the background spectrum to obtain the true spectrum of the wafer film, and then the thickness information of the wafer film is obtained based on the true spectrum, which can improve the accuracy of the wafer film thickness measurement.

[0092] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled 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 list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A background signal extraction method, characterized in that: include: Acquire multiple groups of measured signal data of multiple samples, each group of measured signal data is collected based on the same measurement conditions; Performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak; Determining the peak of the background signal originating from the measurement conditions according to the numerical solution of each group of the parameters; Based on the determined peak of the background signal originating from the measurement condition, the background signal of the measurement condition is obtained.

2. The method according to claim 1, characterized in that Determining the peak of the background signal originating from the measurement condition according to the numerical solution of each group of the parameters includes: For each group of measured signal data, determine whether any peak in the measured signal data has a similar peak in other measured signal data; If similar peaks exist in other measured signal data, it is determined that the peak is a peak of a background signal originating from the measurement condition.

3. The method according to claim 1, characterized in that Determining the peak of the background signal originating from the measurement condition according to the numerical solution of each group of the parameters includes: Performing cluster analysis on the parameter numerical solution of each peak to determine whether at least one set of peaks with similar parameters is obtained; If a collection of peaks with similar parameters is not obtained, the background signal is not identified; If at least one set of peaks with similar parameters is obtained, each peak in each set originates from the background signal of the measurement condition.

4. The method according to claim 1, characterized in that: The obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: Select any one of the multiple groups of measured signal data; The peak of the background signal originating from the measurement condition is determined in the selected measured signal data, and the background signal of the measurement condition is obtained by using a corresponding parameter numerical solution.

5. The method according to claim 1, characterized in that The obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: Based on the peak of the background signal originating from the measurement condition, respectively extracting background signals from a plurality of groups of measured signal data; A comprehensive operation is performed on multiple groups of background signals to obtain the background signals of the measurement conditions, wherein the comprehensive operation includes taking an average or a median.

6. The method according to claim 1, characterized in that The obtaining the background signal of the measurement condition based on the determined peak of the background signal originating from the measurement condition comprises: A cluster center calculation is performed on the peak of at least one frame of the background signal originating from the measurement condition to obtain the background signal of the measurement condition.

7. A background signal extraction method, characterized in that: include: Acquire at least one set of measured signal data without placing the measured sample, each set of the measured signal data is collected based on the same measurement conditions; Performing parameter equation fitting for each set of measured signal data to obtain a parameter numerical solution for each set of measured signal data, wherein the parameter numerical solution includes height information, width information, position information and shape information of each peak; Based on the parameter numerical solution of each set of measured signal data, the background signal of the measurement condition is obtained.

8. The method according to claim 7, characterized in that The parameter numerical solution based on each set of measured signal data to obtain the background signal of the measurement condition includes: Select any one of the multiple groups of measured signal data; The background signal of the measurement condition is obtained by using the parameter numerical solution of the selected measured signal data.

9. The method according to claim 7, characterized in that: The parameter numerical solution based on each set of measured signal data to obtain the background signal of the measurement condition includes: Using the parameter numerical solutions of the multiple groups of measured signal data, the background signals corresponding to the measurement conditions are obtained; A comprehensive operation is performed on multiple groups of background signals to obtain the background signals of the measurement conditions, wherein the comprehensive operation includes taking an average or a median.

10. The method according to claim 7, characterized in that The parameter numerical solution based on each set of measured signal data to obtain the background signal of the measurement condition includes: Cluster center calculation is performed on parameter numerical solutions of multiple groups of measured signal data to obtain background signals under the measurement conditions.

11. The method according to claim 1 or 7, characterized in that: The performing parameter equation fitting for each set of measured signal data respectively includes: Acquire a mathematical model adapted to the measured signal data, wherein the mathematical model includes a multi-pseudo Viogt model or a multi-pseudo Viogt and straight line combined mathematical model; The mathematical model and the measured signal data are used to obtain a fitting equation for the measured signal data.

12. The method according to claim 11, characterized in that If the mathematical model adapted to the measured signal data is a mathematical model combining multiple pseudo Viogts and straight lines, a straight line segment and a first peak segment and a second peak segment located closest to both sides of the straight line segment are intercepted from the measured signal data; Performing single pseudo Viogt peak fitting on the first peak segment and the second peak segment respectively to obtain a first pseudo Viogt function and a second pseudo Viogt function; Obtaining the vertices of the first pseudo Viogt function and the second pseudo Viogt function, and connecting the two vertices; Calculating a sum function according to the first pseudo Viogt function and the second pseudo Viogt function; Using the coordinates of the connecting line and the multiple intersection points of the sum function to connect the line again, a target straight line is obtained, and it is determined whether the target straight line is a tangent line to the peak point of the sum function; If the target straight line is a tangent line of the peak point of the sum function, two peak segments of the sum function are intercepted using target horizontal coordinates of multiple intersection points, where the target horizontal coordinates are calculated by averaging the horizontal coordinates of multiple intersection points; The fitting equation of the background signal is obtained by calculation based on the intercepted data and the target straight line.

13. The method according to claim 12, characterized in that Using the coordinates of the connecting line and the multiple intersection points of the sum function to connect the line again to obtain a target straight line, and determining whether the target straight line is a tangent line of the peak point of the sum function, including: According to the target abscissa and its corresponding ordinate, a plurality of target intersection points are obtained; Connecting a plurality of target intersection points to obtain a target straight line; Calculate the error between the target straight line and the ordinate of the sum function at a certain abscissa and make an error judgment. If the error is less than a preset error, the target straight line is regarded as a tangent to the peak point of the sum function. If the error is greater than the preset error, continue to iterate the connection based on the intersection of the target straight line and the sum function until the error between the vertical coordinate of the iterative target straight line and the sum function at a certain horizontal coordinate is less than the preset error, and the target straight line is regarded as the tangent of the peak point of the sum function.

14. A signal processing method, characterized in that: include: Obtaining a background signal according to the background signal extraction method described in any one of claims 1 to 13; The background signal is subtracted from the actual measured signal corresponding to the sample being tested to obtain the real signal of the sample being tested.

15. A background signal extraction device, characterized in that: include: 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 executes the background signal extraction method as described in any one of claims 1-13.

16. A signal processing device, characterized in that: include: 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 signal processing method as claimed in claim 14.

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