Photoetching machine connecting piece vibration fatigue damage detection system

Abstract

The photoetching machine connecting piece vibration fatigue damage detection system is composed of an offline test module, an online test module and a processor, the offline test module is used for testing the roughness of the surface of the connecting piece when a photoetching machine is in a non-operation state, and the online test module is used for testing the vibration parameters of the connecting piece when the photoetching machine is in an operation state. The method for the micro-amplitude vibration test comprises the following steps: decomposing a video into multi-scale and multi-direction sub-bands, extracting interested phase change and amplifying, then using an optimized one-dimensional row gradient domain to guide an image filter to finely eliminate double sides, artifacts and other noises in high-frequency sub-bands, and finally synthesizing the video after motion amplification, the method has superiority in the aspects of keeping structural definition and minimizing information loss.

Description

Technical Field

[0001] The present invention relates to the field of testing, and in particular to vibration fatigue damage detection of photolithography machine connectors. Background Art

[0002] Vibration is one of the common problems encountered in the mechanical structure of the lithography machine. Since vibration can cause resonance or fatigue of the structure and thus damage the structure, understanding the roughness and vibration parameters of the lithography machine connectors will avoid precision inaccuracies caused by vibration factors during use.

[0003] Improving the lithography accuracy of lithography machines is the main research direction of lithography machines and also the technical difficulty of lithography machines. The increasingly higher motion accuracy of lithography machines has increasingly stringent requirements on the vibration environment. The optical scanning resolution of lithography machines is at the nanometer level. Therefore, if the lithography machine equipment has vibration interference during operation, it may cause errors in the scanning results, which will ultimately affect the lithography accuracy of the lithography machine. As for the source of vibration, the surrounding environment includes tiny vibrations on the ground and vibrations when people walk by. Although these vibrations are relatively small, relative to the accuracy requirements of the lithography machine, measures must be taken to test these vibrations. At the same time, the lithography machine itself will also generate vibrations when working, and measures need to be taken to test these vibrations to minimize their impact on the motion accuracy of the lithography machine.

[0004] In the prior art, sensors are generally used to test the vibration parameters of lithography machines. However, due to the complex sources of vibration, more complex vibration interference, and the need to install accelerometers on the surface of the structure, the increased load may affect the dynamic characteristics of the test, resulting in poor test results. With the development of visual instruments (including high-resolution cameras and video cameras) and the advancement of image and video processing technology, vision-based vibration measurement is becoming increasingly popular in the field of lithography machine vibration testing. The most commonly used algorithms include template matching algorithms and digital image correlation algorithms. Template matching methods can track the displacement of the region of interest along a video frame sequence. Image algorithms are generally more accurate than template matching, but require the placement of a speckle pattern on the surface to be analyzed. Edge detection technology is also widely used, but these methods are suitable for objects with clear contours and can capture the edge movement of objects, but cannot provide accurate motion information for objects with unclear textures or blurred edges. In actual scenarios, the vibration amplitude of the mechanical structure of the lithography machine is usually small, which limits the practical application of target matching and tracking algorithms. Summary of the Invention

[0005] The photolithography machine connecting piece vibration fatigue damage detection system provided by the application is composed of an offline test module, an online test module and a processor, the offline test module tests the roughness of the connecting piece surface of the photolithography machine in a non-running state, and the online test module tests the vibration parameters of the connecting piece of the photolithography machine in a running state, since the photolithography machine is a precision instrument, the roughness of the connecting piece has a direct influence on the machining precision, and if the roughness is too large, the vibration amplitude will be increased when the photolithography machine is running, thereby affecting the machining precision, therefore, through the test of the offline test module, the connecting pieces with unqualified roughness can be effectively removed, and the online test module can test the vibration parameters of the connecting piece in real time in the running state of the photolithography machine, which can not only effectively avoid the running of the photolithography machine in a state of excessive vibration, but also will not affect the normal running of the photolithography machine in the test process.

[0006] Preferably, the photolithography machine connecting piece vibration fatigue damage detection system comprises an offline test module, an online test module and a processor, wherein the offline test module comprises a laser, a laser holder, a semi-pentagonal prism, a focusing lens, a prism holder, a first image acquisition device and a prism, the measured connecting piece is arranged on a test table, the laser holder holds the laser to emit a light beam to the measured connecting piece through the prism, the first image acquisition device sequentially acquires image information of the measured connecting piece through the semi-pentagonal prism and the focusing lens, the first image acquisition device transmits the acquired image information to the processor, the online test module comprises a second image acquisition device, the second image acquisition device is arranged above the measured connecting piece, the second image acquisition device transmits the acquired image information to the processor, the processor analyzes the roughness of the measured connecting piece and the vibration parameters when the photolithography machine is running according to the received image information, and if the corresponding threshold is exceeded, it is determined that the vibration fatigue damage is caused.

[0007] Preferably, the processor analyzes the roughness of the measured connecting piece according to the image information transmitted by the first image acquisition device.

[0008] The longitudinal coordinate y of the kth column pixel gray center of the image k , and

[0009]

[0010] wherein m is the number of sampling points, y i is the longitudinal coordinate of the i th row pixel gray center, f(x k ,y i ) is the gray value of the pixel point at the coordinate, f(x i ,y i ) is the gray value of the pixel point at the coordinate, x k is the transverse coordinate of the kth column pixel gray center, x i is the transverse coordinate of the i th row pixel gray center.

[0011] Then the roughness R of the measured connector 1 is calculated, and then,

[0012]

[0013] Wherein, m is the number of sampling points.

[0014] The processor stores corresponding roughness thresholds for different lithography machine connectors, and if the analyzed roughness of the measured connector is greater than the corresponding roughness threshold, the processor determines that the connector is unqualified.

[0015] The processor calculates the vibration parameters of the measured connector according to the image information transmitted by the received second image acquisition device.

[0016] Filtering using a guide image:

[0017]

[0018] Wherein, wherein u i is the high-frequency sub-band image after phase amplification, which is also a guide image, H i is the output image after filtering using the guide image, ω rk is a square window centered on the center pixel k, i, and are the average of the sum of linear coefficients, respectively, then:

[0019]

[0020] Wherein |ω rk | is the number of pixels in the local window ω rk , and the cost function E is:

[0021]

[0022] Wherein μ is a scalar, γ rk is defined as follows:

[0023]

[0024] Wherein χ(rk) is defined as σ u,1 (rk)σ u,16 (rk), the size of the filter window is set to 16, σ u,1 (rk) is the standard deviation of the image u in the local window ω1(rk), and the value of η is set to 4 / (ξ χ,∞ -min(χ(i))), ξ χ,∞ is the average of χ(i);

[0025] Wherein, the parameter Γ ui is an edge perception weight, defined as:

[0026]

[0027] where N is the image u i The number of pixels in , ε is a parameter to prevent the denominator from being 0, then,

[0028]

[0029] b rk =ξ X,16 (rk)-a rk ξ u,16 (rk)

[0030] Among them, * represents matrix dot multiplication, is the guide image u in the local window ω rk The variance in and ξ X,16 (rk) are u i *χ(i),u i and χ(i) in the local row window ω rk The mean of

[0031] Identify the region R in the video image and obtain the motion intensity value I R :

[0032]

[0033] where N R is the number of pixels in R, I p is the motion intensity of pixel p.

[0034] The number of video frames of the second image acquisition device is set to f, and the vibration profile equation D(t) within time t is:

[0035]

[0036] The processor selects the maximum value of D(t) as the vibration parameter of the tested connector 1. The processor stores a vibration parameter threshold. If the vibration parameter of the tested connector obtained by the processor analysis is greater than the stored vibration parameter threshold, the processor determines that the vibration fatigue damage of the connector has reached the upper limit, and the processor controls the lithography machine to stop running.

[0037] Preferably, the light beam emitted by the laser is a distributed light beam.

[0038] Preferably, the prism is an aspherical cylindrical lens, and the top of the prism is a curved roof line.

[0039] Preferably, the laser is a line structure laser with a line width less than 0.5 mm.

[0040] Preferably, the half-pentagonal prism is a 45° half-pentagonal prism, and the reflecting surface of the half-pentagonal prism is coated with aluminum and protected by black paint.

[0041] The photolithography connecting piece vibration fatigue damage detection system provided by the application combines complex domain processing and gradient domain processing, obtains subbands of different scales and directions through complex operable decomposition, and then generates new subbands by using phase difference amplification. The complex domain processing method has good sensitivity to slight vibration, combines subbands of different directions under the same scale to obtain amplified high-frequency and low-frequency subband images, and introduces an optimized one-dimensional row gradient domain guided image filtering method for filtering processing on the high-frequency subband images. The method combines the processing advantages of the gradient domain, reduces possible artifacts in the amplification process, and maintains the definition and structural contour features of the video images. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The photolithography connecting piece vibration fatigue damage offline test mechanical diagram of the application;

[0043] Figure 2 The photolithography connecting piece vibration fatigue damage offline test structure diagram of the application;

[0044] Figure 3 The photolithography connecting piece vibration fatigue damage offline test system diagram of the application;

[0045] Figure 4 The photolithography connecting piece vibration fatigue damage online test structure diagram of the application;

[0046] Figure 5 The photolithography connecting piece vibration test image comparison diagram of the application;

[0047] Figure 6 The photolithography connecting piece amplitude comparison diagram of the application.

[0048] The photolithography connecting piece amplitude comparison diagram of the application. DETAILED DESCRIPTION

[0049] The photolithography connecting piece vibration fatigue damage detection system provided by the application will be described in detail below in combination with the drawings and examples.

[0050] The flow charts and block diagrams in the accompanying drawings of the present invention illustrate the possible implementation architecture, functions and operations of the methods and systems according to the various embodiments of the present disclosure. It should be noted that each box in the block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code can include one or more executable instructions for realizing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flow chart and / or block diagram, and the combination of the boxes in the flow chart and / or block diagram, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0051] The terms "including," "comprising," and similar terms should be understood as open-ended terms, i.e., "including but not limited to," indicating that other contents may also be included. The same reference numerals in the embodiments and drawings indicate the same or similar components or structural features. Furthermore, when describing a component as being disposed on another component, this means that the component is directly disposed on the other component, or that the component is indirectly disposed on the other component, i.e., one or more components are disposed between the two components. When describing a component as being directly disposed on another component, this means that there are no other components between the two components.

[0052] The vibration fatigue damage detection system for connectors of a lithography machine of the present invention is composed of an offline test module, an online test module and a processor. The offline test module tests the surface roughness of the connectors of the lithography machine when it is not in operation, and the online test module tests the vibration parameters of the connectors of the lithography machine when it is in operation. Since the lithography machine is a precision instrument, the roughness of its connectors has a direct impact on the processing accuracy. If the roughness is too large, the vibration amplitude will increase when the lithography machine is in operation, thereby affecting the processing accuracy. Therefore, connectors with unqualified roughness can be effectively eliminated through the test of the offline test module. The online test module can perform real-time testing on the vibration parameters of the connectors when the lithography machine is in operation, which can not only effectively avoid the lithography machine from operating in a state of excessive vibration, but also will not affect the normal operation of the lithography machine during the test.

[0053] The photolithography machine connector vibration fatigue damage detection system of the present invention includes an offline test module, an online test module and a processor 9 .

[0054] like Figures 1-2As shown, the offline test module includes a laser 2, a laser clamp 3, a semi-pentagonal prism 4, a focusing lens 5, a prism clamp 6, a first image acquisition device 7 and a prism 8. The tested connection part 1 is set on the test bench. The laser clamp 3 clamps the laser 2 and emits a light beam to the tested connection part 1 through the prism 8. The first image acquisition device 7 collects image information of the tested connection part 1 through the semi-pentagonal prism 4 and the focusing lens 5 in turn, and the first image acquisition device 7 transmits the collected image information to the processor 9.

[0055] Specifically, the light beam emitted by the laser 2 is a distributed light beam. Uniform light intensity can reduce errors generated during image processing. Therefore, in the present invention, the light beam emitted by the laser 2 is uniformly distributed through the prism 8.

[0056] Specifically, the prism 8 is an aspheric cylindrical lens with a curved roof line at the top. The two-dimensional aspheric curve redistributes the intensity of the incident parallel light beam from the laser 2 into a uniform distribution in the far field.

[0057] Specifically, the laser 2 is a line structure laser with a line width less than 0.5 mm.

[0058] Specifically, a semi-pentagonal prism 4 is provided in the imaging optical path to adjust the image plane position so that the first image acquisition device 7 can receive the image vertically. At the same time, the receiving angle of the first image acquisition device 7 can be fixed, thereby reducing the degree of freedom of adjustment of the first image acquisition device 7.

[0059] Specifically, the half pentagonal prism 4 is a 45° half pentagonal prism, which deflects the main optical axis by 45°, and the reflecting surface is aluminum-plated and protected with black paint.

[0060] Specifically, the first image acquisition device 7 transmits the acquired image information to the processor 9, and the processor 9 analyzes the roughness of the tested connector 1 based on the received image information. The processor 9 stores corresponding roughness thresholds corresponding to different lithography machine connectors. If the analyzed roughness of the tested connector 1 is greater than the corresponding roughness threshold, the processor 9 determines that the connector is unqualified.

[0061] like Figure 3 As shown, the processor 9 calculates the roughness of the tested connection component 1 based on the received image information transmitted by the first image acquisition device 7 .

[0062] First calculate the vertical coordinate y of the grayscale centroid of the k-th column of the image k , then there is,

[0063]

[0064] Among them, m is the number of sampling points, y i is the vertical coordinate of the grayscale center of the pixel in the i-th row, f(xk y i ) is the gray value of the pixel point under the coordinate, f(x i ,y i ) is the gray value of the pixel point under the coordinate, x k is the horizontal coordinate of the gray gravity center of the kth column of pixels, x i is the horizontal coordinate of the gray gravity center of the ith row of pixels.

[0065] Then the roughness R of the measured connecting piece 1 is calculated, that is,

[0066]

[0067] Wherein, m is the number of sampling points.

[0068] As shown in Figure 4 , the online test module comprises a second image acquisition device 13, the second image acquisition device 13 is arranged above the measured connecting piece 1, the second image acquisition device 13 transmits the collected image information to the processor 9, the processor 9 analyzes the vibration parameters of the measured connecting piece 1 according to the received image information, the vibration parameter threshold is stored in the processor 9, if the vibration parameters of the measured connecting piece 1 analyzed by the processor 9 are greater than the vibration parameter threshold stored by the processor 9, the processor 9 determines that the vibration fatigue damage of the connecting piece has reached the upper limit, and the processor 9 controls the photolithography machine to stop running. Here, the measured connecting piece 1 is a silicon wafer table, which is only one of the specific embodiments of the present application. In actual testing, the tester can select different photolithography machine connecting pieces as the measured connecting piece 1 according to the specific test requirements, for example, it can be any one of the base frame 10, the objective lens 11, the stabilizing device 12, the illumination system 14, the mask table 15 and other connecting pieces.

[0069] Specifically, the processor 9 calculates the vibration parameters of the measured connecting piece 1 according to the image information transmitted by the second image acquisition device 13.

[0070] In the vibration parameter test method provided by the present application, the complex domain processing and the gradient domain processing are combined, the different scales and directions of the subbands are obtained through the decomposing of the complex number, then the new subbands are generated by using the phase difference amplification. This complex domain processing method has good sensitivity to slight vibration, the subbands of different directions under the same scale are combined to obtain the amplified high-frequency and low-frequency subband images, and a kind of optimized one-dimensional row gradient domain guided image filtering method is introduced for filtering processing on the high-frequency subband image. This method combines the processing advantages of the gradient domain, reduces the possible artifacts in the amplification process, and at the same time maintains the definition and structural outline features of the video image.

[0071] Firstly, the local linear correlation between the filtering output in the local window and the guided image is established:

[0072]

[0073] wherein, wherein u i is the phase amplified high frequency sub-band image, which is also the guided image, H i is the output image filtered using the guided image, ω rk is a square window centered at pixel k,i, used to index each pixel within the window, and are the average of the sum of linear coefficients, whose solution is given by the following equation:

[0074]

[0075]

[0076] wherein |ω rk | is the number of pixels in the local window ω rk , the optimal values of linear coefficients a rk and b rk are obtained by minimizing the cost function E, which is defined as:

[0077]

[0078] wherein μ is a scalar, whose value will affect the output result, γ rk is defined as follows:

[0079]

[0080] wherein χ(rk) is defined as σ u,1 (rk)σ u,16 (rk), the size of the filter window is set to 16, σ u,1 (rk) is the standard deviation of the image u in the local window ω1(rk), the value of η is set to 4 / (ξ χ,∞ -min(χ(i))), ξ χ,∞ is the average of χ(i), the value of γ rk close to 1 indicates that the pixel k is located in the edge area, if the pixel is located in the smooth area, the value of γ rk will be close to 0. Therefore, the edge preservation ability of the gradient domain guided filtering is superior to the prior art guided filtering algorithm.

[0081] wherein the parameter Γ ui is an edge-aware weight, defined as:

[0082]

[0083] wherein N is the image u i, ε is a very small parameter used to prevent the denominator from being 0. The edge-aware weight can determine the importance of each pixel to the guidance image, thereby reducing the misclassification of non-noise pixels as noise and further improving the algorithm's ability to retain image details.

[0084] Therefore, the linear coefficient a rk and b rk The optimal solution is:

[0085]

[0086] b rk =ξ X,16 (rk)-a rk ξ u,16 (rk)

[0087] Among them, * represents matrix dot multiplication, is the guide image u in the local window ω rk The variance in and ξ X,16 (rk) are u i *χ(i),u i and χ(i) in the local row window ω rk The mean in .

[0088] like Figure 5 As shown, Figure (a) is the original image, and Figure (b) is the image information processed by the processor 9 after the tested connection part 1 vibrates. The optimized one-dimensional row gradient domain guided Gaussian image filtering not only retains the image details and edges through the gradient domain guided filtering, but also reduces the computational complexity and improves the efficiency of separating stripe noise from high-frequency information images. While maintaining image stability and clarity, it improves the quality and visualization effect of the amplified video image, making the amplified video suitable for precise analysis and measurement.

[0089] The vibration parameter measurement method adopted by the processor 9 accurately captures the micro-vibration signal of the tested connection part 1. First, the standard video frame is reconstructed using the filtered high-frequency sub-band image and low-frequency sub-band. Then, the video frame is converted into a grayscale image, and the pixel-level motion changes are captured by identifying the motion information of all pixels in the area of ​​interest. Finally, the motion information of these pixels is averaged and the pixel motion intensity in the area of ​​interest is integrated to obtain accurate and reliable vibration parameters.

[0090] First, after identifying a region of interest R in the video image, the continuous frames of the video image are processed. This process obtains motion information from all pixels in R and captures pixel-level motion changes. Subsequently, the motion information intensity I of these pixels in R is averaged and the pixel motion intensity is integrated to obtain the motion intensity value I.R :

[0091]

[0092] where N R is the number of pixels in R, I p is the motion intensity of pixel p.

[0093] The video frame number of the second image acquisition device 13 is set to f, and the motion intensity I R is used to calculate the vibration profile equation D(t), which represents the displacement of R at time t:

[0094]

[0095] The processor 9 selects the maximum value of D(t) as the vibration parameter of the measured connecting piece 1.

[0096] As Figure 6 shown, Figure S1 is a waveform diagram of the vibration signal of the measured connecting piece 1 without analysis by the processor 9, Figure S2 is a waveform diagram of the vibration signal of the measured connecting piece 1 analyzed by the prior art, and Figure S3 is a waveform diagram of the vibration signal of the measured connecting piece 1 after analysis and processing by the processor 9 in the present application.

[0097] The method for micro-vibration testing in the present application decomposes the video into multi-scale and multi-direction subbands, extracts the phase changes of interest and amplifies them, then uses an optimized one-dimensional line gradient domain guided image filter to finely eliminate the bilateral, artifacts and other noises in the high-frequency subbands, and finally synthesizes the motion amplified video. This method has superiority in maintaining structural clarity and minimizing information loss.

[0098] Finally, it is again emphasized that the constituent elements / components described in the foregoing embodiments of the present application are only for illustrative purposes and are not intended to limit the scope of the present application. Other equivalent components or changes should be within the scope of protection of the present application.

Claims

1. A photolithography machine connector vibration fatigue damage detection system, characterized in that: The detection system includes an offline test module, an online test module and a processor (9); The offline test module comprises a laser (2), a laser clamp (3), a semi-pentagonal prism (4), a focusing lens (5), a prism clamp (6), a first image acquisition device (7) and a prism (8); the tested connector (1) is arranged on a test bench; the laser clamp (3) clamps the laser (2) and emits a light beam to the tested connector (1) through the prism (8); the first image acquisition device (7) sequentially acquires image information of the tested connector (1) through the semi-pentagonal prism (4) and the focusing lens (5); and the first image acquisition device (7) transmits the acquired image information to a processor (9); The online test module includes a second image acquisition device (13), which is arranged above the tested connection component (1), and transmits the acquired image information to the processor (9); The processor (9) analyzes the roughness of the tested connector (1) and the vibration parameters of the photolithography machine during operation based on the received image information, and determines that the roughness exceeds a corresponding threshold value as vibration fatigue damage.

2. The photolithography machine connector vibration fatigue damage detection system according to claim 1, characterized in that: The processor (9) analyzes the roughness of the tested connecting member (1) based on the received image information transmitted by the first image acquisition device (7). Calculate the vertical coordinate y of the grayscale centroid of the pixel in the kth column of the image k , then there is, Among them, m is the number of sampling points, y i is the vertical coordinate of the grayscale center of the pixel in the i-th row, f(x k ,y i ) is the gray value of the pixel at this coordinate, f(x i ,y i ) is the gray value of the pixel at this coordinate, x k is the horizontal coordinate of the grayscale centroid of the k-th column pixel, x i is the horizontal coordinate of the grayscale centroid of the pixel in the i-th row; Then calculate the roughness R of the tested connection part 1, then we have, Where m is the number of sampling points; The processor (9) stores corresponding roughness thresholds corresponding to different photolithography machine connectors. If the analyzed roughness of the tested connector (1) is greater than the corresponding roughness threshold, the processor (9) determines that the connector is unqualified.

3. The photolithography machine connector vibration fatigue damage detection system according to claim 2, characterized in that: The processor (9) calculates the vibration parameters of the tested connection member (1) based on the received image information transmitted by the second image acquisition device (13); Filter using a guided image: Among them, u i It is the high-frequency sub-band image after phase amplification, and also the guiding image, H i is the output image after filtering using the guidance image, ω rk is a square window centered on the center pixel k,i, and are the average values ​​of the sum of linear coefficients, then: where |ω rk | is the local window ω rk The number of pixels in, the cost function E is: where μ is a scalar, γ rk is defined as follows: where χ(rk) is defined as σ u,1 (rk)σ u,16 (rk), the filter window size is set to 16, σ u,1 (rk) is the standard deviation of image u in the local window ω1(rk), and the value of η is set to 4 / (ξ χ,∞ -min(χ(i))),ξ χ,∞ is the mean value of χ(i); Among them, the parameter Γ ui is the edge-aware weight, defined as: where N is the image u i The number of pixels in , ε is a parameter to prevent the denominator from being 0, then, b rk =ξ X,16 (rk)-a rk x u,16 (rk) Among them, * represents matrix dot multiplication, is the guide image u in the local window ω rk The variance in and ξ X,16 (rk) are u i *χ(i),u i and χ(i) in the local row window ω rk The mean of Identify the region R in the video image and obtain the motion intensity value I R : where N R is the number of pixels in R, I p is the motion intensity of pixel p; The number of video frames of the second image acquisition device (13) is set to f, and the vibration profile equation D(t) within time t is: The processor (9) selects the maximum value of D(t) as the vibration parameter of the tested connector 1. The processor (9) stores a vibration parameter threshold. If the vibration parameter of the tested connector (1) obtained by analysis by the processor (9) is greater than the stored vibration parameter threshold, the processor (9) determines that the vibration fatigue damage of the connector has reached an upper limit, and the processor (9) controls the photolithography machine to stop operating.

4. The photolithography machine connector vibration fatigue damage detection system according to claim 2, characterized in that: The light beam emitted by the laser (2) is a distributed light beam.

5. The photolithography machine connector vibration fatigue damage detection system according to claim 4, characterized in that: The prism (8) is an aspherical cylindrical lens, and the top of the prism (8) is a curved roof line.

6. The photolithography machine connector vibration fatigue damage detection system according to claim 5, characterized in that: The laser (2) is a line structure laser with a line width less than 0.5 mm.

7. The photolithography machine connector vibration fatigue damage detection system according to claim 6, characterized in that: The half pentagonal prism (4) is a 45-degree half pentagonal prism. The reflecting surface of the half pentagonal prism (4) is aluminum-plated and protected by black paint.

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