Substrate analysis system, substrate analysis method, and storage medium

Abstract

To make it possible to efficiently analyze a processing state on substrate processing.SOLUTION: An analyzer 10 comprises: an imaging unit 11 for imaging a substrate surface; a defect range estimation section 131 that on the basis of an imaging result of the imaging unit 11, estimates a defect range that is a range in which a defect is caused on the substrate surface; a gray value acquisition section 132 that acquires a gray value in the defect range when the substrate surface is irradiated with light; and a defect type determination section 133 that on the basis of the gray value, determines a defect type in the defect range.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0001] The present disclosure relates to a substrate analysis system, a substrate analysis method, and a storage medium.

Background Art

[0002] Patent Document 1 describes a substrate processing method including a step of generating a captured image of a substrate after processing related to each layer constituting a laminated film on the substrate, and a step of showing information indicating a feature amount estimated based on the captured image for each of a plurality of layers including the outermost layer of the laminated film.

Prior Art Documents

Patent Documents

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Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present disclosure provides a substrate analysis system, a substrate analysis method, and a storage medium that can efficiently analyze the processing state of substrate processing.

Means for Solving the Problems

[0005] A substrate analysis system according to one aspect of the present disclosure includes an imaging unit that images a substrate surface, a defect range estimation unit that estimates a defect range, which is a range where a defect has occurred on the substrate surface, based on an imaging result of the imaging unit, a gray value acquisition unit that acquires a gray value in the defect range when light is irradiated on the substrate surface, and a defect type determination unit that determines a defect type in the defect range based on the gray value.

Effects of the Invention

[0006] According to the present disclosure, it is possible to provide a substrate analysis system, a substrate analysis method, and a storage medium that can efficiently analyze the processing state of substrate processing. [Brief explanation of the drawing]

[0007] [Figure 1] This is a schematic diagram illustrating the general configuration of a substrate processing system. [Figure 2] This diagram illustrates defect scope estimation and defect type determination. [Figure 3] This is a longitudinal cross-sectional view schematically showing the configuration of the imaging unit. [Figure 4] This is a schematic cross-sectional view showing the configuration of the imaging unit. [Figure 5] This is a schematic longitudinal cross-sectional view showing the configuration of the Gray value detection unit. [Figure 6] This is a schematic cross-sectional view showing the configuration of the Gray value detection unit. [Figure 7] This figure shows the relationship between Gray values ​​and wavelengths for each type of defect. [Figure 8] This figure shows an example of the trend in the change of Gray values ​​between different wavelengths. [Figure 9] This is a schematic diagram illustrating the hardware configuration of the diffraction unit. [Figure 10] This flowchart shows the processing steps for the substrate analysis method. [Modes for carrying out the invention]

[0008] The embodiments will be described in detail below with reference to the drawings. In the description, the same elements or elements having the same function will be denoted by the same reference numeral, and redundant descriptions will be omitted.

[0009] The substrate processing system 1 is a system that performs the formation of a photosensitive film on a substrate, exposure of the photosensitive film, and development of the photosensitive film. Furthermore, the substrate processing system 1 performs etching to remove oxide films and thin films along the pattern of the formed photosensitive film, and cleaning after etching. Examples of substrates to be processed include semiconductor wafers, glass substrates, mask substrates, and FPDs (Flat Panel Displays). The substrates also include semiconductor wafers and the like on which coatings have been formed in the preceding processing.

[0010] Figure 1 is a schematic diagram illustrating the general configuration of a substrate processing system 1. In the example shown in Figure 1, the substrate processing system 1 includes an analysis device 10 (substrate analysis system), a coating device 20, a developing device 30, an exposure device 40, an etching device 50, and a cleaning device 60. Any two or more of these devices may be implemented in a single device. For example, the coating device 20 and the developing device 30 may be combined into a single coating and developing device.

[0011] The coating apparatus 20 forms a photosensitive resist film on the surface of the substrate. The exposure apparatus 40 performs an exposure process by irradiating the portion of the resist film to be exposed with energy rays using methods such as immersion exposure. The developing apparatus 30 performs a developing process on the resist film after the exposure process. The etching apparatus 50 performs an etching process to remove the oxide film and thin film along the pattern of the formed resist film.

[0012] The cleaning apparatus 60 performs a cleaning process on the substrate after the etching process. The cleaning apparatus 60 may include, for example, a device that supplies SC1 to a rotating substrate for cleaning, and then supplies IPA to the substrate for drying. Alternatively, the cleaning apparatus 60 may include, for example, a device that performs a batch process by supplying phosphoric acid to a group of multiple substrates, and then supplies IPA to the group of substrates for drying each substrate.

[0013] The analysis device 10 is a device that analyzes the processing state of the substrate due to substrate processing in each device included in the substrate processing system 1. The analysis device 10 estimates the defect area on the substrate and determines the type of defect in that area from the Gray value in that area. The analysis device 10 may also estimate the cause of defect occurrence in the previous process based on the determined type of defect.

[0014] Figure 2 illustrates defect range estimation and defect type determination. Figure 2(a) illustrates an example of defect range estimation, and Figure 2(b) illustrates an example of defect type determination for the defect range shown in Figure 2(a). In the example shown in Figure 2(a), regions d1 to d3, which are estimated to be defect ranges on the substrate W, are identified from the imaging results obtained by imaging the substrate W. Then, as shown in Figure 2(b), by obtaining gray values ​​for each region d1 to d3, it is identified that the defect in region d1 is related to the developer, the defect in region d2 is related to yttrium, and the defect in region d3 is related to the resist. By determining the defect type in this way, it becomes possible to estimate the cause of the defect (process, equipment / module in which the defect occurred) (details will be described later).

[0015] Returning to FIG. 1, the analysis apparatus 10 includes an imaging unit 11, a gray value detection unit 12, and an analysis unit 13. The imaging unit 11 and the analysis unit 13 are communicable with each other. Also, the gray value detection unit 12 and the analysis unit 13 are communicable with each other. Note that the configuration of the analysis apparatus 10 shown in FIG. 1 is an example, and the arrangement of each component included in the analysis apparatus 10 is not limited to the example shown in FIG. 1. That is, each functional unit constituting the imaging unit 11 or the analysis unit 13 may be individually mounted on other devices, and necessary information may be exchanged between the respective functional units. The respective functional units constituting the analysis unit 13 referred to here are a defect range estimation unit 131, a gray value acquisition unit 132, a defect type determination unit 133, and a cause estimation unit 134. For example, the imaging unit 11 or the defect range estimation unit 131 may be mounted on other devices such as the coating apparatus 20. Also, the defect type determination unit 133 and the cause estimation unit 134 of the analysis unit 13 may be mounted on other servers (not shown). Further, the analysis apparatus 10 itself may be mounted on other devices such as the coating apparatus 20, for example.

[0016] The imaging unit 11 images the surface of the substrate W according to the control of the analysis unit 13 and transmits the imaging result to the analysis unit 13. Based on the imaging result, the analysis unit 13 estimates the defect range of the substrate W (details will be described later).

[0017] FIG. 3 is a longitudinal sectional view schematically showing the configuration of the imaging unit 11. FIG. 4 is a cross-sectional view schematically showing the configuration of the imaging unit 11. As shown in FIGS. 3 and 4, the imaging unit 11 has a casing 110. Inside the casing 110, a mounting table 115 on which the substrate W is mounted is provided. This mounting table 115 can be rotated and stopped by a rotation drive unit 116 such as a motor. On the bottom surface of the casing 110, a guide rail 113 extending from one end side (the negative X direction side in FIG. 4) to the other end side (the positive X direction side in FIG. 4) of the casing 11 is provided. The mounting table 115 and the rotation drive unit 116 are provided on the guide rail and can be moved along the guide rail 113 by a drive device 117.

[0018] On the side surface of the other end side (the positive X direction side in FIG. 4) inside the casing 110, a camera 111 is provided. As the camera 111, for example, a line sensor camera is used. Near the center of the upper part of the casing 110, a half mirror 114 is provided. The half mirror 114 is provided at a position facing the camera 111 in a state where the mirror surface is inclined 45 degrees upward toward the camera 111 from the state where it faces vertically downward. Above the half mirror 114, a light source 112 is provided. The half mirror 114 and the light source 112 are fixed to the upper surface inside the casing 110. The illumination from the light source 112 passes through the half mirror 114 and is directed downward. Then, the light reflected by an object (here, the substrate W) below the light source 112 is further reflected by the half mirror 114 and taken into the camera 111. In this way, the camera 111 can image the surface of the substrate W. That is, in the imaging unit 11, the substrate W is moved in one direction (the X direction in FIG. 4) along the guide rail 113, and the surface of the substrate W is scanned and imaged by the camera 111.

[0019] The gray value detection unit 12 is configured to detect the gray value when irradiating light to the defect range (details will be described later) estimated by the analysis unit 13 based on the imaging result by the imaging unit 11. The gray value detection unit 12 includes a SWIR (Short Wavelength Infra-Red) sensor 121 that detects short-wave infrared light. The short-wave infrared light is, for example, light in the wavelength band of 700 to 2500 nm, and may be light in the wavelength range of 900 to 1700 nm. The SWIR sensor 121 can distinguish and detect the gray values of various chemical solutions (organic and inorganic chemical solutions) and metals, etc. The gray value detection unit 12 detects the gray value according to the control of the analysis unit 13 and transmits the detection result to the analysis unit 13. Based on the detection result, the analysis unit 13 discriminates the defect type in the defect range of the substrate W (details will be described later).

[0020] Figure 5 is a schematic longitudinal cross-sectional view showing the configuration of the Gray value detection unit 12. Figure 6 is a schematic transverse cross-sectional view showing the configuration of the Gray value detection unit 12. As shown in Figures 5 and 6, the Gray value detection unit 12 has a casing 120. A mounting table 125 on which the substrate W is mounted is provided inside the casing 120. This mounting table 125 can be freely rotated and stopped by a rotary drive unit 126 such as a motor. A guide rail 123 is provided on the bottom surface of the casing 120, extending from one end of the casing 120 (the negative X direction side in Figure 6) to the other end (the positive X direction side in Figure 6). The mounting table 125 and the rotary drive unit 126 are mounted on the guide rail 123 and can move along the guide rail 123 by a drive device 127.

[0021] A SWIR sensor 121 is provided on the other end side (the positive X-direction side in Figure 6) inside the casing 120. Note that the SWIR sensor 121 may be a SWIR camera. A half mirror 124 is provided near the upper center of the casing 120. The half mirror 124 is positioned opposite the SWIR sensor 121, with its mirror surface facing vertically downwards and tilted 45 degrees upwards toward the SWIR sensor 121. A light source 122 is provided above the half mirror 124. The light source 122 is a light source that emits light including at least the wavelength range of short-wave infrared light, for example, a white light source. The light source 122 emits light in multiple wavelength bands, for example, light in the wavelength bands of at least 1000 nm, 1200 nm, and 1400 nm. The half mirror 124 and the light source 122 are fixed to the upper surface inside the casing 120. Light from the light source 122 passes through the half mirror 124 and shines downwards. The light from the light source 122 illuminates, for example, the entire surface of the substrate W. The light reflected from the surface of the substrate W is then reflected again by the half mirror 124 and detected by the SWIR sensor 121. In this way, the SWIR sensor 121 can detect light from the entire surface of the substrate W and detect the gray values ​​(gray value distribution) of multiple wavelengths in each region of the surface of the substrate W. Note that the SWIR sensor 121 may also detect only the gray values ​​of the defect range of the substrate W. Specifically, by adjusting the position and orientation of the substrate W by the drive unit 127 and the rotation drive unit 126, the SWIR sensor 121 may detect only the gray values ​​of the defect range of the substrate W.

[0022] Although the above description assumes that the imaging unit 11 and the gray value detection unit 12 have different configurations, if the defect area can be identified and the gray value acquired by imaging with a single imaging unit, then only one imaging unit may be provided.

[0023] Returning to Figure 1, the analysis unit 13 includes a defect range estimation unit 131, a Gray value acquisition unit 132, a defect type determination unit 133, a factor estimation unit 134, and a storage unit 135.

[0024] The defect range estimation unit 131 estimates the defect range, which is the area on the surface of the substrate W where defects occur, based on the imaging results (imported image) from the imaging unit 11. The defect range estimation unit 131 estimates the defect range on the surface of the substrate W from the pixel values ​​of each region on the surface of the substrate W shown in the acquired image.

[0025] The gray value acquisition unit 132 acquires the gray value in the defect area when light is irradiated onto the surface of the substrate W. Specifically, the gray value acquisition unit 132 may acquire the gray value of each region on the surface of the substrate W from the gray value detection unit 12 and identify (acquire) the gray value of the defect area from among them. The gray value acquisition unit 132 may also acquire gray values ​​corresponding to each of multiple wavelength bands in the defect area. Here, multiple wavelength bands refer to, for example, three or more wavelength bands, such as the wavelength bands of 1000 nm, 1200 nm, and 1400 nm.

[0026] The defect type determination unit 133 determines the type of defect in the defect range based on the Gray values. If there are multiple regions within a single defect range where the distribution of Gray values ​​differs from each other, the defect type determination unit 133 may determine the type of defect for each of the multiple regions. In other words, the defect type determination unit 133 may not only determine one type of defect for a single defect range, but may also determine the type of defect for each of the multiple regions within a single defect range where the distribution of Gray values ​​differs from each other.

[0027] The defect type determination unit 133 may determine the defect type based on the Gray value corresponding to each of the multiple wavelengths. The defect type determination unit 133 may also determine the defect type based on the trend of change among the Gray values ​​corresponding to each of the multiple wavelengths. The determination of the defect type will be explained in detail with reference to Figures 7 and 8.

[0028] Figure 7 shows the relationship between Gray values ​​and wavelengths for each type of defect. In Figure 7, the horizontal axis represents wavelength, and the vertical axis represents Gray values. In Figure 7, the Gray values ​​detected for each wavelength of light irradiated are shown for each chemical (resist, developer, etc.) that can cause defects when present on the substrate surface. As shown in Figure 7, the distribution of Gray values ​​at each wavelength differs for each type of defect (each chemical that can cause defects when present on the substrate surface), so the type of defect can be determined from the Gray value.

[0029] As shown in Figure 7, it may not be possible to uniquely identify the type of defect based solely on the Gray value at a single wavelength. Therefore, it is preferable to determine the type of defect based on the Gray values ​​corresponding to multiple wavelengths.

[0030] Furthermore, even when considering Gray values ​​corresponding to multiple wavelengths, the Gray value distribution may be similar, making it impossible to uniquely identify the type of defect. In this case, however, the type of defect can be determined by considering the trend of change between the Gray values ​​corresponding to multiple wavelengths. Figure 8 shows an example of the trend of change in Gray values ​​between different wavelengths. In defect classification A shown in Figure 8(a), the Gray value decreases from the shortest wavelength towards the adjacent wavelength band, and then increases towards the longest wavelength. On the other hand, in defect classification B shown in Figure 8(b), the Gray value increases from the shortest wavelength towards the adjacent wavelength band, and then increases further towards the longest wavelength. Thus, since the trends of change between the Gray values ​​corresponding to multiple wavelengths differ between defect classification A and defect classification B, the type of defect can be uniquely identified by considering these trends. The trend of change refers to, for example, the upward and downward fluctuation of the Gray value, or the degree of the slope of the upward and downward fluctuation. As mentioned above, when there are three or more wavelengths, there are two or more adjacent wavelength intervals, making it possible to more effectively identify the trend of change.

[0031] The defect type discrimination unit 133 may use data from an external analyzer showing the relationship between Gray values ​​and wavelengths for each defect type, as shown in Figure 7, as training data to accumulate Gray value data for each wavelength. The defect type discrimination unit 133 may then generate a model (regression model, machine learning model, etc.) to discriminate the defect type from the Gray value data for multiple wavelengths. In this case, for example, the Gray value data for each wavelength may be obtained using an equation such as Gray value × wavelength 1000nm + Gray value × wavelength 1200nm + Gray value × wavelength 1400nm. The defect type discrimination unit 133 stores the Gray value data for each wavelength and the model generated from each data in the storage unit 135. Based on the model stored in the storage unit 135, the defect type discrimination unit 133 discriminates the defect type from the Gray value data for multiple wavelengths.

[0032] Returning to Figure 1, the factor estimation unit 134 estimates the defect cause in the preceding process based on the defect type determined by the defect type determination unit 133. Here, the preceding process refers to a process performed before the point in time when the processing state of the substrate is analyzed by, for example, the analysis device 10, and is a process performed by, for example, the coating device 20, developing device 30, exposure device 40, etching device 50, or cleaning device 60. Estimating the defect cause means estimating the process (specific processing) in which the defect occurred, or estimating the device / module that performs the said process, etc.

[0033] The factor estimation unit 134 may estimate the cause of the defect based on the type of defect determined by the defect type determination unit 133, the defect characteristics such as the shape and location of the defect, and information about the equipment of the preceding process (including customer host information, etc.). For example, suppose the defect type determination unit 133 determines that the defect type is a defect caused by IPA. In this case, suppose the shape of the defect is such that the defect on the substrate is a shape that draws a line in the circumferential direction or a shape that forms an arc, and is located in a local area in the radial direction of the substrate. In this case, the factor estimation unit 134 may estimate that the defect is caused by residual liquid during the rotation process, and that the cleaning process involving rotation is the process in which the defect occurred (specific process). Alternatively, suppose the shape of the defect is such that it has a directionality that extends from one end to the opposite side of the substrate. In this case, the factor estimation unit 134 may estimate that the drying process in which IPA is supplied after the phosphoric acid batch processing is the process in which the defect occurred (specific process).

[0034] Figure 9 is a block diagram illustrating the hardware configuration of the analysis unit 13. The analysis unit 13 is composed of one or more control computers. As shown in Figure 9, the analysis unit 13 has a circuit 190. The circuit 190 includes at least one processor 191, memory 192, storage 193, input / output ports 194, input device 195, and display device 196.

[0035] The storage device 193 has a storage medium that can be read by a computer, such as a hard disk. The storage device 193 stores a program that causes the analysis unit 13 to execute the information processing method of the analysis device 10. For example, the storage device 193 stores a program that causes the analysis unit 13 to configure each of the above-mentioned functional blocks.

[0036] Memory 192 temporarily stores the program loaded from the storage medium of storage 193 and the calculation results by processor 191. Processor 191 works in cooperation with memory 192 to execute the above program, thereby configuring each of the functional modules described above. Input / output port 194 performs input and output of electrical signals between the imaging unit 11 and the gray value detection unit 12 in response to commands from processor 191.

[0037] The input device 195 and the display device 196 function as the user interface for the analysis unit 13. The input device 195 is, for example, a keyboard and acquires input information from the user. The display device 196 includes, for example, an LCD monitor and is used to display information to the user. The display device 196 is used, for example, to display the factor information mentioned above. The input device 195 and the display device 196 may be integrated as a so-called touch panel.

[0038] Next, with reference to Figure 10, the processing procedure for the substrate analysis method performed by the analysis device 10 will be described. Figure 10 is a flowchart showing the processing procedure for the substrate analysis method.

[0039] As shown in Figure 10, the substrate surface is first imaged (Step S1, imaging step). Subsequently, based on the imaging results from the imaging step, the defect range, which is the area on the substrate surface where defects occur, is estimated (Step S2, defect range estimation step).

[0040] Next, the gray value of the defect area is obtained when light is shone on the substrate surface (Step S3, Gray value acquisition step). Then, the type of defect in the defect area is determined based on the gray value (Step S4, Defect type determination step).

[0041] Finally, based on the defect type identified in the defect type identification step, the cause of the defect in the previous step is estimated (Step S5, Cause Estimation Step).

[0042] Next, the effects and advantages of the analytical apparatus 10 according to this embodiment will be described.

[0043] The analysis device 10 includes an imaging unit 11 that images the substrate surface, and a defect range estimation unit 131 that estimates the defect range, which is the area on the substrate surface where defects occur, based on the imaging results of the imaging unit 11. Furthermore, the analysis device 10 includes a gray value acquisition unit 132 that acquires the gray value in the defect range when light is irradiated onto the substrate surface, and a defect type determination unit 133 that determines the type of defect in the defect range based on the gray value.

[0044] In the analysis apparatus 10 according to this embodiment, first, the defect area on the substrate surface is estimated based on the imaging results of the substrate surface, and then the type of defect in the defect area is determined based on the gray value in the defect area when light is irradiated onto the substrate surface. Since there is a correlation between the gray value and the type of defect, the type of defect can be determined with high accuracy based on the gray value, and the processing status of the substrate can be appropriately analyzed. Furthermore, by estimating the general defect area from the imaging results and then determining the type of defect from the gray value in that imaging area, the range in which the defect type determination is performed based on the gray value can be limited, and the type of defect can be determined efficiently. In addition, compared to conventional inspection machines that require time for each substrate, such as those that perform sampling inspections of substrates during the substrate processing process, the type of defect can be determined more efficiently.As described above, the analysis apparatus 10 according to this embodiment can efficiently analyze the processing status of the substrate.

[0045] The Gray value acquisition unit 132 may acquire Gray values ​​corresponding to each of multiple wavelengths within the defect range, and the defect type determination unit 133 may determine the defect type based on the Gray values ​​corresponding to each of the multiple wavelengths. Since the Gray values ​​differ for each wavelength, determining the defect type from the Gray values ​​corresponding to each of the multiple wavelengths allows for more accurate determination of the defect type.

[0046] The defect type determination unit 133 may determine the defect type for each of the multiple regions if there are multiple regions within a single defect range where the distribution of Gray values ​​differs from one another. This allows for the appropriate determination of the defect type for each region, even when there are multiple regions within a defect range estimated from the imaging results where the distribution of Gray values ​​differs from one another (i.e., the defect types differ from one another).

[0047] The defect type determination unit 133 may determine the defect type based on the trend of change between Gray values ​​corresponding to each of several wavelengths. In this way, instead of simply determining the defect type based on the absolute value of the Gray value, considering the trend of change between Gray values ​​allows for more accurate determination of the defect type. Furthermore, even if the absolute value fluctuates due to the influence of noise, for example, the defect type can be accurately determined based on the trend of change between Gray values.

[0048] The analysis device 10 may further include a factor estimation unit 134 that estimates the cause of defect occurrence in the preceding process based on the defect type determined by the defect type determination unit 133. With such a configuration, the cause of defect occurrence (the process, equipment, module, etc. in which the defect occurred) can be identified, and the occurrence of defects can be suppressed by improving the cause of the defect. [Explanation of Symbols]

[0049] 10...Analysis device (substrate analysis system), 11...Imaging unit (imaging section), 131...Defect range estimation unit, 132...Gray value acquisition unit, 133...Defect type discrimination unit, 134...Cause estimation unit.

Claims

1. An imaging unit that images the surface of the substrate, A defect range estimation unit estimates the defect range, which is the area on the substrate surface where defects occur, based on the imaging results of the imaging unit. A gray value acquisition unit that acquires the gray value in the defect area when light is irradiated onto the substrate surface, The system includes a defect type determination unit that determines the type of defect in the defect range based on the aforementioned gray value, The Gray value acquisition unit acquires the Gray value corresponding to each of the multiple wavelengths in the defect range, The defect type determination unit determines the type of defect based on the Gray value corresponding to each of the multiple wavelengths and the relationship between the wavelength and Gray value for each type of defect, in a substrate analysis system.

2. The substrate analysis system according to claim 1, wherein the defect type determination unit determines the defect type for each of the multiple regions if there are multiple regions in a single defect range in which the distribution of the Gray values ​​differs from each other.

3. The defect type determination unit determines the defect type based on the trend of change among the gray values ​​corresponding to each of the plurality of wavelengths. The substrate analysis system according to claim 1 or 2, wherein the aforementioned trend of change is the upward or downward fluctuation or the degree thereof among multiple wavelength conditions of the gray value.

4. A substrate analysis system according to any one of claims 1 to 3, further comprising a factor estimation unit that estimates the cause of defect occurrence in a preceding process based on the defect type determined by the defect type determination unit.

5. The substrate analysis system according to any one of claims 1 to 4, wherein the imaging unit performs imaging with respect to light in the wavelength band of 700 to 2500 nm.

6. The substrate analysis system according to any one of claims 1 to 5, wherein the gray value acquisition unit acquires gray values ​​in multiple wavelength bands using a short-wave infrared light sensor.

7. The imaging process involves imaging the surface of the substrate, A defect range estimation step is performed to estimate the defect range, which is the area on the substrate surface where defects occur, based on the imaging results in the imaging step. A gray value acquisition step is to acquire the gray value in the defect area when light is irradiated onto the substrate surface, The system includes a defect type determination step for determining the type of defect in the defect range based on the aforementioned gray value, In the Gray value acquisition step, the Gray value corresponding to each of the multiple wavelengths in the defect range is acquired. A substrate analysis method in which, in the defect type determination step, the type of defect is determined based on the Gray value corresponding to each of the multiple wavelengths and the relationship between the wavelength and Gray value for each type of defect.

8. The substrate analysis method according to claim 7, wherein, in the defect type determination step, if there are multiple regions in a single defect range where the distribution of the Gray values ​​differs from each other, the defect type is determined for each of the multiple regions.

9. The substrate analysis method according to claim 7 or 8, wherein the defect type determination step determines the defect type based on the trend of change among the Gray values ​​corresponding to each of the plurality of wavelengths.

10. A substrate analysis method according to any one of claims 7 to 9, further comprising a factor estimation step for estimating the cause of the defect in the preceding step based on the type of defect determined in the defect type determination step.

11. The substrate analysis method according to any one of claims 7 to 10, wherein the imaging in the imaging step is performed with light in the wavelength band of 700 to 2500 nm.

12. The substrate analysis method according to any one of claims 7 to 11, wherein in the gray value acquisition step, a short-wave infrared light sensor is used to acquire gray values ​​in multiple wavelength bands.

13. A computer-readable storage medium storing a program for causing an apparatus to perform the substrate analysis method according to any one of claims 7 to 12.

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