Surface analysis method and surface analysis device

Abstract

The present invention can perform highly accurate analysis when visualizing the analysis results in spectroscopic imaging. A surface analysis method has the following steps: a step of acquiring spectroscopic image data of a sample surface using a spectroscopic camera; a step of extracting n wavelengths dispersed in a specific wavelength range from the acquired spectroscopic image data and setting the spectrum of each wavelength in the spectroscopic image data as an n-dimensional space vector for each pixel; a step of normalizing the space vector for each pixel; a step of clustering the normalized space vector in a specific number of categories; and a step of identifying and displaying the pixels clustered in the category for each category.

Description

Technical Field

[0001] The invention relates to a surface analysis method and a surface analysis device. Background Art

[0002] As one of the methods of surface analysis, spectroscopic imaging is known, which uses a spectroscopic camera (hyper-spectrum sensor, etc.) to obtain distribution data (spectroscopic image data) storing spectroscopic spectrum information for each pixel, and visualizes the distribution of substances or compositions contained in the sample by analyzing the spectroscopic spectrum information for each pixel (for example, refer to the following patent document 1).

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2016-102769 Summary of the invention

[0006] Technical issues to be solved by the invention

[0007] The aforementioned spectroscopic imaging is used in various fields such as analysis of substances in biological samples, and statistical methods such as multivariate analysis are used in the analysis of spectroscopic image data. An effective method for visualizing spectroscopic image data is clustering of spectroscopic spectrum information. By displaying pixels of a group of spectroscopic spectrum information clustered in one classification in the same color, the analysis results based on the spectroscopic image data can be visualized as a color image.

[0008] It has been confirmed that in the visualization of such spectral image data, when analyzing the surface state of a multi-layer film sample, if the spectral image data of the reflected light acquired by the spectral camera is directly clustered, the analysis result may be inaccurate due to the interference of light reflected inside the multi-layer film.

[0009] In particular, when detecting defective parts in a multilayer film substrate such as a thin film transistor (TFT) by spectral imaging, as mentioned above, there is a problem that the outline of the defective part cannot be clearly visualized due to the interference of light reflected inside the multilayer film.

[0010] The present invention is proposed to solve this problem. That is, the subject of the present invention is to enable high-accuracy analysis when visualizing analysis results in spectroscopic imaging, and in particular, to enable clear visualization of the outline of a specific analysis object when analyzing a multilayer film-like sample.

[0011] Means for solving technical problems

[0012] In order to solve such a problem, the present invention has the following configuration.

[0013] A surface analysis method, characterized in that it has the following steps: a step of using a spectroscopic camera to acquire spectroscopic image data of a sample surface; a step of extracting n wavelengths dispersed within a specific wavelength range from the acquired spectroscopic image data, and setting the spectrum of each wavelength in the spectroscopic image data as an n-dimensional space vector for each pixel; a step of standardizing the space vector for each pixel; a step of clustering the standardized space vectors in a specific number of categories; and a step of identifying and displaying pixels clustered in the category for each category. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is an explanatory diagram showing the process of the surface analysis method according to the embodiment of the present invention.

[0015] Figure 2 It is an explanatory diagram showing the structure of a surface analysis device.

[0016] Figure 3 This is an explanatory diagram showing the functions of the information processing unit.

[0017] Figure 4 This is an explanatory diagram for explaining the process of vectorization in n-dimensional space.

[0018] Figure 5 This is an explanatory diagram for explaining the space vector normalization process.

[0019] Figure 6 This is an explanatory diagram for explaining the clustering process.

[0020] Figure 7 is an explanatory diagram showing an example of visualization (identification display) of clusters ( Figure 7 (a) is the standardized clustering, Figure 7 (b) is absolute value clustering).

[0021] Figure 8 It is an explanatory diagram showing a configuration example of a laser correction device including a surface analysis device. DETAILED DESCRIPTION

[0022] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Figure 1 As shown, the surface analysis method according to the embodiment of the present invention includes: a spectroscopic image data acquisition step S1, an n-dimensional space vectorization step S2 for each pixel, a space vector normalization step S3, a clustering step S4, and an identification and display step S5.

[0023] like Figure 2As shown, the surface analysis device 1 for executing such a process comprises: a spectroscopic camera 20 for acquiring spectroscopic image data of the surface of the sample W; an information processing unit 30 for analyzing and processing the acquired spectroscopic image data; and a display unit 40 for displaying the processing result of the information processing unit 30. Figure 2 The surface analysis device 1 shown magnifies and identifies defective portions of a multilayer film substrate as a sample W placed on a stage S, and a microscope 10 is provided in the aforementioned spectroscopic camera 20 .

[0024] exist Figure 2 In the figure, the microscope 10 is an optical microscope that irradiates the surface Wa of the multilayer film substrate as the sample W with white incident light to obtain an enlarged image of a unit area (for example, a pixel area of ​​a TFT substrate) for identifying a defective portion on the surface Wa, and is provided with an optical system such as an objective lens 11 or a tube lens 17, and also with a white light source 12 for irradiating the white incident light on the surface Wa and its optical system (a reflector 13 and a half mirror 14). In addition, the microscope 10 is provided with a monitoring camera 15 for obtaining a monitoring image of the enlarged image of the surface Wa and an optical system (a half mirror 16) used therein, etc. as needed.

[0025] The spectroscopic camera 20 configures a slit 23 and a grating element (diffraction grating) 21 on the optical axis 10P of the optical system of the microscope 10, so as to wavelength-separate the light reflected by the surface Wa, and images the separated light onto the imaging surface 22a of the two-dimensional camera 22 via the relay lens system 24, and obtains the spectroscopic spectrum information of the enlarged image of the surface Wa for each pixel of the imaging surface 22a by line spectrometry.

[0026] Figure 1 In the spectral image data acquisition step S1 , spectral image data (spectral spectrum information for each pixel) of the surface Wa in the sample W is acquired using the above-mentioned spectral camera 20 .

[0027] like Figure 3 As shown, the information processing unit 30 includes the following mechanisms: an n-dimensional space vectorization mechanism 31, which is software for executing the aforementioned n-dimensional space vectorization step S2 for each pixel; a space vector normalization mechanism 32, which is software for executing the space vector normalization step S3; a clustering mechanism 33, which is software for executing the clustering step S4; and an identification display mechanism 34, which is software for executing the identification display step S5. Thus, the input spectral image data is visualized and output as display image data.

[0028] The various analysis and processing steps based on the information processing unit 30 are explained. The n-dimensional space vectorization step S2 extracts n wavelengths dispersed in a specific wavelength range in the spectral image data acquired in the spectral image data acquisition step S1, and sets the spectrum of each wavelength in the spectral image data to an n-dimensional space vector.

[0029] like Figure 4 As shown, in the acquired spectral image data, a spectral spectrum information is stored for each pixel P (Xn, Yn) of the imaging surface 22a of the two-dimensional camera 22. A wavelength range of, for example, λ1 = 400nm and λn = 700nm is selected from the wavelength range of the spectral spectrum information, and the wavelength range is divided into (n-1) (for example, n = 200), and n wavelength (λ1 to λn) components are extracted, and an n-dimensional space vector is obtained by combining the wavelength (λ1 to λn) and the intensity (I1 to In) in each wavelength.

[0030] Then, if Figure 5 As shown, in the space vector normalization step S3, the n-dimensional space vector is normalized to obtain an n-dimensional normalized space vector. The normalization mentioned here refers to the process of obtaining a unit vector whose length (norm) becomes 1 while maintaining the direction of the n-dimensional space vector, and multiplying the n-dimensional space vector by the inverse of the norm of the space vector to obtain a unit vector (n-dimensional normalized space vector) with a norm of 1.

[0031] The clustering step S4 clusters the normalized n-dimensional space vector of each pixel into a specific number of categories. The number of categories here can be set according to the analysis object. For example, when extracting defective parts of a multi-layer film substrate such as a TFT substrate, a specified number of categories are set according to the structure of the TFT substrate, and a category frame that does not belong to the category (cannot be classified) is set.

[0032] For clustering, a GMM (Gaussian mixture models) method based on machine learning or the like can be used. Figure 6 An example of the result of clustering the normalized space vector of each pixel by setting 15 categories and 2 unclassifiable category frames according to the structure of the TFT substrate is shown. Here, the histogram of the number of pixels entering each category is represented by the difference from the histogram of the normal pattern. The category with a large difference from the histogram of the normal pattern can be identified as a defective part.

[0033] Figure 7 The following is a display example of the identification display step S5 for visualizing the result of the above-mentioned clustering step S4. Here, the pixels clustered in each category are visualized (image display) by adding contrast or color distinction. Figure 7(a) is the result of clustering the n-dimensional space vector in a standardized manner (standardized clustering). Figure 7 (b) is the result of clustering without normalizing the n-dimensional space vector (absolute value clustering).

[0034] When the general Figure 7 In the case of the standardized cluster visualization shown in (a), the outline of the characteristic part (for example, the defect part) can be clearly visualized. In contrast, when the spectral image data of the same sample surface are clustered by absolute values, as shown in Figure 7 As shown in (b), the contour of the characteristic portion becomes unclear due to the influence of interference of light reflected inside the multilayer film.

[0035] Thus, according to the surface analysis method or surface analysis device involved in the embodiment of the present invention, by clustering and visualizing the acquired spectroscopic image data, it is possible to perform a highly accurate analysis when analyzing the characteristic parts of the surface. In particular, when identifying and displaying defective parts of a multi-layer substrate, since the contour of the defective part can be clearly visualized, it is possible to achieve high-precision repair (laser correction) of the defective part.

[0036] Figure 8 The following is a configuration example of a laser correction device 2 having the surface analysis device 1. The laser correction device 2 performs correction processing by irradiating a defective portion identified by visualization of the information processing unit 30 with a laser beam, and includes a laser irradiation unit 3 for irradiating a laser beam L on the same axis as the optical axis of the microscope 10.

[0037] The laser irradiation unit 3 includes, for example, a laser light source 53, a laser scanner 55, etc. The laser beam L emitted from the laser light source 53 is incident on the optical system of the microscope 10 via the reflection mirror 54 and the galvanometer mirrors 55A and 55B of the laser scanner 55, and is irradiated on the surface Wa of the unit area of ​​which an enlarged image is obtained using the microscope 10.

[0038] In the example shown in the figure, a switchable reflection mirror 18 for entering and retreating is provided on the optical axis of the microscope 10. By moving the switchable reflection mirror 18 into the optical axis of the microscope 10, reflected light from the surface Wa is incident on the spectroscopic camera 20 to activate the surface analysis device 1. By retreating the switchable reflection mirror 18 from the optical axis of the microscope 10, the laser correction device 2 for irradiating the laser beam L onto the surface Wa can be activated.

[0039] The laser correction device 2 equipped with such a surface analysis device 1 first operates the surface analysis device 1, whereby the information processing unit 30 transmits information such as the presence or absence of a defect and the position of the defect when a defect exists to the laser control unit 50. The laser control unit 50 determines whether to perform laser correction based on the aforementioned information transmitted from the information processing unit 30, and when performing laser correction, sets the laser irradiation range or processing method based on the position information of the defect, etc.

[0040] Furthermore, in the example shown in the figure, the magnified image of the microscope 10 is also formed in the monitoring camera 15, so that the laser correction can be performed while observing the image captured by the monitoring camera 15 on the display device 52. At this time, the two-dimensional image obtained by the monitoring camera 15 is sent to the laser control unit 50 or the information processing unit 30 after image processing by the image processing unit 51, so that the laser irradiation unit 3 can also be controlled by the two-dimensional image.

[0041] According to this laser correction device 2, the defective part of the multilayer film substrate W can be identified in detail with a clear outline by the surface analysis device 1, and the laser correction processing can be set based on the information of the identification. Thus, high-quality correction processing can be performed without being affected by the operator's skills, and the process from the identification of the defective part to the processing can be automated, thereby enabling efficient and high-quality correction processing.

[0042] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the specific structures are not limited to these embodiments, and even if there are design changes that do not deviate from the scope of the purpose of the present invention, they are also included in the present invention. In addition, as long as there are no special contradictions or problems in the purposes and structures of the above-mentioned embodiments, the technologies of each other can be used and combined.

[0043] Explanation of symbols

[0044] 1-surface analysis device, 2-laser correction device, 3-laser irradiation unit, 10-microscope, 10P-optical axis, 11-objective lens, 12-white light source, 13-reflector, 14, 16-half-reflector, 15-monitoring camera, 17-tube lens, 18-switching reflector, 20-spectrometric camera, 21-grating element, 22-two-dimensional camera, 22a-camera surface, 23-slit, 30-information processing unit, 31-n-dimensional space vectorization mechanism, 32-space vector standardization mechanism, 33-clustering mechanism, 34-identification display mechanism, 40-display unit, 50-laser control unit, 51-image processing unit, 52-display device, 53-laser light source, 54-reflector, 55-laser scanner, 55A, 55B-galvanometer mirrors, S-stage, W-sample (multilayer film substrate), Wa-surface, L-laser beam.

Claims

1. A surface analysis method, characterized in that: The process is as follows: A process of acquiring spectroscopic image data of a sample surface using a spectroscopic camera; A step of extracting n wavelengths dispersed within a specific wavelength range from the acquired spectral image data, and setting a spectrum of each wavelength in the spectral image data as an n-dimensional space vector for each pixel; A step of normalizing the spatial vector of each pixel; A step of clustering the normalized space vectors into a specific number of categories; and A step of identifying, for each of the categories, pixels clustered in the category.

2. The surface analysis method according to claim 1, characterized in that The sample surface is the surface of the TFT substrate, Display defective portions are identified by the pixels clustered in the classification.

3. A surface analysis device, characterized in that: have: Spectroscopic camera, to obtain spectroscopic image data of the sample surface; An information processing unit, which analyzes and processes the spectroscopic image data; and A display unit displays the processing result of the information processing unit, The information processing unit has the following mechanisms: a mechanism for extracting n wavelengths dispersed in a specific wavelength range from the spectral image data and setting a spectrum of each wavelength in the spectral image data as an n-dimensional space vector for each pixel; A mechanism for normalizing the spatial vector for each pixel; means for clustering the normalized space vectors into a specified number of categories; and The display unit includes means for identifying and displaying pixels clustered in the category for each category.

4. A laser correction device, which uses the surface analysis device according to claim 3 to irradiate a recognized defective portion with a laser beam to perform correction processing.

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