Method for obtaining three-dimensional microstructure topography of material surface

Abstract

A kind of material surface three-dimensional microstructure topography acquisition method, comprising: using OCT system to sample scanning, acquisition three-dimensional interference spectrum signal, obtain the complex signal containing sample structure information and three-dimensional OCT image;All B-scan images in three-dimensional OCT image are extracted material surface profile, remove material base and noise signal, extract the phase information of complex signal, generate phase difference image;Finally realize the acquisition of material surface three-dimensional microstructure topography.The advantages and beneficial effects compared with prior art of the present application are: simple and efficient, can quickly realize the non-contact three-dimensional imaging of micron level and nanometer level microstructure of arbitrary curved surface or planar material surface, obtain material surface three-dimensional microstructure topography, save computing resources.

Description

Technical Field

[0001] This invention belongs to the field of material testing technology where the surface is arbitrarily curved or flat. Specifically, it relates to a method for obtaining the three-dimensional microstructure morphology of a material surface. Background Technology

[0002] Surface defect detection for arbitrary curved surfaces plays a crucial role in precision component manufacturing, life sciences, and precision optics. Taking optical lenses as an example, surface defects, including scratches, micro-impurities, bubbles, and cracks, are typically major factors affecting component performance and lifespan. High-precision surface defect detection for aspherical lenses is of great significance for locating defect positions, determining defect types, and studying defect growth processes.

[0003] Commonly used microstructure imaging techniques provide methods for analyzing the surface structural properties of materials. Common optical and non-optical methods include atomic force microscopy (AFM), scanning electron microscopy (SEM), fluorescence imaging, and white light interferometry. While these methods can meet some detection needs, they also have limitations—AFM has high requirements for the sample and the detection environment and is a contact measurement; SEM requires the sample to be a conductor or semiconductor; fluorescence imaging methods have a certain degree of material selectivity; and white light interferometry generally cannot perform nanometer-resolution imaging of surface microstructures.

[0004] Optical coherence tomography (OCT) is a non-contact, non-destructive, and high-resolution three-dimensional visualization real-time imaging technique. Based on the principles of interferometry and heterodyne detection, and relying on a broadband light source, it can achieve imaging of the microstructure of material surfaces. However, conventional OCT structural imaging or visualization methods typically only achieve micrometer-level resolution, making it difficult to image the nanometer-level microstructure of arbitrary surfaces.

[0005] Therefore, there is an urgent need for a method to obtain the three-dimensional microstructure morphology of material surfaces, which can quickly, non-destructively, and comprehensively detect the micron to nanometer scale of arbitrary surface structures of materials. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for obtaining the three-dimensional microstructure morphology of material surfaces, optimize the OCT microstructure imaging capability, and enable rapid, non-destructive and comprehensive visualization of the micron to nanoscale structure of arbitrary material surfaces.

[0007] To achieve the above objectives, the present invention provides a method for obtaining the three-dimensional microstructure morphology of a material surface, comprising:

[0008] Step 1: Use an optical coherence tomography (OCT) system to scan the sample and acquire three-dimensional interference spectral signals. Perform inverse Fourier transform along the depth dimension to obtain complex signals containing sample structural information and a three-dimensional OCT image with micron-level resolution in the longitudinal direction.

[0009] Step 2: Select a B-scan image from the three-dimensional OCT image, and extract the material surface contour from the B-scan image with a vertical resolution of micrometers.

[0010] Step 3: Based on the obtained material surface profile, extract noise, material substrate, and microstructure signals; iteratively calculate the material substrate signal; and obtain the B-scan image after subtracting the material substrate signal, including:

[0011] Step 3.1: Based on the material surface contour information of the B-scan image, extract noise, material substrate, and microstructure signals;

[0012] Step 3.2: Utilizing the sparse and discrete nature of the microstructure information on the material surface, iterative calculations are performed to obtain the microstructure signal and the material substrate signal;

[0013] Step 3.3: Remove the material substrate signal from the complex signal containing sample structural information to generate a B-scan image with the material substrate signal subtracted.

[0014] Step 4: Extract the phase information from the complex signal of the sample structure information in the B-scan image after subtracting the material substrate signal, calculate the phase difference relative to a certain position P(x0,z0), and generate a phase difference image; use the phase difference image to calculate the nanometer-level depth difference of the material surface in the B-scan image.

[0015] Step 5: Repeat steps 2-4 for all B-scan images in the 3D OCT image to obtain the 3D microstructure morphology of the material surface.

[0016] Further: In step 2, the method for extracting the material surface contour in a B-scan image with a longitudinal resolution of micrometers is to set the approximate depth range corresponding to the sample surface position and select the maximum intensity in the depth direction as the sample surface contour.

[0017] Further: In step 3.1, the method for extracting noise, material substrate, and microstructure signals based on the material surface contour information of the B-scan image is as follows: The surface contour signal of the B-scan image along the x-direction is defined as follows:

[0018] s(x)=p(x)+b(x)+e(x) (1)

[0019] Where p(x), b(x), and e(x) represent the positions of the microstructure, the material substrate, and the noise in the depth direction, respectively.

[0020] Furthermore, in step 3.2, the microstructure signal is obtained by iterative calculation by utilizing the sparse and discrete characteristics of the microstructure information on the material surface. The calculation is shown in formula (2):

[0021]

[0022] Where arg min represents finding the minimum value of the objective function F(p(x)); The square of the L2 norm is given; H is a high-pass filter whose cutoff frequency represents the ratio of the number of points in one signal cycle to the total number of points in the x-direction; D i α is the i-th order difference factor; φ is the penalty function; α i M is the regularization parameter, taking a real number greater than or equal to 0; M is D i The highest order; N i The total number of points taken along the x-direction; adjust the high-pass filter to adapt to different noise signals, and adjust the penalty function and regularization parameter to adjust the sparsity to adapt to different microstructure distributions.

[0023] Further: In step 4, the method for calculating the phase difference relative to a certain position P(x0,z0) is to calculate the phase difference between the complex signals of two adjacent and symmetrical positions P1 and P2 that are directly adjacent to the position P(x0,z0). This phase difference is the phase difference of the position P, expressed as: Its calculation is shown in formula (3):

[0024]

[0025] Among them, C P1 and C P2 Let P1 and P2 be complex signals, and Angle() denotes taking the phase of the complex signal.

[0026] Further: In step 4, the nanometer-scale depth difference Δz on the material surface in the B-scan image is calculated. P The method is to calculate using formula (4):

[0027]

[0028] in, Let λ be the phase difference at a certain position P on the B-scan image. c λ is the center wavelength of the OCT system light source, and n is the refractive index of the sample.

[0029] The method for acquiring the three-dimensional microstructure morphology of material surfaces described in this invention utilizes an OCT system for collecting interference spectral information, including but not limited to: a full-field OCT system, a time-domain OCT system, a spectral-domain OCT system, and a swept-frequency source OCT system. This method can be applied to the detection of roughness or microstructural defects in materials with arbitrary curved or planar surfaces, achieving non-contact measurement.

[0030] The advantages and beneficial effects of this invention compared with the prior art are:

[0031] (1) The method of the present invention can perform non-contact three-dimensional imaging of the micro-scale and nano-scale microstructures of any curved or planar material surface to obtain the three-dimensional microstructure morphology of the material surface.

[0032] (2) The present invention adopts a simple and efficient method of imaging from coarse to fine, which can quickly realize the imaging of micron-level three-dimensional microstructures on the material surface and further obtain nano-level microstructure images.

[0033] (3) The present invention can detect the roughness or microstructure defects of any curved or planar material surface in a non-contact manner. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope.

[0035] Figure 1 This is a flowchart illustrating the method for obtaining the three-dimensional microstructure morphology of a material surface.

[0036] Figure 2 The diagram shows the three-dimensional microstructure of the aspherical lens surface; wherein, (a) is the three-dimensional microstructure of the aspherical lens surface obtained by the detection method in this embodiment; (b) is the intensity map of a certain B-scan image of the aspherical lens in depth obtained by the detection method in this embodiment; and (c) is the nanoscale microstructure of the aspherical lens surface obtained by the detection method in this embodiment. Detailed Implementation

[0037] 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. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0038] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 This is a flowchart illustrating the method for acquiring the three-dimensional microstructure morphology of a material surface based on an OCT system according to the present invention. The OCT system used to acquire interference spectral information includes, but is not limited to: a full-field OCT system, a time-domain OCT system, a spectral-domain OCT system, and a swept-frequency source OCT system. The imaging method includes:

[0041] Step 100: Use an OCT system to scan the sample and acquire a three-dimensional interference spectral signal containing information about the sample's depth dimension;

[0042] Step 200: Perform an inverse Fourier transform along the depth dimension on the three-dimensional interference spectral signal to obtain a complex signal containing sample structural information and a three-dimensional OCT image with micrometer-level resolution in the longitudinal direction.

[0043] Step 300: Select a B-scan image from the three-dimensional OCT image, and extract the material surface contour from the B-scan image with a vertical resolution of micrometers.

[0044] Step 400: Based on the material surface contour information of the B-scan image, extract noise, material substrate, and microstructure signals;

[0045] Step 500: Utilizing the sparse and discrete nature of the microstructure information on the material surface, iterative calculations are performed to obtain the microstructure signal and the material substrate signal;

[0046] Step 600: Remove the material substrate signal from the complex signal containing sample structural information to generate a B-scan image with the material substrate signal subtracted;

[0047] Step 700: Extract the phase information from the complex signal of the sample structure information in the B-scan image after subtracting the material substrate signal, calculate the phase difference relative to a certain position P(x0,z0), and generate a phase difference image;

[0048] Step 800: Using the phase difference image, calculate the nanometer-scale depth difference of the material surface in the B-scan image;

[0049] Step 900: Repeat the above steps for all B-scan images in the 3D OCT image to obtain the 3D microstructure morphology of the material surface.

[0050] Figure 2 The diagram shows the three-dimensional microstructure of the aspherical lens surface; wherein, (a) is the three-dimensional microstructure of the aspherical lens surface obtained by the detection method in this embodiment; (b) is the intensity map of a certain B-scan image of the aspherical lens in depth obtained by the detection method in this embodiment; and (c) is the nanoscale microstructure of the aspherical lens surface obtained by the detection method in this embodiment.

[0051] This embodiment employs a spectral domain OCT system. The OCT system parameters are: a low-coherence light source center wavelength of 840 nm, a bandwidth of 100 nm, a lateral resolution of 3.1 μm, a vertical micrometer-level resolution of 3.4 μm (in air), an acquisition speed of 25 kHz, an en-face acquisition pixel count of 1000 × 1000, and an imaging range of 4 mm × 4 mm. The specific implementation process involves using the spectral domain OCT system to acquire the interference spectrum signal of the aspherical lens surface, performing an inverse Fourier transform along the depth dimension to obtain a complex signal containing the surface structure information of the aspherical lens and a three-dimensional microstructure map of the aspherical lens surface; wherein, the three-dimensional microstructure map of the aspherical lens surface is as follows. Figure 2 As shown in (a), the figure uses the grayscale value of the color to represent the depth dimension information of the aspherical lens surface; from Figure 2 (a) selects a sample's structural intensity distribution along its depth dimension, i.e., the B-scan image, as shown. Figure 2 As shown in (b), the maximum intensity in the depth direction of the B-scan image is selected as the sample surface contour; noise, material substrate, and microstructure signals are extracted from the surface contour information of the B-scan image, and the surface contour signal along the x-direction of the B-scan image is set according to formula (1); utilizing the sparse and discrete characteristics of the material surface microstructure information, the penalty function and regularization parameter are adjusted according to formula (2) to adjust the sparsity to adapt to the microstructure distribution of the aspherical lens surface, and the material substrate signal is obtained; using Figure 2 (b) Remove the material substrate signal from the complex signal containing sample structure information in the corresponding B-scan image to generate a B-scan image with the material substrate signal subtracted; extract the phase information from the complex signal of sample structure information in the B-scan image after subtracting the material substrate signal, and calculate P(x0, z) relative to a certain position using formula (3). 0)The phase difference is used to generate a phase difference image; based on the phase difference image, the nanometer-level depth difference of the B-scan image is calculated using formula (4); the above steps are repeated for all B-scan images in the three-dimensional OCT image to obtain the three-dimensional microstructure morphology of the aspherical lens surface, such as Figure 2 (c) The embodiments of the present invention achieve a detection sensitivity of 15.7 nm nanometer-level difference along the depth dimension. Figure 2 (c) uses color grayscale values ​​to represent the depth dimension information of the aspherical lens surface. Figure 2 (c) As can be seen, nanometer-level resolution imaging can clearly distinguish microscopic scratches and defects on the surface of aspherical lenses; in this embodiment, the high-pass filter cutoff frequency is 0.02, and the penalty function... Where r = 6, ∈ = 1 × 10 -6 The independent variable q = D in the penalty function φ i p(x); a total of 2nd order differences were calculated, M = 2; regularization parameter α i The chosen values ​​are α0 = 0.4, α1 = 4, and α2 = 3.2. The total number of points N in the x-direction is... i It is 1000.

[0052] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it. They should not be used to limit the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for obtaining the three-dimensional microstructure morphology of a material surface, characterized in that, include: Step 1: Use an optical coherence tomography (OCT) system to scan the sample and acquire three-dimensional interference spectral signals. Perform inverse Fourier transform along the depth dimension to obtain complex signals containing sample structural information and three-dimensional OCT images with micron-level resolution in the longitudinal direction. Step 2: Select a B-scan image from the three-dimensional OCT image, and extract the material surface contour from the B-scan image with a vertical resolution of micrometers. Step 3: Based on the obtained material surface profile, extract noise, material substrate, and microstructure signals; iteratively calculate the material substrate signal to obtain a B-scan image after subtracting the material substrate signal; the method includes: Step 3.1: Based on the material surface contour information of the B-scan image, extract noise, material substrate, and microstructure signals; Step 3.2: Utilizing the sparse and discrete nature of the microstructure information on the material surface, iteratively calculate the microstructure signal and the material substrate signal; obtain the microstructure signal. The calculation is shown in formula (2): (2) Where arg min represents finding the minimum value of the objective function F(p(x)); H represents the square of the L2 norm; H is a high-pass filter whose cutoff frequency represents the ratio of the number of points in one signal cycle to the total number of points in the x-direction. It is the i-th order difference factor; For the penalty function; The regularization parameter takes a real number greater than or equal to 0; M is... The highest order; N i The total number of points taken along the x-direction; adjust the high-pass filter to adapt to different noise signals, and adjust the penalty function and regularization parameter to adjust the sparsity to adapt to different microstructure distributions; Step 3.3: Remove the material substrate signal from the complex signal containing sample structural information to generate a B-scan image with the material substrate signal subtracted; Step 4: Extract the phase information from the complex signal of the sample structure information in the B-scan image after subtracting the material substrate signal, and generate a phase difference image; use the phase difference image to calculate the nanometer-level depth difference of the material surface in the B-scan image; Step 5: Repeat steps 2-4 for all B-scan images in the 3D OCT image to obtain the 3D microstructure morphology of the material surface.

2. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that, The method for extracting the material surface contour in a B-scan image with a longitudinal resolution of micrometers, as described in step 2, involves setting an approximate depth range corresponding to the sample surface position and selecting the maximum intensity value in the depth direction as the sample surface contour.

3. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that... The method for extracting noise, material substrate, and microstructure signals based on the material surface contour information of B-scan images is as follows: The surface contour information of the B-scan image along the x-direction is defined as follows: (1) Where p(x), b(x), and e(x) represent the positions of the microstructure, the material substrate, and the noise in the depth direction, respectively.

4. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that, The method for generating the phase difference image described in step 4 is to extract the phase information from the complex signal of the B-scan image sample structure information after subtracting the material substrate signal, calculate the phase difference relative to a certain position P(x0,z0), and generate the phase difference image.

5. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 4, characterized in that... The method for calculating the phase difference relative to a certain position P(x0,z0) is as follows: calculate the phase difference between the complex signals of two adjacent and symmetrical positions P1 and P2 that are at the same position P(x0,z0). This phase difference is the phase difference at position P, expressed as: Its calculation is shown in formula (3): (3) Among them, C P1 and C P2 Let P1 and P2 be the complex signals at the two positions, and Angle() represents taking the phase of the complex signal.

6. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that... Step 4 describes calculating the nanometer-scale depth difference of the material surface in the B-scan image. The method is to calculate using formula (4): (4) in, Let P be the phase difference at a certain position P on the B-scan image. λ is the center wavelength of the OCT system light source, and n is the refractive index of the sample.

7. The method for obtaining the three-dimensional microstructure morphology of a material surface according to any one of claims 1 to 6, characterized in that... OCT systems used to acquire sample interference spectral signals include, but are not limited to: full-field OCT systems, time-domain OCT systems, spectral-domain OCT systems, and swept-source OCT systems.

8. The method for obtaining the three-dimensional microstructure morphology of a material surface according to any one of claims 1 to 6, characterized in that, This method can be applied to the detection of roughness or microstructural defects in materials with arbitrary curved or flat surfaces, achieving non-contact measurement.

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