A data processing method and system based on line-spectral confocal and a medium

Abstract

This invention discloses a data processing method, system, and medium based on line spectral confocal microscopy. The method includes: analyzing the light intensity corresponding to each wavelength in the spectral image to obtain two initial wavelengths; extracting the corresponding predicted peak value and predicted full width at half maximum (FWHM) from the spectral images where the peak value is only the first and second initial wavelengths, respectively; analyzing the light intensity data corresponding to each wavelength after the light intensity overlaps, and accumulating the deviation between the light intensity data and the light intensity data corresponding to each wavelength in the spectral image corresponding to the current position; adjusting the two initial wavelengths according to the deviation to determine the thickness of the object under test. This invention reduces interference from overlapping signals, can obtain wavelengths that converge to the upper and lower surfaces of the object under test, and improves the detection accuracy of the line spectral confocal sensor for the thickness of ultrathin transparent objects.

Description

Technical Field

[0001] This invention belongs to the technical field of spectral confocalization, and relates to a data processing method, system and medium based on line spectral confocalization. Background Technology

[0002] Linear spectral confocal sensors extend single-point measurements to line measurements, and then achieve surface measurements through high-speed scanning, thereby enabling rapid, high-precision, non-contact detection of complex surface three-dimensional morphology. Linear spectral confocal sensors utilize the different focal lengths of different monochromatic lights in a dispersive objective for measurement. A line source emits white light, which, after passing through the dispersive objective, produces continuous monochromatic light along the optical axis. Light of different wavelengths is converged at different axial positions by the dispersive objective. The specific wavelength of light converged on the surface of the object being measured and reflected back is received by a spectrometer. The spectrometer analyzes the spectrum of the reflected light to obtain the distance from the surface of the object being measured.

[0003] In line spectral confocal sensors, when measuring the thickness of an object, reflection signals are generated from the upper and lower surfaces of the sample, forming two independent peaks. The wavelength difference between these peaks corresponds to the thickness. When the object is a transparent, ultrathin sample, the reflection responses converging at the upper and lower surfaces of the transparent object partially overlap in the spectral domain. This causes the two axial response curves centered at different wavelengths to couple and superimpose, resulting in peak wavelength drift. If the measurement distance corresponding to the two peak wavelengths in the overlapped spectrum is used to calculate the thickness of the object, the accuracy of the thickness measurement will be severely affected. This problem is particularly prominent in the thickness measurement of ultrathin transparent samples (such as films, glass, and biological tissue sections). Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned problems in the prior art and to provide a data processing method, system and medium based on line spectral confocalization.

[0005] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution: A data processing method based on line spectral confocal microscopy is used to detect the thickness of a transparent analyte. Obtain the spectrum corresponding to any position on the scan line, extract the peak value and peak wavelength in the spectrum, and retain the peak corresponding to the effective peak wavelength in the spectrum. The number of effective peak wavelengths is equal to 2. By analyzing the light intensity corresponding to each wavelength in the spectrum, the first initial wavelength that converges to the upper surface of the object and the second initial wavelength that converges to the lower surface of the object are obtained. From the spectra with peak values ​​only at the first and second initial wavelengths, respectively, the corresponding predicted peak values ​​and predicted full width at half maximum (FWHM) are extracted. Based on the predicted peak value and predicted full width at half maximum (FWHM), the light intensity data corresponding to each wavelength after light intensity overlap is obtained, and the cumulative deviation between the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position is calculated. Determine if the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, adjust one of the initial wavelengths sequentially and update the first and second initial wavelengths until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, calculate the difference between the measurement heights corresponding to the first and second initial wavelengths to determine the thickness of the object to be measured.

[0006] Furthermore, the effective peak wavelength in the spectrum is determined by using a wavelength whose half-width at half-maximum (FWHM) is greater than the set width threshold corresponding to the peak wavelength. The set width threshold is a reference spectrum with only one peak and the peak wavelength is the FWHM corresponding to the peak wavelength in the spectrum.

[0007] Furthermore, the method for identifying the first initial wavelength and the second initial wavelength based on the light intensity data corresponding to each wavelength in the spectral diagram includes: A sliding window is used to perform polynomial least squares fitting on the light intensity data within the window. The light intensity data corresponding to the polynomial at the center point of the sliding window are selected to process the light intensity data corresponding to each wavelength within the sliding window and obtain a smooth and complete signal. Count the number of peaks in the current spectrum; Determine if the number of peaks in the current spectrum is equal to 2. If it is equal to 2, then preliminarily determine the initial wavelengths of the two peaks.

[0008] Furthermore, the second derivative of the polynomial of the spectral signal corresponding to the spectrum is taken, and the first and second initial wavelengths corresponding to the second derivative being equal to 0 are selected.

[0009] Furthermore, methods for determining the initial wavelength based on peak values ​​in the spectral image include: Calculate the full width at half maximum (FWHM) threshold based on the peak values ​​in the spectral image; Find the intersection point between the full width at half maximum (FWHM) threshold and the light intensity data in the spectral image, determine the number of intersection points, and extract the wavelength corresponding to the intersection point as the intersection wavelength; If the number of intersection points is not equal to 4, then the second derivative of the polynomial of the spectral signal is taken, and the wavelengths corresponding to the previous wavelength and the next wavelength are selected. Determine the relationship between the wavelength and the wavelength at the first intersection point and the wavelength at the second intersection point. If the wavelength is greater than the wavelength at the second intersection point or less than the wavelength at the first intersection point, the wavelength is lost; otherwise, if the wavelength is between the wavelength at the first intersection point and the wavelength at the second intersection point, the wavelength is retained. The wavelengths are extracted and retained, and based on the first and second intersection wavelengths, the two initial wavelengths that converge to the upper and lower surfaces of the test object are analyzed.

[0010] Furthermore, the sum of the first intersection wavelength and the retained wavelength is averaged to obtain a first initial wavelength that converges to the upper surface of the test object. The sum of the second intersection wavelength and the retained wavelength is averaged to obtain a second initial wavelength that converges to the lower surface of the test object.

[0011] Furthermore, when the number of intersection points is equal to 4, based on the wavelengths of the first, third, fourth, and second intersection points, and combined with the light intensity corresponding to each wavelength within the intervals between the first and third intersection points and between the fourth and second intersection points, the centroid method is used to calculate and obtain the first initial wavelength that converges to the upper surface of the object under test and the second initial wavelength that converges to the lower surface of the object under test.

[0012] Furthermore, a signal overlap model is constructed to obtain the light intensity data corresponding to each wavelength after the light intensity overlaps. The signal overlap model is as follows: E0 represents background noise, n represents the number of times the monochromatic light, after dispersion by the dispersive objective lens, converges to the surface of the object under test and is reflected, and A i This represents the peak value corresponding to the peak wavelength when the i-th initial wavelength converges onto the non-transparent surface of the analyte, where λ represents any wavelength in the spectrum. i0 Let w represent the initial wavelength corresponding to the i-th peak. i β is represented as the full width at half maximum (FWHM) of the i-th peak wavelength. i The shape of the i-th peak wavelength is represented by β. For non-transparent analytes, the spectral data converged to the surface of the analyte exhibits a Gaussian distribution. i The value is 2.

[0013] Based on the same inventive concept, this invention discloses a data processing method based on line spectral confocalization, including: The image preprocessing module is used to obtain the spectrum corresponding to any position on the scan line, extract the peak value and peak wavelength in the spectrum, and retain the peak corresponding to the effective peak wavelength in the spectrum, wherein the number of effective peak wavelengths is equal to two. The peak wavelength analysis module is used to analyze the light intensity corresponding to each wavelength in the spectrum to obtain the first initial wavelength that converges to the upper surface of the test object and the second initial wavelength that converges to the lower surface of the test object. The parameter filtering module extracts the corresponding predicted peak value and predicted full width at half maximum (FWHM) from the spectra of those with peak values ​​only at the first and second initial wavelengths, respectively. The model bias analysis module obtains the light intensity data corresponding to each wavelength after light intensity overlap based on the predicted peak value and predicted half-peak width, and calculates the cumulative bias between the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position. The thickness detection module is used to determine whether the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, one of the initial wavelengths is adjusted sequentially, and the first and second initial wavelengths are updated until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, the difference between the measurement heights corresponding to the first and second initial wavelengths is calculated to determine the thickness of the object to be measured.

[0014] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data processing method based on line spectral confocalization as described above.

[0015] The beneficial effects of this invention are: The data processing method based on line spectral confocal imaging provided by this invention analyzes the light intensity corresponding to each wavelength in the spectrum to preliminarily determine the initial wavelengths that converge to the upper and lower surfaces of the object under test. By combining the predicted peak value and predicted half-width of the initial wavelength with the single peak wavelength in the spectrum, the light intensity data corresponding to each wavelength after light intensity overlap is obtained. The deviation of the light intensity values ​​corresponding to each wavelength is accumulated. The thickness of the object under test is determined by the difference between the measurement heights corresponding to two initial wavelengths with a deviation less than a set deviation threshold. This reduces the interference of overlapping signals and improves the detection accuracy of the line spectral confocal sensor for the thickness of ultra-thin transparent objects under test.

[0016] This invention uses a set width threshold corresponding to each peak wavelength in the spectrum at any position on the scan line, and compares it with the half-width of that peak wavelength to filter each peak wavelength in the spectrum, thereby obtaining the number of effective peak wavelengths in the spectrum, realizing the processing of each peak in the spectrum, and reducing the interference of clutter peaks in the spot image.

[0017] This invention obtains a first initial wavelength and a second initial wavelength by analyzing the wavelengths corresponding to the peaks in the spectrum after signal overlap. The first initial wavelength and the second initial wavelength are closer to the wavelengths that converge to the upper and lower surfaces of the test object than the wavelengths corresponding to the peaks in the spectrum after signal overlap.

[0018] This invention constructs a signal overlap model based on the predicted peak value and predicted half-peak width determined by the initial wavelength. This model obtains the light intensity values ​​of each overlapping wavelength corresponding to the wavelength that converges to the upper and lower surfaces of the test object as the initial wavelength. The model accumulates the deviation between the light intensity values ​​of each overlapping wavelength and the wavelength corresponding to each wavelength in the spectrum at the current position, and updates the initial wavelength according to the deviation, so that the updated initial wavelength approaches the wavelength corresponding to the wavelength that converges to the upper and lower surfaces of the test object.

[0019] This invention obtains a first background noise and a second background noise by sequentially determining the light intensity values ​​corresponding to each wavelength outside the effective wavelength range, averaging them, and then averaging the first background noise and the second background noise to obtain the background noise corresponding to the overlapping spectrum. This reduces the error of the background noise, improves the accuracy of the background noise calculation, and thus improves the accuracy of the constructed signal overlap model.

[0020] This invention compares the calculated deviation with a set deviation threshold, and adjusts the two initial wavelengths sequentially based on the comparison results, so that the adjusted initial wavelengths are the wavelengths that converge to the upper and lower surfaces of the object under test. Then, the thickness data of the ultrathin transparent object under test is obtained based on the measurement heights corresponding to the two initial wavelengths. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is the spectral diagram of the transparent material in this invention; Figure 2 This is a flowchart of the data processing method based on line spectral confocalization in this invention; Figure 3 This is a flowchart of the initial wavelength identification method in this invention; Figure 4 This is a flowchart of the method for determining background noise in this invention; Figure 5 This is a system block diagram of the data processing system based on line spectral confocalization in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Spectral confocal microscopy utilizes a broadband light source and a dispersive objective lens to focus light of different wavelengths at different positions along the axis, establishing a relationship between wavelength and distance. When measuring the thickness of a transparent object, the upper and lower surfaces of the object generate reflected signals, forming two independent peaks. The difference between the distances corresponding to the wavelengths of these peaks corresponds to the thickness of the object.

[0024] When the thickness of a transparent object measured by laser confocal microscopy is less than the spectral bandwidth of the system's axial response, the spectral signals generated by the upper and lower surfaces partially overlap in the spectral domain. For example... Figure 1 After overlapping, the spectral curves are no longer two independent peaks, but merge into a broad peak or a shifted peak. The merged peak is a non-Gaussian, asymmetric "abnormal" peak, which causes the peak wavelength to drift. Because the peak deviates from the true peak, the peak extraction is incorrect, which increases the thickness measurement error.

[0025] Based on the above issues, such as Figure 2 As shown, this invention provides a data processing method based on line spectral confocal microscopy to detect a transparent object and obtain its thickness, including: Step 1: Obtain the spectrum corresponding to any position on the scan line, extract the peak value and peak wavelength in the spectrum, and retain the peak corresponding to the effective peak wavelength in the spectrum. The number of effective peak wavelengths is equal to 2. The effective peak wavelength in the spectrum is determined by using a wavelength whose half-width at half-maximum (FWHM) is greater than the set width threshold corresponding to the peak wavelength. The set width threshold is a reference spectrum with only one peak and the peak wavelength is the FWHM corresponding to the peak wavelength in the spectrum.

[0026] When a line spectral confocal sensor detects a transparent object, the spectral signals generated by the upper and lower surfaces of the object partially overlap in the spectral domain. This causes the spectral pattern corresponding to any position on the scanning line to not satisfy a Gaussian distribution. In the spectral pattern, each wavelength has a corresponding light intensity data.

[0027] For transparent test objects, if the mutual interference between the spectral signals generated by the upper and lower surfaces of the test object is not considered, the spectral signals converged to either the upper or lower surface of the test object will all satisfy a Gaussian distribution. A line spectral confocal sensor is used to detect the upper surface of the non-transparent test object. The spectrometer obtains the spectrum generated by the upper surface. Based on the peak wavelength in the spectrum, the half-width at half-maximum (WHM) of the peak corresponding to that peak wavelength can be determined. By adjusting the distance between the line spectral confocal sensor and the upper surface of the non-transparent test object, spectra at different distances from the objective lens to the upper surface of the test object can be obtained. Based on the peak wavelength in the spectrum, the WHM of the peak corresponding to each peak wavelength can be obtained, thereby determining the set width threshold corresponding to the peak wavelength.

[0028] When the test object is a transparent material, there are multiple peaks in the spectrum corresponding to any position. It is necessary to select the main peak from several peaks, where the number of main peaks is one or two. Since the spectral signals generated by the upper and lower surfaces of the test object overlap, the number of peaks is one or two. This application focuses on two peaks.

[0029] Step 2: Analyze the light intensity data corresponding to each wavelength in the spectrum to obtain the first initial wavelength and the second initial wavelength; wherein, the first initial wavelength is the initial wavelength that converges to the upper surface of the object to be tested, and the second initial wavelength is the initial wavelength that converges to the lower surface of the object to be tested. Since the test object is a transparent double-layer structure, a peak will be generated on both the upper and lower surfaces of the test object. The signals of the two wavelengths are fused to form the spectrum corresponding to the current position.

[0030] By analyzing the light intensity data corresponding to each wavelength in the spectrum, the initial wavelengths that converge to the upper and lower surfaces of the transparent test object are initially determined. This makes the first and second initial wavelengths closer to the true wavelengths that converge to the upper and lower surfaces of the test object compared to the peak wavelengths corresponding to the two peaks in the spectrum. This allows for a gradual approach to the true wavelengths that converge to the upper and lower surfaces of the test object based on the deviation amount.

[0031] like Figure 3 As shown, the method for identifying the first and second initial wavelengths based on the light intensity data corresponding to each wavelength in the spectrum includes: Step 21: Using a sliding window, perform polynomial least squares fitting on the light intensity data within the window, and filter out the light intensity data corresponding to the polynomial at the center point of the sliding window. This allows for the processing of the light intensity data corresponding to each wavelength within the sliding window to obtain a smooth and complete signal. Based on the light intensity data corresponding to the polynomial at the center point of the sliding window, light intensity data corresponding to each wavelength within the sliding window that is greater than the light intensity data corresponding to the polynomial at the center point of the sliding window are removed, and interpolation is used to fill in the light intensity data corresponding to the removed wavelength.

[0032] The width of the sliding window used for screening is W. Under the width of the sliding window, the peak position, peak width, and peak height of the main peak in the spectrum remain unchanged.

[0033] The above processing method can be used to process abnormal small peaks in the spectrum and reduce their impact on the number of peaks.

[0034] Abnormal small peaks can also be identified by setting peak height or peak width, and abnormal peaks can be removed to eliminate the influence of abnormal peaks on the spectrum. Based on the light intensity data corresponding to two adjacent wavelengths, an interpolation algorithm is used to correct the removed peaks to obtain continuous light intensity data on the spectrum.

[0035] Step 22: Count the number of peaks in the current spectrum; For overlapping peaks, when the distance between the two peaks is close, there may be one peak or two peaks.

[0036] Step 23: Determine if the number of peaks in the current spectrum is equal to 2. If it is equal to 2, then preliminarily determine the initial wavelengths of the two peaks. The spectrum is a fusion of two separate peak signals. The exact location of the two peaks cannot be directly located by the spectrum alone. At this time, the wavelengths corresponding to the two separate peaks in the spectrum are not the wavelengths that converge to the upper and lower surfaces of the test object. This embodiment discloses a rough calculation method for the wavelength corresponding to the peak position. Specifically, based on the light intensity data corresponding to each wavelength in the spectrum, a polynomial of the spectral signal is constructed. The second derivative of the polynomial E of the spectral signal is then calculated, and the first initial wavelength λ corresponding to the second derivative being equal to 0 is selected. 10 Second initial wavelength λ 20 The first initial wavelength λ 10 It must satisfy the condition in the second derivative. and Similarly, the second initial wavelength λ 20 It must satisfy the condition in the second derivative. and , λ 10 -1 and λ 10 +1 represents the first initial wavelength λ. 10 Two adjacent wavelengths, λ 20 -1 and λ 20 +1 represents the difference between the second initial wavelength λ and the wavelength λ. 20 Two adjacent wavelengths.

[0037] The first initial wavelength λ of the peak can be obtained through the above method. 10 Second initial wavelength λ 20 .

[0038] This embodiment also discloses a method for determining the initial wavelength based on the peak position in the spectral image, and screening out wavelengths that satisfy E. λ >E λ -1 and E λ >E λ+1 The first initial wavelength λ of the condition 10 Second initial wavelength λ 20The first initial wavelength λ of the peak in the spectrum can be obtained through the above method. 10 Second initial wavelength λ 20 .

[0039] This embodiment also discloses a method for determining an initial wavelength based on peak values ​​in a spectral image, comprising: Step 10: Filter the peak values ​​in the current signal and calculate the full width at half maximum (FWHM) threshold based on the peak values; Half-width threshold A th The calculation formula for A is: th =(A 10 +A 20 ) / 4, A 10 and A 20 These are represented as the light intensity data corresponding to the first peak and the second peak in the spectrum, respectively.

[0040] Step 20: Find the intersection point between the full width at half maximum (FWHM) threshold and the light intensity data in the spectrum, determine the number of intersection points, and extract the wavelength corresponding to the intersection point; When the distance between two peaks in the spectrum is close, only two intersection points with the signal in the spectrum can be screened corresponding to the half-width threshold. It is impossible to separate the two close peaks based on the two intersection point data.

[0041] Step 30: If the number of intersection points is not equal to 4, then perform second derivative on the polynomial of the spectral signal, and select the wavelengths whose second derivative is less than 0 for the previous wavelength and greater than 0 for the next wavelength, i.e., wavelengths λ. C0 _; When the number of intersection points is not equal to 4, the number of intersection points is equal to 2. The wavelengths corresponding to these intersection points are the wavelengths λ of the first intersection point. C1_ The wavelength λ at the second intersection point C2_ Wherein, the wavelength λ at the first intersection point C1_ The wavelength λ is less than the second intersection point. C2_ .

[0042] Step 40: Determine the wavelength λ C0_ Wavelength λ at the first intersection point C1_ The wavelength λ at the second intersection point C2_ The relationship between them, if greater than the wavelength λ of the second intersection point. C2_ Or less than the wavelength λ of the first intersection point C1_ Then the wavelength λ is lost. C0_ Until the selected wavelength λ C0_ Wavelength λ at the first intersection point C1_ The wavelength λ at the second intersection point C2_ Between; conversely, if located at the first intersection point, wavelength λ C1_ The wavelength λ at the second intersection pointC2_ Between these, the wavelength λ is retained. C0_ ; Step 50: Extract the retained wavelength λ C0_ Based on the wavelength λ of the first intersection point C1_ The wavelength λ at the second intersection point C2_ The analysis focuses on two initial wavelengths that converge to the upper and lower surfaces of the test object. The first initial wavelength that converges to the upper surface of the test object is obtained by averaging the sum of the first intersection wavelength and the retained wavelength. The second initial wavelength that converges to the lower surface of the test object is obtained by averaging the sum of the second intersection wavelength and the retained wavelength.

[0043] Specifically, the first initial wavelength λ converges to the surface of the object under test. 10 :λ 10 =(λ C1_ +λ C0_ ) / 2; The second initial wavelength λ converges to the upper surface of the object under test 20 :λ 20 =(λ C2_ +λ C0_ ) / 2; The values ​​of the two peaks in the spectrum are affected by the superposition of the light intensity of the two real wavelengths that converge to the upper and lower surfaces at that position, resulting in a first peak and a second peak in the spectrum. The wavelengths corresponding to the first peak and the second peak deviate from the real wavelengths that converge to the upper and lower surfaces of the test object.

[0044] Based on the wavelength λ of the first intersection point in the spectrum C1_ The wavelength λ at the second intersection point C2 The wavelengths corresponding to the peaks between the two points can be used to determine the true wavelengths that converge to the upper surface, which are located at the first intersection wavelength λ. C1_ With wavelength λ C0_ Between them, the true wavelength that converges to the lower surface is located at wavelength λ. C0_ Wavelength λ at the second intersection point C2 between.

[0045] Step 60: If the number of intersection points is equal to 4, analyze the two initial wavelengths that converge to the upper and lower surfaces of the object under test based on the wavelengths corresponding to the intersection points.

[0046] At this point, since the number of intersections between the full width at half maximum (FWHM) threshold and the signal in the spectrum is 4, the wavelengths corresponding to each intersection point, in ascending order of wavelength, are successively the wavelengths of the first intersection point, λ. C1_ The wavelength λ at the third intersection point C3_ The wavelength λ at the fourth intersection point C4_ The wavelength λ at the second intersection point C2 .

[0047] The first initial wavelength λ converges to the surface of the object under test 10 :λ 10 =(λ C1_ +λ C3_ ) / 2; The second initial wavelength λ converges to the upper surface of the object under test 20 :λ 20 =(λ C2_ +λ C4_ ) / 2; Using the above method, the initial wavelengths that converge to the upper and lower surfaces of the analyte in the spectral image can be identified.

[0048] In this embodiment, since the peaks corresponding to the first peak and the second peak in the spectrum are not symmetrical, the valley point between the first peak and the second peak is identified. The first initial wavelength and the second initial wavelength are calculated by using the wavelength corresponding to the valley point and the wavelength of the first intersection point and the wavelength of the second intersection point, or by averaging the wavelengths corresponding to the two intersection points adjacent to the first peak point to obtain the first initial wavelength, and by averaging the wavelengths corresponding to the two intersection points adjacent to the second peak point to obtain the second initial wavelength. As a result, the accuracy of the first initial wavelength that converges to the upper surface of the test object and the second initial wavelength that converges to the lower surface is relatively insufficient and is easily affected by the light intensity value of the peak in the spectrum.

[0049] To provide a more stable method, in this embodiment, when the number of intersection points is equal to 4, based on the wavelengths of the first intersection point, the third intersection point, the fourth intersection point, and the second intersection point, and combining the light intensity corresponding to each wavelength within the intervals between the first and third intersection point wavelengths and between the fourth and second intersection point wavelengths, the centroid method is used to calculate the first initial wavelength that converges to the upper surface of the test object and the second initial wavelength that converges to the lower surface of the test object.

[0050] The values ​​of the wavelengths at the first intersection point, the third intersection point, the fourth intersection point, and the second intersection point increase sequentially.

[0051] The formula for calculating the first initial wavelength is: ; Formula for calculating the second initial wavelength: .

[0052] By using the light intensity data corresponding to each wavelength within the wavelength ranges of the first and third intersection points, and the wavelength ranges of the fourth and second intersection points, the first initial wavelength converging to the upper surface of the test object and the second initial wavelength converging to the lower surface of the test object are calculated respectively. This eliminates peak asymmetry caused by optical aberrations of the line spectrum confocal sensor and the tilt of the test object surface, thereby accurately determining the wavelengths corresponding to the peak values ​​in the spectral diagrams of the upper and lower surfaces. This makes the calculated light intensity biased towards the denser light intensity values, improving the accuracy of the calculation of the first and second initial wavelengths, and thus making the first and second initial wavelengths close to the wavelengths corresponding to the true peaks.

[0053] Using the above method, the first initial wavelength and the second initial wavelength can be determined based on the wavelength corresponding to the peak in the spectrum after signal overlap. This makes the determined first initial wavelength and the second initial wavelength more likely to converge to the wavelengths on the upper and lower surfaces of the test object compared to the wavelengths corresponding to the peak in the spectrum after signal overlap, thus providing relatively accurate basic data for subsequent data processing.

[0054] Step 3: Extract the predicted peak value and predicted full width at half maximum (FWHM) from the spectra corresponding to the first and second initial wavelengths, respectively. The predicted peak value includes the first initial peak value in the spectrum that is only the first initial wavelength and the second initial peak value in the spectrum that is only the second initial wavelength. The predicted width includes the first predicted half-peak width w1 in the spectrum that is only the first initial wavelength and the second predicted half-peak width w2 in the spectrum that is only the second initial wavelength. For a non-transparent test object, white light, after passing through a dispersive objective lens, converges onto the upper surface of the non-transparent test object to form a spectrum. When the wavelength corresponding to the peak in the spectrum is the first initial wavelength, the peak value corresponding to the first initial wavelength is the first initial peak value, and the half-width at half-maximum (HWHM) corresponding to the first initial peak value is the first predicted HWHM. Similarly, when the wavelength corresponding to the peak in the spectrum is the second initial wavelength, the peak value corresponding to the second initial wavelength is the second initial peak value, and the HWHM corresponding to the second initial peak value is the second predicted HWHM.

[0055] Specifically, based on the set width threshold corresponding to each peak wavelength in step 1, when the peak is only the first initial wavelength, the set width threshold corresponding to the first initial wavelength is selected from several peak wavelengths, and this set width threshold is used as the predicted half-width of the first initial wavelength, and the peak value corresponding to the peak wavelength is used as the predicted peak value corresponding to the first initial wavelength. Similarly, when the peak is only the second initial wavelength, the set width threshold corresponding to the second initial wavelength is selected from several peak wavelengths, and this set width threshold is used as the predicted half-width of the second initial wavelength, and the peak value corresponding to the peak wavelength is used as the predicted peak value corresponding to the second initial wavelength.

[0056] Step 4: Based on the predicted peak value and predicted full width at half maximum (FWHM), obtain the light intensity data corresponding to each wavelength after the light intensity overlaps, and calculate the cumulative deviation between the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position. A signal overlap model is constructed to obtain the light intensity data corresponding to each wavelength after light intensity overlap. The signal overlap model is as follows: ,in, The light intensity represents the wavelength λ after the light intensity overlaps, E0 represents the background noise, and n represents the number of times the monochromatic light after dispersion by the dispersive objective converges to the surface of the test object and is reflected, i.e., the number of surfaces that reflect. In this application, the test object is made of transparent material, and reflection occurs on both the upper and lower surfaces, so n=2. i The peak value corresponding to the i-th initial wavelength when it converges to the non-transparent surface of the analyte is represented by λ, where λ represents any wavelength in the spectrum. i0 Let w represent the initial wavelength corresponding to the i-th peak, i.e., the first initial wavelength or the second initial wavelength. i Let β be the half-width at half maximum (FWHM) corresponding to the i-th peak wavelength, where FWHM is the predicted FWHM based on the peak wavelength. i The shape of the i-th peak wavelength is represented by β. For non-transparent analytes, the spectral data converged to the surface of the analyte exhibits a Gaussian distribution. i The value is 2.

[0057] In this process, such as Figure 4 As shown, the method for determining background noise includes the following steps: Step 31: Extract the predicted peak value and predicted full width at half maximum (FWHM) corresponding to the first initial wavelength and the second initial wavelength, respectively; Step 32: Filter the spectrum with only one peak and the peak wavelengths being the first initial wavelength and the second initial wavelength respectively, determine the region of the effective wavelength range, and obtain the light intensity data corresponding to each wavelength outside the region of the effective wavelength range. The effective wavelength range includes the first effective wavelength range and the second effective wavelength range.

[0058] The first effective wavelength range is The second effective wavelength range is .

[0059] Step 33: Calculate the average light intensity corresponding to each wavelength outside the effective wavelength range in the spectrum diagram with only one peak and the peak wavelength being the first initial wavelength and the second initial wavelength respectively, to obtain the first average light intensity and the second average light intensity, and then calculate the average of the first average light intensity and the second average light intensity to obtain the light intensity value corresponding to the background noise.

[0060] By analyzing the predicted peak value and predicted half-peak width corresponding to the spectrum with only one peak and the peak wavelength being the initial wavelength, the effective wavelength range corresponding to each initial wavelength is analyzed. The light intensity corresponding to each wavelength in the region outside the effective wavelength range is averaged to obtain the background noise corresponding to each initial wavelength. Furthermore, based on the background noise corresponding to each initial wavelength, the final background noise corresponding to the signal overlap model is obtained, thereby improving the accuracy of the spectral information corresponding to each position on the transparent surface of the test object.

[0061] In addition, in this embodiment, the predicted peak corresponding to the first initial wavelength is selected, and the background light intensity threshold is determined by the product between the screening threshold coefficient and the predicted peak. Each light intensity data in the spectrum that is less than the background light intensity threshold is screened out, and the average of each screened light intensity data is calculated to obtain the first background noise corresponding to the spectrum with only one peak and the peak wavelength being the first initial wavelength. Similarly, the predicted peak corresponding to the second initial wavelength is selected, and the background light intensity threshold is determined by the product between the screening threshold coefficient and the predicted peak. The light intensity data in the spectrum that are less than the background light intensity threshold are screened out, and the average of the screened light intensity data is calculated to obtain the second background noise corresponding to the spectrum with only one peak and the peak wavelength being the second initial wavelength. The background noise value in the signal overlap model is obtained by averaging the first background noise and the second background noise.

[0062] The screening threshold coefficient is generally set to 1%-3%. The specific coefficient can be determined based on the ratio between the values ​​at both ends of the spectrum and the peak value in the spectrum.

[0063] By using the above method, the value of background noise in the signal overlap model is also determined, so as to eliminate the problem that the background noise values ​​corresponding to the peak wavelengths being the first initial wavelength and the second initial wavelength are different, which effectively improves the accuracy of the signal overlap model.

[0064] Step 5: Determine whether the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, adjust one of the initial wavelengths sequentially by a fixed wavelength change, and update the first and second initial wavelengths until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, calculate the difference between the measurement heights corresponding to the first and second initial wavelengths to determine the thickness of the object to be measured.

[0065] The set deviation threshold is used to allow for the degree of deviation between the light intensity corresponding to each wavelength in the current spectrum and the light intensity corresponding to each wavelength in the signal overlap model constructed based on the first and second initial wavelengths. The value of the set deviation threshold can be selected according to the required fitting accuracy; here, it is set according to the requirements. The smaller the deviation, the closer the spectrum corresponding to the fitted signal overlap model is to the current spectrum; conversely, the larger the deviation, the more the spectrum corresponding to the fitted signal overlap model deviates from the current spectrum.

[0066] When the deviation exceeds a set deviation threshold, the method for adjusting the first initial wavelength and the second initial wavelength includes: Step 51: Adjust the trend of change of one of the initial wavelengths by a fixed amount of wavelength change to obtain the updated initial wavelength. The fixed wavelength change is less than the difference between the first initial wavelength and the second initial wavelength; Step 52: Extract the predicted peak value and predicted half-peak width corresponding to one of the initial wavelengths after adjustment, and update the signal overlap model; Step 53: Determine the cumulative deviation between the light intensity values ​​corresponding to each wavelength in the updated signal overlap model and the light intensity values ​​corresponding to each wavelength in the spectrum corresponding to the current position, and use this as the second deviation. Step 54: Determine whether the second deviation is less than the first deviation. The first deviation is the cumulative deviation between the light intensity data corresponding to each wavelength in the signal overlap model and the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position before the first and second initial wavelengths are adjusted. Step 55: If the second deviation is greater than the first deviation, then adjust the trend of the initial wavelength change in step 51 in the opposite direction. Specifically, if in step 51 the value of the first initial wavelength (or the second initial wavelength) is adjusted by a fixed wavelength change, thereby increasing the value of the first initial wavelength (or the second initial wavelength), and the calculated second deviation is greater than the first deviation when neither the first initial wavelength nor the second initial wavelength is adjusted, then the value of the first initial wavelength (or the second initial wavelength) is adjusted in the opposite direction, thereby decreasing the value of the first initial wavelength (or the second initial wavelength).

[0067] Step 56: If the second deviation is less than the first deviation, update the first deviation (take the second deviation, which is less than the first deviation, as the first deviation). Then continue to adjust the updated initial wavelength according to the trend in step 51, update the initial wavelength and the second deviation, until the second deviation is greater than the first deviation. Then adjust the trend of another initial wavelength and repeat the above steps until the updated first deviation is less than the set deviation threshold.

[0068] Specifically, when the second deviation is less than the first deviation, the first deviation is updated with the value of the second deviation, that is, the second deviation is assigned to the first deviation.

[0069] By using a fixed wavelength change, the value of one initial wavelength is adjusted one by one. Based on the second deviation between the light intensity corresponding to each wavelength in the updated signal overlap model after the initial wavelength adjustment and the light intensity corresponding to each wavelength in the actual spectrum corresponding to the current position, the trend of the initial wavelength after adjustment is determined based on the relationship between the second deviation and the first deviation. This makes the adjusted first and second initial wavelengths closer to the true wavelengths that converge to the upper and lower surfaces of the object under test, so as to improve the accuracy of the calculation results of the surface thickness of the object under test.

[0070] Based on the same inventive concept, such as Figure 5 As shown, this application also proposes a data processing method based on line spectral confocal microscopy to detect transparent test objects and obtain the thickness of the test objects, including: The image preprocessing module is used to obtain the spectrum corresponding to any position on the scan line, extract the peaks and peak wavelengths in the spectrum, and retain the peaks corresponding to the effective peak wavelengths in the spectrum, wherein the number of effective peak wavelengths is no more than two. The peak wavelength analysis module is used to analyze the light intensity corresponding to each wavelength in the spectrum to obtain a first initial wavelength and a second initial wavelength. The first initial wavelength is the initial wavelength that converges to the upper surface of the object to be tested, and the second initial wavelength is the initial wavelength that converges to the lower surface of the object to be tested. The parameter filtering module extracts the corresponding predicted peak value and predicted full width at half maximum (FWHM) from the spectra of those with peak values ​​only at the first and second initial wavelengths, respectively. The model deviation analysis module constructs a signal overlap model based on the predicted peak value and predicted full width at half maximum (FWHM), and accumulates the deviation between the light intensity data corresponding to each wavelength in the signal overlap model and the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position. The thickness detection module is used to determine whether the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, one of the initial wavelengths is adjusted sequentially, and the first and second initial wavelengths are updated until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, the difference between the measurement heights corresponding to the first and second initial wavelengths is calculated to determine the thickness of the object to be measured.

[0071] The specific implementation of the data processing system based on line spectral confocalization provided in this application can be referred to the above-described data processing method based on line spectral confocalization, and will not be repeated here.

[0072] In another embodiment, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements any of the above-described line spectral confocal data processing methods.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0075] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A data processing method based on line-spectral confocal, for detecting a transparent object to be measured to obtain the thickness of the object to be measured, characterized in that, include: Obtain the spectrum corresponding to any position on the scan line, extract the peak value and peak wavelength in the spectrum, and retain the peak corresponding to the effective peak wavelength in the spectrum. The number of effective peak wavelengths is equal to 2. By analyzing the light intensity corresponding to each wavelength in the spectrum, the first initial wavelength that converges to the upper surface of the object and the second initial wavelength that converges to the lower surface of the object are obtained. From the spectra with peak values ​​only at the first and second initial wavelengths, respectively, the corresponding predicted peak values ​​and predicted full width at half maximum (FWHM) are extracted. Based on the predicted peak value and predicted full width at half maximum (FWHM), the light intensity data corresponding to each wavelength after light intensity overlap is obtained, and the cumulative deviation between the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position is calculated. Determine if the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, adjust one of the initial wavelengths sequentially and update the first and second initial wavelengths until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, calculate the difference between the measurement heights corresponding to the first and second initial wavelengths to determine the thickness of the object to be measured.

2. The data processing method based on line-spectral confocal according to claim 1, characterized in that, The effective peak wavelength in the spectrum is determined by using a wavelength whose half-width at half-maximum (FWHM) is greater than the set width threshold corresponding to the peak wavelength. The set width threshold is a reference spectrum with only one peak and the peak wavelength is the FWHM corresponding to the peak wavelength in the spectrum.

3. The data processing method based on line-spectral confocal according to claim 1, characterized in that, The method for identifying the first and second initial wavelengths based on the light intensity data corresponding to each wavelength in the spectral diagram includes: A sliding window is used to perform polynomial least squares fitting on the light intensity data within the window. The light intensity data corresponding to the polynomial at the center point of the sliding window are selected to process the light intensity data corresponding to each wavelength within the sliding window and obtain a smooth and complete signal. Count the number of peaks in the current spectrum; Determine if the number of peaks in the current spectrum is equal to 2. If it is equal to 2, then preliminarily determine the initial wavelengths of the two peaks.

4. The data processing method based on line spectral confocalization according to claim 3, characterized in that, The second derivative of the polynomial of the spectral signal corresponding to the spectrum is taken, and the first and second initial wavelengths corresponding to the second derivative being equal to 0 are selected.

5. The data processing method based on line-spectral confocal according to claim 3, characterized in that, Methods for determining the initial wavelength based on peak values ​​in a spectral image include: Calculate the full width at half maximum (FWHM) threshold based on the peak values ​​in the spectral image; Find the intersection point between the full width at half maximum (FWHM) threshold and the light intensity data in the spectral image, determine the number of intersection points, and extract the wavelength corresponding to the intersection point as the intersection wavelength; If the number of intersection points is not equal to 4, then the second derivative of the polynomial of the spectral signal is taken, and the wavelengths corresponding to the previous wavelength and the next wavelength are selected. Determine the relationship between the wavelength and the wavelength at the first intersection point and the wavelength at the second intersection point. If the wavelength is greater than the wavelength at the second intersection point or less than the wavelength at the first intersection point, the wavelength is lost; otherwise, if the wavelength is between the wavelength at the first intersection point and the wavelength at the second intersection point, the wavelength is retained. The wavelengths are extracted and retained, and based on the first and second intersection wavelengths, the two initial wavelengths that converge to the upper and lower surfaces of the test object are analyzed.

6. The data processing method based on line-spectral confocal according to claim 5, characterized in that, The first initial wavelength that converges to the upper surface of the test object is obtained by averaging the sum of the first intersection wavelength and the retained wavelength. The second initial wavelength that converges to the lower surface of the test object is obtained by averaging the sum of the second intersection wavelength and the retained wavelength.

7. The data processing method based on line-spectral confocal according to claim 5, characterized in that, When the number of intersection points is equal to 4, based on the wavelengths of the first, third, fourth, and second intersection points, and combined with the light intensity corresponding to each wavelength within the intervals between the first and third intersection points and between the fourth and second intersection points, the centroid method is used to calculate and obtain the first initial wavelength that converges to the upper surface of the object under test and the second initial wavelength that converges to the lower surface of the object under test.

8. The data processing method based on line-spectral confocal according to claim 1, characterized in that, A signal overlap model is constructed to obtain light intensity data corresponding to each wavelength after light intensity overlap, and the signal overlap model is ; E0 represents the light intensity value corresponding to wavelength λ after the light intensity overlaps, E0 represents the background noise, n represents the number of times the monochromatic light after dispersion by the dispersive objective converges to the surface of the object under test and undergoes reflection, and A represents the light intensity value corresponding to wavelength λ after the light intensity overlaps. i This represents the peak value corresponding to the peak wavelength when the i-th initial wavelength converges onto the non-transparent surface of the analyte, where λ represents any wavelength in the spectrum. i0 Let w represent the initial wavelength corresponding to the i-th peak. i β is represented as the full width at half maximum (FWHM) of the i-th peak wavelength. i The shape of the i-th peak wavelength is represented by β. For non-transparent analytes, the spectral data converged to the surface of the analyte exhibits a Gaussian distribution. i The value is 2.

9. A data processing system based on line-spectral confocal, applied to the data processing method based on line-spectral confocal in any one of claims 1-8, characterized in that, include: The image preprocessing module is used to obtain the spectrum corresponding to any position on the scan line, extract the peak value and peak wavelength in the spectrum, and retain the peak corresponding to the effective peak wavelength in the spectrum, wherein the number of effective peak wavelengths is equal to two. The peak wavelength analysis module is used to analyze the light intensity corresponding to each wavelength in the spectrum to obtain the first initial wavelength that converges to the upper surface of the test object and the second initial wavelength that converges to the lower surface of the test object. The parameter filtering module extracts the corresponding predicted peak value and predicted full width at half maximum (FWHM) from the spectra of those with peak values ​​only at the first and second initial wavelengths, respectively. The model bias analysis module obtains the light intensity data corresponding to each wavelength after light intensity overlap based on the predicted peak value and predicted half-peak width, and calculates the cumulative bias between the light intensity data corresponding to each wavelength in the spectrum corresponding to the current position. The thickness detection module is used to determine whether the deviation is less than the set deviation threshold. If it is greater than the set deviation threshold, one of the initial wavelengths is adjusted sequentially, and the first and second initial wavelengths are updated until the deviation is less than the set deviation threshold. If it is less than the set deviation threshold, the difference between the measurement heights corresponding to the first and second initial wavelengths is calculated to determine the thickness of the object to be measured.

10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the data processing method based on line spectral confocalization as described in any one of claims 1-8.

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