Image analysis method and image analysis system

Abstract

The present invention provides an image analysis method comprising the following steps: obtaining a multi-layer structure image provided by an electron microscope, and displaying the multi-layer structure image through a display device, wherein the multi-layer structure image is a grayscale image; setting a measurement line segment on the multi-layer structure image, wherein the measurement line segment extends in a first direction; detecting the grayscale distribution of the multi-layer structure image corresponding to the measurement line segment along the measurement line segment; and analyzing the grayscale distribution to determine the thickness of multiple dark layers and multiple light layers in the multi-layer structure image according to a threshold range. The image analysis method and image analysis system of the present invention can automatically measure the thickness of each layer of the multi-layer structure image.

Description

Technical Field

[0001] The present invention relates to an analysis method, and in particular to an image analysis method and an image analysis system. Background Art

[0002] In the semiconductor manufacturing process, the size of the component will affect the change of electrical properties. Therefore, the size requirements often need to be very precise. When measuring semiconductor components, electron microscopes with magnification and inspection functions such as transmission electron microscopes and scanning electron microscopes are usually used. However, when using an electron microscope to measure the size of the component image, it is often necessary to manually set the edge points of each area one by one to obtain the size of each area, which takes a long time. In view of this, several embodiments of the solution will be proposed below. Summary of the invention

[0003] The present invention is directed to an image analysis method and an image analysis system, which can automatically measure the thickness of each layer of a multi-layer structure image according to a set measurement line segment.

[0004] According to an embodiment of the present invention, the image analysis method of the present invention includes the following steps: obtaining a multi-layer structure image provided by an electron microscope, and displaying the multi-layer structure image through a display device, wherein the multi-layer structure image is a grayscale image; setting a measurement line segment on the multi-layer structure image, wherein the measurement line segment extends in a first direction; detecting the grayscale distribution of the multi-layer structure image corresponding to the measurement line segment along the measurement line segment; and analyzing the grayscale distribution to determine the thickness of multiple dark layers and the thickness of multiple light layers in the multi-layer structure image according to a threshold range.

[0005] According to an embodiment of the present invention, the image analysis system of the present invention includes an electron microscope, a display device and an image analysis device. The electron microscope is used to provide a multi-layer structure image. The display device is used to display the multi-layer structure image. The image analysis device is coupled to the electron microscope and the display device to obtain the multi-layer structure image provided by the electron microscope and output the multi-layer structure image to the display device. The image analysis device includes a storage device and a processor. The storage device includes an image analysis module. The processor is coupled to the storage device. The processor inputs the multi-layer structure image to the image analysis module. The processor sets a measurement line segment on the multi-layer structure image, wherein the measurement line segment extends in a first direction. The processor detects the grayscale distribution of the multi-layer structure image corresponding to the measurement line segment along the measurement line segment through the image analysis module. The processor analyzes the grayscale distribution through the image analysis module to determine the thickness of multiple dark layers and the thickness of multiple light layers in the multi-layer structure image according to a threshold range.

[0006] Based on the above, the image analysis method and the image analysis system of the present invention can automatically measure the thickness of each layer of a multi-layer structure image according to a set measurement line segment, thereby saving a lot of time required for manual operation.

[0007] In order to make the above features and advantages of the present invention more clearly understood, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a schematic diagram of an image analysis system according to an embodiment of the present invention;

[0009] Figure 2 is a flow chart of an image analysis method according to an embodiment of the present invention;

[0010] Figure 3 is a schematic diagram of a multi-layer structure image according to an embodiment of the present invention;

[0011] Figure 4 is a schematic diagram of grayscale distribution according to an embodiment of the present invention;

[0012] Figure 5 is a schematic diagram of a display interface according to an embodiment of the present invention;

[0013] Figure 6 is a flow chart of an image analysis method according to an embodiment of the present invention;

[0014] Figure 7 FIG. 4 is a schematic diagram of grayscale distribution of a multi-layer structure image according to an embodiment of the present invention.

[0015] Description of Reference Numerals

[0016] 100: image analysis system;

[0017] 101: image analysis device;

[0018] 110: processor;

[0019] 120: storage device;

[0020] 121: image analysis module;

[0021] 140: electron microscope;

[0022] 150: display device;

[0023] S210, S220, S230, S240, S250, S610, S620, S630, S640, S650: steps;

[0024] 300: multi-layer structure image;

[0025] 310-1 to 310-4, 710-1 to 710-4: dark layer images;

[0026] 320-1 to 320-5, 720-1 to 720-3: light layer images;

[0027] 330: measuring line segment;

[0028] 340-1 to 340-7: white thin layer images;

[0029] 400: grayscale distribution;

[0030] 461, 462, 761, 762: threshold range;

[0031] 500: display interface;

[0032] 510: multi-layer structure image;

[0033] 520: Tool bar;

[0034] 530: measurement result;

[0035] 700: grayscale distribution of multi-layer structure image;

[0036] 710a, 720a: starting point;

[0037] 710b, 720b: end point;

[0038] P1: first direction;

[0039] P2: Second direction. DETAILED DESCRIPTION

[0040] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0041] Furthermore, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the invention belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and the present invention, and will not be interpreted as an idealized or overly formal meaning unless explicitly defined as such herein.

[0042] Figure 1 is a schematic diagram of an image analysis system according to an embodiment of the present invention. Figure 1The image analysis system 100 may include an image analysis device 101, an electron microscope 140, and a display device 150. The electron microscope 140 may be used to provide a multi-layer structure image by photographing a semiconductor process object (semiconductor product). The multi-layer structure image is an electron microscope image and is a grayscale image. The multi-layer structure image may include multiple semiconductor structure layers of different materials, and the grayscale distribution of the image of the multi-layer semiconductor structure layer may be determined according to different semiconductor materials.

[0043] In the present embodiment, the display device 150 can be used to display the multi-layer structure image. The image analysis device 101 can be coupled to the electron microscope 140 and the display device 150 to obtain the multi-layer structure image provided by the electron microscope 140 and output the multi-layer structure image to the display device 150. The image analysis device 101 may include a processor 110 and a storage device 120. The storage device 120 may include an image analysis module 121. The processor 110 may be coupled to the storage device 120. In the present embodiment, the image analysis device 101 may be an independent computer device or a cloud server, and the present invention is not limited thereto.

[0044] In the present embodiment, the processor 110 may input the multi-layer structure image to the image analysis module 121, and the processor 110 may set a measurement line segment on the multi-layer structure image, wherein the measurement line segment extends in the stacking direction of the multi-layer structure. In the present embodiment, the processor 110 may set the measurement line segment in a manner that may include manual setting or automatic setting. In this regard, manual setting may, for example, refer to setting instructions or parameters provided by an input device of the image analysis system 100 (for example, input operations performed by a user) to set the position of the measurement line segment, but the present invention is not limited thereto. Automatic setting may, for example, refer to automatic setting by the image analysis system 100 according to the boundary range of the image or according to preset conditions, but the present invention is not limited thereto.

[0045] Next, the processor 110 can detect the grayscale distribution of the multi-layer structure image on the corresponding measurement line segment along the measurement line segment via the image analysis module 121. Furthermore, the processor 110 can analyze the grayscale distribution via the image analysis module 121 to determine the thickness of multiple dark layers and multiple light layers in the multi-layer structure image according to the threshold. In this way, the image analysis system 100 can automatically measure the thickness of each layer of the multi-layer structure image according to the set measurement line segment, thereby saving a lot of time required for manual operation.

[0046] In this embodiment, the processor 110 may include, for example, a central processing unit (CPU), a microprocessor (MCU) or a field programmable gate array (FPGA), but the invention is not limited thereto.

[0047] In this embodiment, the storage device 120 may include, for example, a random access memory (RAM), a read-only memory (ROM), an optical disc, a magnetic disk, a hard drive, a solid-state drive, a flash drive, a security digital (SD) card, a memory stick, a compact flash (CF) card, or any type of storage device, but the present invention is not limited thereto. The storage device 120 may store the image analysis module 121 and the related image data, related analysis results and data, and display interface described in each embodiment, so that the processor 110 can read and execute them.

[0048] In this embodiment, the electron microscope 140 may include, for example, a scanning electron microscope (SEM) or a transmission electron microscope (TEM), but the present invention is not limited thereto. In this embodiment, the display device 150 may include, for example, various electronic devices with display functions. In addition, in another embodiment, the display device 150 may be disposed in the image analysis device 101, so that the image analysis device 101 may be, for example, a computer device with a display function.

[0049] Figure 2 is a flow chart of an image analysis method according to an embodiment of the present invention. Figure 3 is a schematic diagram of a multi-layer structure image according to an embodiment of the present invention. Figure 4 is a schematic diagram of grayscale distribution according to an embodiment of the present invention. Figures 1 to 4In this embodiment, the image analysis system 100 may perform the following steps S210 to S250 of the image analysis method. In step S210, the image analysis device 101 may obtain the multi-layer structure image 300 provided by the electron microscope 140. In step S220, the image analysis device 101 may display the multi-layer structure image 300 through the display device 150. In step S230, the image analysis module 121 may set a measurement line segment 330 on the multi-layer structure image 300. In step S240, the image analysis module 121 detects the grayscale distribution 400 of the multi-layer structure image 300 corresponding to the measurement line segment 330 along the measurement line segment 330. In step S250, the image analysis module 121 analyzes the grayscale distribution 400 to determine the thickness of multiple dark layers and the thickness of multiple light layers in the multi-layer structure image 300 according to the threshold ranges 461 and 462.

[0050] Specifically, the multi-layer structure image 300 may be a grayscale image, and the first direction P1 is perpendicular to the second direction P2. In addition, the multi-layer structure image 300 may include a plurality of dark layer images 310-1 to 310-4 and a plurality of light layer images 320-1 to 320-5, wherein the plurality of dark layer images 310-1 to 310-4 and the plurality of light layer images 320-1 to 320-5 are arranged alternately along the first direction P1, and the plurality of dark layer images 310-1 to 310-4 and the plurality of light layer images 320-1 to 320-5 extend along the second direction P2, respectively. In this embodiment, the plurality of dark layer images 310-1 to 310-4 may be a first type of semiconductor material layer, and the plurality of light layer images 320-1 to 320-5 may be a second type of semiconductor material layer, wherein the first type of semiconductor material layer is different from the second type of semiconductor material layer. In this embodiment, the plurality of dark layer images 310-1 to 310-4 and the plurality of light layer images 320-1 to 320-5 may further include a plurality of white thin layer images 340-1 to 340-7, respectively, and the plurality of white thin layer images 340-1 to 340-7 may be a third type of semiconductor material layer different from the first type and the second type.

[0051] refer to Figures 1 to 4 In step S230, the processor 110 may execute the image analysis module 121 to set the measurement line segment 330 on the multi-layer structure image 300 (such as the manual setting or automatic setting mentioned above). In step S240, the processor 110 may detect a plurality of continuous pixels of the multi-layer structure image 300 corresponding to the measurement line segment 330 along the measurement line segment 330 to obtain a plurality of grayscale values ​​of the plurality of pixels. Furthermore, the processor 110 may establish a grayscale value based on the plurality of grayscale values. Figure 4In step S250, the processor 110 may analyze the grayscale distribution 400 and determine the thickness of multiple dark layers and multiple light layers in the multi-layer structure image 300 according to the preset threshold ranges 461 and 462. Figure 4 As shown, the values ​​of the horizontal axis of the grayscale distribution 400 may correspond to each pixel on the measurement line segment 330 along the first direction P1, and the values ​​of the vertical axis may represent the grayscale values ​​of each pixel on the measurement line segment 330 of the multi-layer structure image 300 corresponding to the measurement line segment 330 .

[0052] In the present embodiment, the threshold range 461 (also referred to as the first threshold range) may be set to, for example, 0 to 15, and the threshold range 462 (also referred to as the second threshold range) may be set to, for example, 45 to 90, but the present invention is not limited thereto. That is, when the grayscale value of a certain pixel is between 0 and 15, the certain pixel may be regarded as a dark pixel. Conversely, when the grayscale value of another certain pixel is between 45 and 90, the certain pixel may be regarded as a light pixel. For example, in step S250, the processor 110 may determine the pixel whose grayscale value is within the threshold range 461 as a dark pixel. Furthermore, the processor 110 may determine the pixel whose grayscale value is within the threshold range 462 as a light pixel. Then, the processor 110 may count the number of dark pixels corresponding to each dark layer thickness or each light layer thickness or the number of light pixels on the measurement line segment 330 to obtain each dark layer thickness or light layer thickness respectively. That is, each dark layer thickness corresponds to the number of dark pixels of each dark layer thickness, and each light layer thickness corresponds to the number of light pixels of each light layer thickness.

[0053] In this embodiment, when the processor 110 calculates the number of dark pixels in each dark layer and the number of light pixels in each light layer, the processor 110 can convert it into a corresponding thickness parameter in real time to output the measurement result. For example, one pixel can correspond to 1 nanometer. In one embodiment, assuming that the 6th to 44th pixels are light pixels, the processor 110 can obtain the thickness of the light layer image corresponding to this layer as 39 nanometers (44-6+1=39). However, the correspondence between pixels and length can be adjusted through manual setting or automatic detection, and the present invention is not limited to this.

[0054] In addition, in the present embodiment, the overall measurement result may be presented as shown in the following table (1) and displayed on the display device 150, but the present invention is not limited thereto. In the present embodiment, the layer number may represent the number of the plurality of dark layer images 310-1 to 310-4 or the plurality of light layer images 320-1 to 320-5 that the measurement line segment 330 sequentially intersects with the multi-layer structure image 300 along the first direction P1. In the present embodiment, the average grayscale value may represent the average grayscale value of the plurality of pixels corresponding to the dark layer images 310-1 to 310-4 or the light layer images 320-1 to 320-5 that the measurement line segment 330 sequentially intersects with the multi-layer structure image 300 along the first direction P1. In the present embodiment, the measured thickness may represent the dark layer thickness or the light layer thickness corresponding to the layer number.

[0055] Layer Number Average grayscale value Measuring thickness 1 8 38 2 80 39 3 5 38 4 83 35

[0056] Table (1)

[0057] It is worth noting that, in one embodiment, the multi-layer structure image 300 may include a plurality of dark layer images 310-1 to 310-4 and light layer images 320-1 to 320-5 with different grayscale value ranges, and the processor 110 may set threshold ranges 461, 462 or other threshold ranges accordingly to measure the thickness. In addition, in one embodiment, in the multi-layer structure image 300, a plurality of dark layer images 310-1 to 310-4 and light layer images 320-1 to 320-5 with different grayscale value ranges may be randomly arranged along the first direction P1, and are not limited to being staggered along the first direction P1.

[0058] Figure 5 is a schematic diagram of a display interface according to an embodiment of the present invention. Figure 3 as well as Figure 5 The display interface 500 may include a multi-layer structure image 510, a tool bar 520, and a measurement result 530. The description of the multi-layer structure image 510 may refer to Figure 3The multi-layer structure image 300 is not described here. In the present embodiment, the tool bar 520 may include buttons such as a magnifying glass, a moving display range, and a setting measurement line segment to provide the user with operations, but the present invention is not limited thereto. For example, the user may use the setting measurement line segment button on the tool bar 520 to set the desired measurement line segment 330. In addition, the measurement result 530 may include, for example, the number of the layer, and the dark layer thickness corresponding to the dark layer or the light layer thickness corresponding to the light layer. It is worth noting that in the measurement result 530, the dark layer thickness and the light layer thickness are each corresponding to the same layer number. In another embodiment, the measurement result 530 may be in the format of Table (1), and the layer number only corresponds to one dark layer thickness or one light layer thickness. In the present embodiment, the display interface 500 may be displayed on the display device 150 so that the user can view the measurement result 530 in real time.

[0059] Figure 6 is a flow chart of an image analysis method according to an embodiment of the present invention. Figures 1 to 4 as well as Figure 6 , Figure 2 Step S250 may be performed, for example, by Figure 6 In this embodiment, when the processor 110 executes the image analysis module 121 to analyze the grayscale distribution 400, it can be divided into the following four steps: step S610 (finding the starting point of the dark layer thickness or the light layer thickness), step S620 (finding the end point of the dark layer thickness or the light layer thickness), step S630 (checking the thickness range) and step S640 (checking whether the thickness overlaps).

[0060] It is worth noting that since the grayscale values ​​of the white thin layer images 340-1 to 340-7 are higher than the grayscale values ​​of the dark layer images 310-1 to 310-4 and the light layer images 320-1 to 320-5, when the processor 110 analyzes the grayscale distribution 400, the grayscale values ​​will decrease whether entering the dark layer images 310-1 to 310-4 or the light layer images 320-1 to 320-5 from the white thin layer images 340-1 to 340-7. In other words, the slope value of two consecutive grayscale values ​​will be a negative value. Then, the processor 110 only needs to determine whether the interval formed by the two consecutive grayscale values ​​includes the upper limit values ​​of the threshold ranges 461 and 462, and whether the second consecutive grayscale value belongs to the threshold range 461 of the dark layer images 310-1 to 310-4 or the threshold range 462 of the light layer images 320-1 to 320-5, so as to mark the starting point of the dark layer thickness or the light layer thickness. That is, when the processor 110 determines that the interval formed by two consecutive grayscale values ​​includes the upper limit value of the threshold range 461 (also called the first upper limit value), and determines that the second pixel of the two consecutive pixels is a dark pixel, the second pixel of the corresponding two consecutive pixels is marked as the dark layer starting point of the corresponding dark layer thickness. Similarly, when the processor 110 determines that the interval formed by two consecutive grayscale values ​​includes the upper limit value of the threshold range 462 (also called the second upper limit value), and determines that the second pixel of the two consecutive pixels is a light pixel, the second pixel of the corresponding two consecutive pixels is marked as the light layer starting point of the corresponding light layer thickness.

[0061] Furthermore, when the processor 110 analyzes the grayscale distribution 400, the grayscale value will increase whether entering the white thin layer images 340-1 to 340-7 from the dark layer images 310-1 to 310-4 or the light layer images 320-1 to 320-5. That is, the slope value of two consecutive grayscale values ​​will be a positive value. Then, the processor 110 only needs to determine whether the interval formed by the two consecutive grayscale values ​​includes the upper limit values ​​of the threshold ranges 461 and 462, and whether the second consecutive grayscale value does not belong to the threshold range 461 of the dark layer images 310-1 to 310-4 or the threshold range 462 of the light layer images 320-1 to 320-5, and then the end point of the dark layer thickness or the light layer thickness can be marked. That is, when the processor 110 determines that the interval formed by the two consecutive grayscale values ​​includes the first upper limit value of the threshold range 461, and determines that the first pixel of the two consecutive pixels is a dark pixel, the first pixel of the corresponding two consecutive pixels is marked as the dark layer end point of the corresponding dark layer thickness. Similarly, when the processor 110 determines that the interval formed by two consecutive grayscale values ​​includes the threshold range 462, also known as the second upper limit value, and determines that the first pixel of two consecutive pixels is a light-colored pixel, the first pixel of the corresponding two consecutive pixels is marked as the light-colored layer end point of the corresponding light-colored layer thickness.

[0062] In the present embodiment, the processor 110 may set a descending critical value and determine whether the slope value is less than the descending critical value to indicate the starting point of the dark layer thickness or the light layer thickness. In the present embodiment, the processor 110 may set an ascending critical value and determine whether the slope value is greater than the ascending critical value to indicate the end point of the dark layer thickness or the light layer thickness. For example, the descending critical value and the ascending critical value may both be set to zero, but the present invention is not limited thereto. In other words, the processor 110 may determine whether the slope value is a negative value (less than zero) to indicate the starting point of the dark layer thickness or the light layer thickness. Furthermore, the processor 110 may determine whether the slope value is a positive value (greater than zero) to indicate the end point of the dark layer thickness or the light layer thickness. In one embodiment, the descending critical value and the ascending critical value may be set to the same or different values ​​according to design requirements, and the present invention is not limited thereto.

[0063] For example, in step S610, when analyzing the grayscale distribution 400, the processor 110 may calculate the slope value of two consecutive grayscale values, and based on the change of the slope value and the determination of whether the second grayscale value of the two consecutive grayscale values ​​falls into the threshold range 461 corresponding to the dark layer images 310-1 to 310-4 or the threshold range 462 corresponding to the light layer images 320-1 to 320-5, mark the pixel corresponding to the second grayscale value of the two consecutive grayscale values ​​as the starting point of the dark layer. In other words, the processor 110 determines whether the interval formed by the two consecutive grayscale values ​​includes the upper limit values ​​of the threshold ranges 461 and 462, so as to mark the pixel corresponding to the second grayscale value of the two consecutive grayscale values ​​as the starting point of the dark layer or the light layer.

[0064] In step S620, when analyzing the grayscale distribution, the processor 110 may calculate the slope value of two consecutive grayscale values, and based on the change of the slope value and the determination of whether the second grayscale value of the two consecutive grayscale values ​​leaves the threshold range 461 corresponding to the dark layer images 310-1 to 310-4 or the threshold range 462 corresponding to the light layer images 320-1 to 320-5, mark the pixel corresponding to the first grayscale value of the two consecutive grayscale values ​​as the end point of the dark layer or the light layer. In other words, the processor 110 determines whether the interval formed by the two consecutive grayscale values ​​includes the upper limit values ​​of the threshold ranges 461 and 462, so as to mark the pixel corresponding to the first grayscale value of the two consecutive grayscale values ​​as the end point of the dark layer or the light layer.

[0065] Next, in step S630, the processor 110 calculates the number of pixels between the starting point and the end point corresponding to each dark layer thickness or light layer thickness. In this way, the processor 110 can calculate the corresponding dark layer thickness or light layer thickness according to the number of pixels between the starting point and the end point corresponding to each dark layer thickness or light layer thickness.

[0066] Furthermore, the processor 110 may check the pixels to discard incorrect starting points. For example, the processor 110 may check whether the average grayscale value of all pixels between the starting point and the end point of each dark layer thickness is within the threshold range 461 corresponding to the dark layer images 310-1 to 310-4 or check whether the average grayscale value of all pixels between the starting point and the end point corresponding to the light layer thickness is within the threshold range 462 corresponding to the light layer images 320-1 to 320-5. Only after the result of the check is correct, the processor 110 will calculate the number of pixels between the starting point and the end point corresponding to each dark layer thickness or light layer thickness. Conversely, after the result of the check is incorrect, the processor 110 may discard the current starting point and re-determine a starting point as a new starting point.

[0067] Finally, in step S640, the processor 110 may check whether the adjacent dark layer thicknesses or light layer thicknesses correspond to the same pixel to check whether the thicknesses overlap, and regard the overlapping thicknesses as the same dark layer thickness or light layer thickness. That is, after the processor 110 checks that the adjacent dark layer thicknesses or light layer thicknesses correspond to the same pixel, the adjacent light layer thicknesses or dark layer thicknesses may be merged into a single light layer thickness or dark layer thickness. In this way, the processor 110 may correct the overlap of the dark layer or light layer due to the interference of noise, and thereby obtain the correct dark layer thickness or light layer thickness. It is worth noting that in one embodiment, step S640 may be omitted, and the thickness calculated in step S630 may be directly used as the dark layer thickness or light layer thickness.

[0068] Figure 7 FIG. 1 is a schematic diagram of grayscale distribution of a multi-layer structure image according to an embodiment of the present invention. Figure 1 as well as Figure 7 In this embodiment, the grayscale distribution 700 of the multi-layer structure image may only include a plurality of dark layer images 710-1 to 710-4 and a plurality of light layer images 720-1 to 720-3, and may not include Figure 3The white thin layer images 340-1 to 340-7 are shown in FIG. 1 . Similar to the above method, when the processor 110 determines the starting point or end point of the thickness of the dark layer or the light layer, it can respectively determine whether the second continuous grayscale value belongs to the threshold range 761 of the dark layer images 710-1 to 710-4 or the threshold range 762 of the light layer images 720-1 to 720-3. In other words, the processor 110 can determine whether the interval formed by two continuous grayscale values ​​includes the upper limit value of the threshold range 761 to indicate the starting point or end point of the thickness of the dark layer. Furthermore, the processor 110 can determine whether the interval formed by two continuous grayscale values ​​includes the upper limit value of the threshold range 762 to indicate the starting point or end point of the thickness of the light layer. Next, the processor 110 can determine whether the starting point or the end point belongs to the dark layer thickness or the light layer thickness according to whether the second pixel of the two consecutive pixels enters or leaves the threshold range 761 of the dark layer images 710-1 to 710-4 or the threshold range 762 of the light layer images 720-1 to 720-3. In addition, the detailed thickness calculation method is the same as above and will not be repeated here.

[0069] In addition, the processor 110 may determine the starting point or end point of the thickness of the dark layer or the light layer according to the positive or negative slope value, but the present invention is not limited to this. Specifically, when entering the light layer from the dark layer, the grayscale value will increase, and when entering the dark layer from the light layer, the grayscale value will decrease. That is, the processor 110 may determine whether the second pixel of two consecutive pixels belongs to a dark pixel or a light pixel according to the slope value of two consecutive grayscale values. For example, the processor 110 may determine the starting point 710a of the thickness of the dark layer according to the negative slope value, and the processor 110 may determine the end point 710b of the thickness of the dark layer according to the positive slope value. In addition, the processor 110 may determine the starting point 720a of the thickness of the light layer according to the positive slope value, and the processor 110 may determine the end point 720b of the thickness of the dark layer according to the negative slope value.

[0070] In summary, the image analysis method and image analysis system of the present invention can automatically and quickly measure the thickness of each layer of a multi-layer structure image according to a set measurement line segment, thereby saving a lot of time required for manually measuring the thickness of each layer. In addition, the image analysis method and image analysis system of the present invention can effectively avoid the interference of image noise or the influence of material impurities and can accurately measure the thickness of each layer in a multi-layer structure image.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An image analysis method, characterized in that: include: Acquire a multi-layer structure image provided by an electron microscope, and display the multi-layer structure image through a display device, wherein the multi-layer structure image is a grayscale image, and the multi-layer structure image includes a plurality of dark layer images and a plurality of light layer images, wherein the plurality of dark layer images and the plurality of light layer images are alternately arranged along a first direction, and the plurality of dark layer images and the plurality of light layer images extend along a second direction respectively, wherein the first direction is perpendicular to the second direction; Setting a measuring line segment on the multi-layer structure image, wherein the measuring line segment extends toward the first direction; Detecting the grayscale distribution of the multi-layer structure image along the measuring line segment corresponding to the measuring line segment; and Analyzing the grayscale distribution to determine the thickness of multiple dark layers and multiple light layers in the multi-layer structure image according to a threshold range, wherein the grayscale distribution includes a distribution of a plurality of grayscale values ​​of a plurality of pixels on the measurement line segment corresponding to the multi-layer structure image, and analyzing the grayscale distribution includes comparing the plurality of grayscale values ​​of the plurality of pixels with the threshold range to determine a plurality of dark pixels corresponding to the plurality of dark layer thicknesses and a plurality of light pixels corresponding to the plurality of light layer thicknesses, The threshold range includes a first threshold range and a second threshold range, and analyzing the grayscale distribution includes: When it is determined that the plurality of grayscale values ​​belong to the first threshold range, determining that the plurality of pixels corresponding to the plurality of grayscale values ​​belong to the plurality of dark pixels; When it is determined that the plurality of grayscale values ​​belong to the second threshold range, determining that the pixels corresponding to the plurality of grayscale values ​​belong to the plurality of light-colored pixels; When it is determined that the interval formed by two consecutive grayscale values ​​includes the first upper limit value of the first threshold range, and when it is determined that the second pixel of the two consecutive pixels is the dark pixel, marking the second pixel of the two consecutive pixels as the starting point of the dark layer of the corresponding dark layer thickness; and When it is determined that the interval formed by two consecutive grayscale values ​​includes the second upper limit value of the second threshold range, and when it is determined that the second pixel of the two consecutive pixels is the light-colored pixel, the second pixel of the corresponding two consecutive pixels is marked as the starting point of the light-colored layer of the corresponding light-colored layer thickness.

2. The image analysis method according to claim 1, characterized in that: The plurality of dark layer thicknesses correspond to the number of the dark pixels of the plurality of dark layer thicknesses, and the plurality of light layer thicknesses correspond to the number of the light pixels of the plurality of light layer thicknesses.

3. The image analysis method according to claim 2, characterized in that: Analyzing the grayscale distribution includes: When it is determined that the interval formed by two consecutive grayscale values ​​includes the first upper limit value of the first threshold range, and when it is determined that the first pixel of two consecutive pixels is the dark pixel, marking the first pixel of the corresponding two consecutive pixels as the dark layer end point of the corresponding dark layer thickness; and When it is determined that the interval formed by two consecutive grayscale values ​​includes the second upper limit value of the second threshold range, and when it is determined that the first pixel of two consecutive pixels is the light-colored pixel, the first pixel of the corresponding two consecutive pixels is marked as the light-colored layer end point of the corresponding light-colored layer thickness.

4. The image analysis method according to claim 3, characterized in that: The multiple dark layer thicknesses correspond to the number of dark pixels between the dark layer starting point and the dark layer end point of the multiple dark layer thicknesses and the number of light pixels between the light layer starting point and the light layer end point of the multiple light layer thicknesses.

5. The image analysis method according to claim 4, characterized in that: It further includes merging the adjacent light layer thicknesses or dark layer thicknesses into a single light layer thickness or dark layer thickness after checking that the adjacent dark layer thicknesses or light layer thicknesses correspond to the same pixel.

6. An image analysis system, characterized in that: include: An electron microscope for providing a multi-layer structure image, wherein the multi-layer structure image includes a plurality of dark layer images and a plurality of light layer images, wherein the plurality of dark layer images and the plurality of light layer images are alternately arranged along a first direction, and the plurality of dark layer images and the plurality of light layer images extend along a second direction respectively, wherein the first direction is perpendicular to the second direction; A display device for displaying the multi-layer structure image; as well as An image analysis device is coupled to the electron microscope and the display device to obtain the multi-layer structure image provided by the electron microscope and output the multi-layer structure image to the display device, wherein the image analysis device comprises: A storage device including an image analysis module; and A processor is coupled to the storage device, wherein The processor inputs the multi-layer structure image into the image analysis module, The processor sets a measuring line segment on the multi-layer structure image, wherein the measuring line segment extends toward the first direction. The processor detects the grayscale distribution of the multi-layer structure image corresponding to the measurement line segment along the measurement line segment via the image analysis module. The processor analyzes the grayscale distribution via the image analysis module to determine the thickness of multiple dark layers and multiple light layers in the multi-layer structure image according to a threshold range. wherein the grayscale distribution includes a distribution of a plurality of grayscale values ​​of a plurality of pixels on the measurement line segment corresponding to the multi-layer structure image, and the processor analyzes the grayscale distribution including comparing the plurality of grayscale values ​​of the plurality of pixels with the threshold range to determine a plurality of dark pixels corresponding to the plurality of dark layer thicknesses and a plurality of light pixels corresponding to the plurality of light layer thicknesses, The threshold range includes a first threshold range and a second threshold range, and analyzing the grayscale distribution includes: When it is determined that the plurality of grayscale values ​​belong to the first threshold range, determining that the plurality of pixels corresponding to the plurality of grayscale values ​​belong to the plurality of dark pixels; When it is determined that the plurality of grayscale values ​​belong to the second threshold range, determining that the pixels corresponding to the plurality of grayscale values ​​belong to the plurality of light-colored pixels; When it is determined that the interval formed by two consecutive grayscale values ​​includes the first upper limit value of the first threshold range, and when it is determined that the second pixel of the two consecutive pixels is the dark pixel, marking the second pixel of the two consecutive pixels as the starting point of the dark layer of the corresponding dark layer thickness; and When it is determined that the interval formed by two consecutive grayscale values ​​includes the second upper limit value of the second threshold range, and when it is determined that the second pixel of the two consecutive pixels is the light-colored pixel, the second pixel of the corresponding two consecutive pixels is marked as the starting point of the light-colored layer of the corresponding light-colored layer thickness.

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