Method for acquiring three-dimensional microstructure morphology of material surface

Abstract

A material surface three-dimensional microstructure morphology obtaining method comprises the steps that a sample is scanned through an OCT system, three-dimensional interference spectrum signals are collected, and complex signals containing sample structure information and a three-dimensional OCT image are obtained; extracting material surface contours from all B-scan images in the three-dimensional OCT image, removing material substrates and noise signals, extracting phase information of complex signals, and generating a phase difference image; and finally obtaining the three-dimensional microstructure morphology on the surface of the material. Compared with the prior art, the method has the advantages and beneficial effects that the method is simple and efficient, non-contact three-dimensional imaging of micron-scale and nano-scale microstructures of any curved surface or plane material surface can be quickly realized, the three-dimensional microstructure morphology of the material surface is obtained, and computing resources are saved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material detection with an arbitrary curved or flat surface, and in particular relates to a method for acquiring the three-dimensional microstructure morphology of a material surface. Background Art

[0002] Defect detection on arbitrarily curved surfaces plays a crucial role in fields such as precision component manufacturing, life sciences, and precision optics. Taking optical lenses as an example, surface defects such as scratches, micro-impurities, bubbles, and cracks are often the primary factors affecting component performance and lifespan. Surface defect detection on high-precision aspheric lenses is crucial for locating defect locations, 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, scanning electron microscopy, fluorescence imaging, and white light interferometry. While these methods can meet some inspection needs, they also have certain limitations. Atomic force microscopy has high requirements for the sample and inspection environment, and is a contact measurement method; scanning electron microscopy requires the sample to be a conductor or semiconductor; fluorescence imaging methods are somewhat selective in materials; and white light interferometry generally cannot achieve nanometer-resolution imaging of surface microstructures.

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

[0005] Therefore, there is an urgent need for a method to obtain the three-dimensional microstructure morphology of the material surface, which can quickly, non-destructively and comprehensively realize the micron to nanometer level detection of any surface structure of the material. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for acquiring the three-dimensional microstructure morphology of the material surface, optimize the OCT microstructure imaging capability, and quickly, non-destructively and comprehensively visualize the micron to nanometer scale structure of the surface of any material morphology.

[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: Scan the sample using an optical coherence tomography (OCT) system to collect three-dimensional interference spectral signals. Perform an inverse Fourier transform along the depth dimension to obtain a complex signal 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 longitudinal resolution of micrometer level;

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

[0011] Step 3.1: Extracting noise, material base, and microstructure signals based on the material surface profile information of the B-scan image;

[0012] Step 3.2: Utilizing the sparse and discrete characteristics of the material surface microstructure information, iterative calculation is performed to obtain the microstructure signal and the material substrate signal;

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

[0014] Step 4: 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 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 to 4 for all B-scan images in the three-dimensional OCT image to obtain the three-dimensional microstructure morphology of the material surface.

[0016] Furthermore, in step 2, the method for extracting the material surface profile in the B-scan image with a longitudinal resolution of micrometer level 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 profile.

[0017] Furthermore, in step 3.1, based on the material surface profile information of the B-scan image, the method for extracting noise, material base and microstructure signals is to set the surface profile signal of the B-scan image along the x direction to be:

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

[0019] Among them, the sizes of p(x), b(x), and e(x) represent the positions of the microstructure, material base, and noise in the depth direction, respectively.

[0020] Further: In step 3.2, the sparse and discrete characteristics of the material surface microstructure information are used to iteratively calculate the microstructure signal The calculation of is shown in formula (2):

[0021]

[0022] Among them, arg min means finding the minimum value of the objective function F(p(x)); represents the square of the second norm; H is a high-pass filter, whose cutoff frequency represents the ratio of the number of points in a signal cycle to the total number of points in the x direction; D i is the i-order difference factor; φ is the penalty function; α i is a regularization parameter, which is a real number greater than or equal to 0; M is D i The highest order of N i is the total number of points taken along the x direction; the high-pass filter is adjusted to adapt to different noise signals, and the penalty function and regularization parameters are adjusted 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 of the complex signals of two positions P1 and P2 that are adjacent and symmetrical to the position P(x0,z0). The phase difference is the phase difference of the position P, which is expressed as Its calculation is shown in formula (3):

[0024]

[0025] Among them, C P1 and C P2 are the complex signals at the two positions P1 and P2, and Angle() represents the phase of the complex signal.

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

[0027]

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

[0029] In the method for acquiring the three-dimensional microstructural morphology of a material surface described herein, the OCT systems used to collect interferometric spectral information include, but are not limited to, full-field OCT systems, time-domain OCT systems, spectral-domain OCT systems, and swept-source OCT systems. The method can be applied to detect the roughness or microstructural defects of materials with arbitrarily curved or flat surfaces, enabling non-contact measurement.

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

[0031] (1) The method of the present invention can perform non-contact three-dimensional imaging of the microstructure of any curved or flat surface material at the micrometer and nanometer levels, thereby obtaining 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 three-dimensional microstructure imaging of the material surface at the micron level and further obtain nanoscale microstructure images;

[0033] (3) The present invention can realize the detection of the roughness or microstructure defects of any curved or flat surface material in a non-contact manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope.

[0035] Figure 1 Schematic diagram of the process for obtaining the three-dimensional microstructure morphology of the material surface;

[0036] Figure 2 is a three-dimensional microstructure diagram of the aspheric lens surface; wherein, (a) is a three-dimensional microstructure diagram of the aspheric lens surface obtained by the detection method according to this embodiment; (b) is an intensity diagram of a B-scan image of the aspheric lens at depth obtained by the detection method according to this embodiment; (c) is a nanoscale microstructure diagram of the aspheric lens surface obtained by the detection method according to this embodiment. DETAILED DESCRIPTION

[0037] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0038] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application as claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative efforts are within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 This is a flow chart of the method for acquiring 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 spectrum 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-source OCT system. The imaging method includes:

[0041] Step 100: Scan the sample using an OCT system to collect a three-dimensional interference spectrum signal containing depth dimension information of the sample;

[0042] Step 200: performing an inverse Fourier transform along the depth dimension on the three-dimensional interference spectrum signal to obtain a complex signal containing sample structure information and a three-dimensional OCT image with micron-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 longitudinal resolution of micrometer level;

[0044] Step 400: extracting noise, material base and microstructure signals based on the material surface profile information of the B-scan image;

[0045] Step 500: utilizing the sparse and discrete characteristics of the material surface microstructure information, iteratively calculate and obtain the microstructure signal and the material substrate signal;

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

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

[0048] Step 800: Calculating the nanometer-level depth difference of the material surface in the B-scan image using the phase difference image;

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

[0050] Figure 2 is a three-dimensional microstructure diagram of the aspheric lens surface; wherein, (a) is a three-dimensional microstructure diagram of the aspheric lens surface obtained by the detection method according to this embodiment; (b) is an intensity diagram of a B-scan image of the aspheric lens at depth obtained by the detection method according to this embodiment; (c) is a nanoscale microstructure diagram of the aspheric lens surface obtained by the detection method according to this embodiment.

[0051] This embodiment adopts a spectral domain OCT system, and the OCT system parameters are: the central wavelength of the low-coherence light source is 840nm, the bandwidth is 100nm, the system lateral resolution is 3.1μm, the longitudinal micron-level resolution is 3.4μm (in air), the acquisition speed is 25kHz, the en face acquisition pixel point is 1000×1000, and the imaging range is 4mm×4mm. The specific implementation process is to use the spectral domain OCT system to collect the interference spectrum signal of the aspheric lens surface, perform inverse Fourier transform along the depth dimension, obtain a complex signal containing the structural information of the aspheric lens surface and a three-dimensional microscopic structure map of the aspheric lens surface; wherein, the three-dimensional microscopic structure map of the aspheric lens surface is as shown in FIG. Figure 2 As shown in (a), the color grayscale value is used to represent the depth dimension of the aspheric lens surface; Figure 2 (a) Select a sample structure intensity distribution along the depth dimension, that is, the B-scan image, such as Figure 2 As shown in (b), the maximum intensity in the depth direction of the B-scan image is selected as the sample surface profile; noise, material base and microstructure signals are extracted from the surface profile information of the B-scan image, and the surface profile signal of the B-scan image along the x direction is set according to formula (1); using 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 aspheric lens surface, and the material base signal is obtained; using Figure 2 (b) removing the material base signal from the complex signal containing the sample structure information in the corresponding B-scan image to generate a B-scan image with the material base signal subtracted; extracting the phase information from the complex signal containing the sample structure information in the B-scan image after subtracting the material base signal, and calculating the phase information relative to a certain position P(x0, z 0)The phase difference is calculated 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 aspheric lens surface, as shown in the figure. Figure 2 (c) The embodiment of the present invention achieves a detection sensitivity of 15.7 nm nanometer-level difference along the depth dimension. Figure 2 In (c), the color grayscale value is used to represent the depth dimension of the aspheric lens surface. Figure 2 (c) It can be seen that nanometer-level resolution imaging can clearly distinguish microscopic scratches and defects on the surface of aspheric 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 in the penalty function φ=D i p(x); a total of 2-order differences are calculated, M = 2; regularization parameter α i Select α0=0.4, α1=4, α2=3.2, and the total number of points in the x direction is N i is 1000.

[0052] The above embodiments are only for illustrating the technical concept and features of the present invention. Their purpose is to enable people familiar with this technology to understand the content of the present invention and implement it. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in 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: Scan the sample using an optical coherence tomography (OCT) system to collect three-dimensional interference spectral signals. Perform an inverse Fourier transform along the depth dimension to obtain a complex signal containing sample structural information and a three-dimensional OCT image 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 longitudinal resolution of micrometer level; Step 3: Based on the obtained material surface profile, extract the noise, material substrate, and microstructure signals, iteratively calculate the material substrate signal, and obtain the B-scan image after subtracting the material substrate signal; Step 4: Extracting phase information from the complex signal of the B-scan image sample structure information after subtracting the material substrate signal to generate a phase difference image; using the phase difference image, calculating the nanometer-level depth difference of the material surface in the B-scan image; Step 5: Repeat steps 2 to 4 for all B-scan images in the three-dimensional OCT image to obtain the three-dimensional microstructure morphology of the material surface.

2. The method for obtaining the three-dimensional microstructure morphology of the material surface according to claim 1, characterized in that: The method for extracting the material surface profile from the B-scan image with a longitudinal resolution of micrometer level described in step 2 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 profile.

3. The method for obtaining the three-dimensional microstructure morphology of the material surface according to claim 1, characterized in that: The method for obtaining the B-scan image after subtracting the material base signal in step 3 includes: Step 3.1: Extracting noise, material base, and microstructure signals based on the material surface profile information of the B-scan image; Step 3.2: Utilizing the sparse and discrete characteristics of the material surface microstructure information, iterative calculation is performed to obtain the microstructure signal and the material substrate signal; Step 3.3: Remove the material base signal from the complex signal containing the sample structure information to generate a B-scan image with the material base signal subtracted.

4. The method for obtaining the three-dimensional microstructure morphology of the material surface according to claim 3, characterized in that Based on the material surface profile information of the B-scan image, the method for extracting noise, material base and microstructure signals is as follows: the surface profile information of the B-scan image along the x direction is set as: s(x)=p(x)+b(x)+e(x) (1) Among them, the sizes of p(x), b(x), and e(x) represent the positions of the microstructure, material base, and noise in the depth direction, respectively.

5. The method for obtaining the three-dimensional microstructure morphology of the material surface according to claim 3, characterized in that: The microstructure signal is obtained by iterative calculation by utilizing the sparse and discrete characteristics of the material surface microstructure information. The calculation of is shown in formula (2): Among them, arg min means finding the minimum value of the objective function F(p(x)); represents the square of the second norm; H is a high-pass filter, whose cutoff frequency represents the ratio of the number of points in a signal cycle to the total number of points in the x direction; D i is the i-order difference factor; φ is the penalty function; α i is a regularization parameter, which is a real number greater than or equal to 0; M is D i The highest order of N i is the total number of points taken along the x direction; the high-pass filter is adjusted to adapt to different noise signals, and the penalty function and regularization parameters are adjusted to adjust the sparsity to adapt to different microstructure distributions.

6. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that: The method for generating a 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 a phase difference image.

7. The method for obtaining the three-dimensional microstructure morphology of the material surface according to claim 6, characterized in that The method for calculating the phase difference relative to a certain position P(x0,z0) is to calculate the phase difference of the complex signals of two positions P1 and P2 that are adjacent and symmetrical to the position P(x0,z0). This phase difference is the phase difference of the position P, which is expressed as Its calculation is shown in formula (3): Among them, C P1 and C P2 are the complex signals at the two positions P1 and P2, and Angle() represents the phase of the complex signal.

8. The method for obtaining the three-dimensional microstructure morphology of a material surface according to claim 1, characterized in that Calculate the nanoscale depth difference Δz of the material surface in the B-scan image as described in step 4 P The method is calculated by formula (4): in, is the phase difference at a certain position P on the B-scan image, λ c is the central wavelength of the OCT system light source, and n is the refractive index of the sample.

9. The method for obtaining the three-dimensional microstructure morphology of a material surface according to any one of claims 1 to 8, characterized in that OCT systems used to collect 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.

10. The method for obtaining the three-dimensional microstructure of a material surface according to any one of claims 1 to 8, wherein The characteristic of this method is that it can be applied to the detection of roughness or microstructural defects of materials with arbitrary curved or flat surfaces. Achieve non-contact measurement.

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