Silicon wafer alignment mark detection positioning method and system, electronic device and medium

Abstract

The application provides a method for detecting and positioning a silicon wafer alignment mark, which comprises the following steps: first, establishing a knowledge base, wherein the knowledge base has first characteristic information of first detection images of a plurality of known alignment marks; then, obtaining second characteristic information of a second detection image of a to-be-detected alignment mark; comparing the first characteristic information with the second characteristic information item by item in similarity; and obtaining position information of the to-be-detected alignment mark on the silicon wafer according to a plurality of known alignment marks obtained by comparison, so as to accurately find the known alignment mark and improve the detection and positioning precision and efficiency of the to-be-detected alignment mark. Correspondingly, the application also provides a detection and positioning system for a silicon wafer alignment mark, an electronic device and a non-transitory computer readable storage medium.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and in particular to a method, system, electronic device, and medium for detecting and locating alignment marks on silicon wafers. Background Technology

[0002] Currently, during the alignment process after either ADI (after-development inspection) or CMP (chemical mechanical polishing) processes, it is necessary to obtain the position of the alignment marks on the silicon wafer.

[0003] Alignment marks in silicon wafers have low reflectivity and are easily affected by pattern reflection or scattering signals. Existing methods for detecting and positioning alignment marks on silicon wafers have a high error rate, which affects alignment accuracy and detection efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, electronic device, and medium for detecting and locating alignment marks on silicon wafers, in order to solve the problems of high error rate and low efficiency in existing methods for detecting and locating alignment marks on silicon wafers.

[0005] To achieve the above objectives, the present invention provides a method for detecting and locating alignment marks on a silicon wafer, comprising:

[0006] A knowledge base is provided, wherein the knowledge base contains first feature information of a first detection image containing several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image;

[0007] A second detection image of the alignment mark to be tested is provided, and second feature information of the second detection image is obtained. The second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image; and,

[0008] The first feature information and the second feature information are compared item by item for similarity, and the position information of the alignment mark to be tested on the silicon wafer is obtained based on the known alignment marks obtained from the comparison.

[0009] Optionally, the first detection image and the second detection image have the same size.

[0010] Optionally, an image is taken of the silicon wafer with the known alignment mark, and the captured image is used as the first detection image; or, an image is taken of the silicon wafer with the known alignment mark, and the portion of the captured image containing the known alignment mark is cropped as the first detection image.

[0011] Optionally, the portion of the captured image containing the known alignment mark can be manually outlined and used as the first detection image.

[0012] Optionally, a photograph is taken of the silicon wafer with the alignment mark to be tested, and the photographed image is used as the second detection image; or, a photograph is taken of the silicon wafer with the alignment mark to be tested, and the portion of the photographed image containing the alignment mark to be tested is cropped as the second detection image.

[0013] Optionally, the portion of the captured image containing the alignment mark to be tested can be cropped using predetermined position and size information as the second detection image; or, the portion of the captured image containing the alignment mark to be tested can be cropped using an image pixel feature processing method as the second detection image.

[0014] Optionally, the image pixel feature processing method includes image segmentation and localization or image symmetry calculation.

[0015] Optionally, the step of obtaining the first feature information includes:

[0016] Obtain the pixel feature statistics of the first detected image;

[0017] Based on the pixel feature statistics of the first detected image, obtain the position and size information of the region of interest where the known alignment mark is located in the first detected image; and,

[0018] Pixel feature information of the region of interest where the known alignment mark is located in the first detection image is obtained based on the position and size information of the region of interest in the first detection image.

[0019] And / or, the step of obtaining the second feature information includes:

[0020] Obtain the pixel feature statistics of the second detected image;

[0021] Based on the pixel feature statistics of the second detection image, the position and size information of the region of interest where the alignment mark to be tested is located in the second detection image are obtained; and,

[0022] Pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is obtained based on the position and size information of the region of interest.

[0023] Optionally, the pixel feature statistics are used to characterize grayscale projection signals in at least two directions, and the pixel feature statistics of the first detection image and / or the second detection image are obtained by using the image grayscale projection method.

[0024] Optionally, autocorrelation calculation is performed on the grayscale projection signal to obtain the location information of the region of interest, and the size information of the region of interest is obtained based on the positions of the peaks and troughs of the grayscale projection signal; or, the grayscale projection signal is processed using an image pixel feature processing method to obtain the location and size information of the region of interest.

[0025] Optionally, the image pixel feature processing method includes image segmentation and localization or image symmetry calculation.

[0026] Optionally, before obtaining the pixel feature statistics of the first detection image, neighborhood pixel features are calculated for each pixel of the first detection image; and / or, before obtaining the pixel feature statistics of the second detection image, neighborhood pixel features are calculated for each pixel of the second detection image.

[0027] Optionally, the calculation of neighborhood pixel features includes the calculation of the average pixel value within the neighborhood, the calculation of the weighted average pixel value, the calculation of the pixel gradient, or the calculation of the pixel extreme value range.

[0028] Optionally, the step of performing a similarity comparison between the first feature information and the second feature information item by item, and obtaining the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained from the comparison includes:

[0029] The pixel feature statistics of the second detection image are compared one by one with the pixel feature statistics of the first detection image of the known alignment mark to obtain a number of candidate known alignment marks whose confidence in similarity is greater than a first set value.

[0030] The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared one by one with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the candidate known alignment marks to obtain a number of preferred known alignment marks corresponding to a similarity greater than a second set value; and,

[0031] The position information of the alignment mark to be tested on the silicon wafer is obtained based on several preferred known alignment marks.

[0032] Optionally, the knowledge base categorizes the known alignment marks into at least two categories based on the pixel feature statistics of the first detected image, and obtains the center pixel feature statistics of the first detected image for all known alignment marks in each category; and,

[0033] When performing a similarity comparison between the pixel feature statistics of the second detected image and the pixel feature statistics of the first detected image with the known alignment mark, the pixel feature statistics of the second detected image are compared with the center pixel feature statistics of each category to obtain several candidate categories with a similarity confidence greater than a third set value. All the known alignment marks in the candidate categories are then used as candidate known alignment marks.

[0034] Optionally, the pixel feature statistics of the second detection image can be compared one by one with the pixel feature statistics of the first detection image with the known alignment mark using a phase correlation method based on fast Fourier transform or a template matching search method.

[0035] Optionally, after obtaining a number of candidate known alignment marks, the pixel feature statistics of the second detection image are cross-correlated with the pixel feature statistics of the first detection image of the candidate known alignment marks one by one to obtain the best candidate known alignment mark from the candidate known alignment marks; and,

[0036] The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the best candidate known alignment mark to obtain the preferred known alignment mark.

[0037] Optionally, after obtaining several candidate categories, the pixel feature statistics of the second detected image are cross-correlated with the center pixel feature statistics of the candidate categories one by one to obtain the best candidate category from the candidate categories; and,

[0038] The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared with the pixel feature information of the regions of interest where all the known alignment marks in the best candidate category are located to obtain the preferred known alignment mark.

[0039] Optionally, the pixel feature information includes one or more of the overall pixel value of the rectangular region, geometric feature information, and grayscale feature information, wherein the geometric feature information includes gradient information and / or edge information of the rectangular region.

[0040] Optionally, the pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared one by one with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the candidate known alignment mark using the pixel grayscale template alignment method and / or the geometric template alignment method.

[0041] Optionally, the pixel grayscale template alignment method includes the standard optical flow method or the reverse-order synthetic image alignment algorithm.

[0042] Optionally, after obtaining several known alignment marks through comparison, the second detection image is traversed using the pixel feature information corresponding to the known alignment marks obtained through comparison, to obtain the position in the second detection image that is most similar to the pixel feature information corresponding to the known alignment marks obtained through comparison, thereby obtaining the position information of the alignment mark to be tested on the silicon wafer.

[0043] Optionally, after obtaining the position information of the alignment mark to be tested on the silicon wafer based on the comparison of several known alignment marks, the second feature information of the alignment mark to be tested is stored in the knowledge base.

[0044] The present invention also provides a detection and positioning system for silicon wafer alignment marks, comprising:

[0045] A storage module is used to store a knowledge base, wherein the knowledge base contains first feature information of a first detection image with several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image;

[0046] The feature acquisition module is used to provide a second detection image of the alignment mark to be tested, and to acquire second feature information of the second detection image. The second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image; and,

[0047] The similarity comparison module is used to perform a similarity comparison between the first feature information and the second feature information item by item, and to obtain the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained from the comparison.

[0048] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores instructions that, when executed by the processor, implement the method for detecting and locating silicon wafer alignment marks.

[0049] The present invention also provides a non-transitory computer-readable storage medium storing instructions that, when executed, implement a method for detecting and locating alignment marks on a silicon wafer.

[0050] In the silicon wafer alignment mark detection and positioning method provided by the present invention, a knowledge base is first established, which contains first feature information of a first detection image of a number of known alignment marks. Then, the second feature information of a second detection image of the alignment mark to be tested is obtained. The first feature information and the second feature information are compared item by item for similarity. Based on the number of known alignment marks obtained by comparison, the position information of the alignment mark to be tested on the silicon wafer is obtained. Since the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment mark is located in the first detection image, and the second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image, when comparing the similarity of the pixel feature statistics of the first feature information and the second feature information, the similarity of the first detection image and the second detection image is actually compared as a whole, so as to filter out a smaller number of known alignment marks from a large number of known alignment marks, narrowing the scope of the next comparison. Then, when comparing the similarity of the pixel feature information of the first feature information and the second feature information, the similarity of the region of interest where the known alignment mark is located in the first detection image is actually compared with the region of interest where the alignment mark to be tested is located in the second detection image, thereby accurately finding the known alignment mark and improving the detection and positioning accuracy and efficiency of the alignment mark to be tested. Accordingly, the present invention also provides a silicon wafer alignment mark detection and positioning system, electronic device, and non-transitory computer-readable storage medium. Attached Figure Description

[0051] Figure 1 This is a flowchart of the silicon wafer alignment mark detection and positioning method provided in Embodiment 1 of the present invention;

[0052] Figure 2 This is a schematic diagram of extracting the first detected image from the first original image according to Embodiment 1 of the present invention;

[0053] Figure 3a and Figure 3b This is a schematic diagram of the first grayscale projection signal and the second grayscale projection signal obtained by performing grayscale projection on the first detection image along the horizontal and vertical directions respectively, according to Embodiment 1 of the present invention.

[0054] Figure 4This is a schematic diagram of extracting the first matrix image from the first detected image according to Embodiment 1 of the present invention;

[0055] Figure 5a and Figure 5b This is a schematic diagram of the third grayscale projection signal and the fourth grayscale projection signal obtained by performing grayscale projection on the second detection image along the horizontal and vertical directions respectively, according to Embodiment 1 of the present invention.

[0056] Figure 6a and Figure 6b This is a schematic diagram of the first similarity signal and the second similarity signal provided in Embodiment 1 of the present invention;

[0057] Figure 7 This is a structural block diagram of the silicon wafer alignment mark detection and positioning system provided in Embodiment 1 of the present invention;

[0058] The attached figures are labeled as follows:

[0059] 10 - Storage module; 20 - Feature acquisition module; 30 - Similarity comparison module. Detailed Implementation

[0060] The specific embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0061] Figure 1 This is a flowchart illustrating the method for detecting and locating silicon wafer alignment marks provided in this embodiment. Figure 1 As shown, the method for detecting and locating the silicon wafer alignment mark includes:

[0062] Step S100: Provide a knowledge base, wherein the knowledge base contains first feature information of a first detection image with several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image;

[0063] Step S200: Provide a second detection image of the alignment mark to be tested, and obtain second feature information of the second detection image, the second feature information including pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image; and,

[0064] Step S300: Perform a similarity comparison between the first feature information and the second feature information item by item, and obtain the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained from the comparison.

[0065] First, step S100 is executed to construct the knowledge base.

[0066] Specifically, a silicon wafer with the known alignment mark (hereinafter referred to as the first silicon wafer) is provided, wherein the position information of the known alignment mark on the first silicon wafer is determined and known. A first image acquisition unit is used to take a picture of the known alignment mark on the first silicon wafer to obtain a first original image. The first original image needs to contain the complete known alignment mark, that is, the known alignment mark needs to be within the imaging field of view of the first image acquisition unit.

[0067] It should be understood that if the first silicon wafer has multiple known alignment marks, the first image acquisition unit can photograph each of the known alignment marks on the first silicon wafer one by one, thereby obtaining multiple first original images. Of course, if the first image acquisition unit photographs multiple known alignment marks on the first silicon wafer simultaneously, the photographed images can be manually processed to obtain a first original image of a single known alignment mark. That is, each first original image contains only one known alignment mark.

[0068] Since the size of the first original image acquired by the first image acquisition unit is relatively large (the area occupied by the known alignment mark in the first original image is relatively small), in this embodiment, the portion of the first original image containing the known alignment mark is cropped as the first detection image. This results in a smaller size and fewer pixels in the first detection image, reducing the difficulty and data volume of subsequent image processing and increasing the speed of image processing. For example, the portion of the first detection image containing the known alignment mark can be cropped manually as the first detection image, ensuring that the known alignment mark is present in the first detection image, and manual cropping ensures that the size of the cropped first detection image is appropriate.

[0069] Figure 2 This is a schematic diagram illustrating the extraction of the first detected image from the first original image, as provided in this embodiment. Figure 2As shown, the first original image 100 is relatively large, while the extracted first detection image 101 is relatively small. However, the first detection image 101 contains the complete known alignment mark 200. In this embodiment, the shape of the first detection image 101 is rectangular, which facilitates subsequent image processing. As an optional embodiment, the shape of the first detection image 101 is not limited to a rectangle, but can also be other shapes, such as a cross shape.

[0070] Of course, as an optional embodiment, the first original image may not be cropped. In this case, the first original image can be directly used as the first detection image for subsequent steps.

[0071] Next, the pixel feature statistics of the first detected image are obtained (hereinafter referred to as the first pixel feature statistics). The first pixel feature statistics are used to characterize the grayscale projection signals in at least two directions of the first detected image, and the first pixel feature statistics are obtained using the image grayscale projection method.

[0072] In this embodiment, the first pixel feature statistics are used to characterize the grayscale projection signals in the horizontal and vertical directions of the first detection image. When the first pixel feature statistics are obtained by the image grayscale projection method, the first detection image is grayscale projected along the horizontal and vertical directions respectively to obtain the first grayscale projection signal along the horizontal direction and the second grayscale projection signal along the vertical direction respectively.

[0073] As an optional embodiment, before obtaining the first pixel feature statistics, neighborhood pixel feature calculation can be performed on each pixel of the first detection image to remove noise from the first detection image. The neighborhood pixel feature calculation includes neighborhood pixel mean calculation, pixel weighted mean calculation, pixel gradient calculation, or pixel extreme value range calculation, etc.

[0074] Next, pixel feature information of the region of interest where the known alignment mark is located in the first detection image is obtained (hereinafter referred to as the first pixel feature information). The first pixel feature information may include one or more of the following: the overall pixel value, geometric feature information, and grayscale feature information of the rectangular region where the known alignment mark is located in the first detection image. The geometric feature information includes the gradient information and / or edge information of the rectangular region.

[0075] Specifically, when obtaining the first pixel feature information, the location and size information of the region of interest where the known alignment mark is located in the first detection image can be obtained first based on the first pixel feature statistics, and then the first pixel feature information can be obtained based on the location and size information of the region of interest where the known alignment mark is located in the first detection image.

[0076] For example, Figure 3a and Figure 3b This is a schematic diagram of the first grayscale projection signal and the second grayscale projection signal obtained by performing grayscale projection on the first detection image along the horizontal and vertical directions, respectively, as provided in this embodiment. Figure 3a and Figure 3b As shown, when obtaining the first pixel feature information, autocorrelation calculations can be performed on the first grayscale projection signal and the second grayscale projection signal respectively to obtain the position information (including position information along the horizontal and vertical directions) of the region of interest where the known alignment mark is located in the first detection image. Based on the positions of the peaks and troughs of the first grayscale projection signal and the second grayscale projection signal, the size information (including size information along the horizontal and vertical directions) of the region of interest where the known alignment mark is located in the first detection image can be obtained. After obtaining the position information and size information of the region of interest where the known alignment mark is located in the first detection image, a first matrix image of the region of interest where the known alignment mark is located in the first detection image can be extracted, and then one or more of the overall pixel values, geometric feature information, and grayscale feature information of the first matrix image can be obtained as the first pixel feature information.

[0077] As an optional embodiment, the image pixel feature processing method can also be used to process the first grayscale projection signal and the second grayscale projection signal to obtain the position information and size information of the region of interest where the known alignment mark is located in the first detection image.

[0078] Figure 4 This is a schematic diagram illustrating the extraction of the first matrix image from the first detected image, as provided in this embodiment. Figure 4 As shown, the first detection image 101 is relatively large, while the extracted first matrix image 102 is relatively small. However, the first matrix image 102 also contains the complete known alignment mark 200. In this embodiment, the region of interest is rectangular, but it should not be limited to this; the region of interest can also be other shapes, such as a cross shape.

[0079] As an optional embodiment, the first matrix image may not be extracted from the first detection image. The first pixel feature information can be directly calculated based on the first detection image and the position and size information of the region of interest where the known alignment mark is located in the first detection image.

[0080] Furthermore, the first pixel feature statistics and the first pixel feature information together constitute the first feature information of the first detected image. It can be understood that the pixel feature statistics can characterize the overall pixel features of the first detected image, while the first pixel feature information can characterize the pixel features of a small region of interest in the first detected image that can accommodate the known alignment mark.

[0081] It should be understood that for other types of known alignment marks, the above steps can be repeated to obtain the first feature information of several types of known alignment marks. Then, based on the first feature information of several types of known alignment marks, formatted data is generated according to a standard template to form the knowledge base. The knowledge base can be stored in computer memory or non-volatile storage media in the form of formatted files and / or database form data. The formatted files include, but are not limited to, formatted binary files, formatted text files, JSON files, XML files, YAML files, or CSV files.

[0082] It should be understood that the known alignment marks referred to in this embodiment are known alignment marks located at different positions on the first silicon wafer. For example, known alignment marks at different positions on the same first silicon wafer should be considered as different types of known alignment marks. Even if the known alignment marks on different first silicon wafers are in corresponding positions (the first silicon wafers in the same batch are manufactured sequentially according to the same process), they should still be considered as different types of known alignment marks due to process deviations.

[0083] Next, step S200 is executed to obtain the second feature information of the second detection image of the alignment mark to be tested.

[0084] Specifically, a silicon wafer with the alignment mark to be tested (hereinafter referred to as the second silicon wafer) is provided. The position information of the alignment mark to be tested on the second silicon wafer is unknown and needs to be detected. A second image acquisition unit is used to take a picture of the alignment mark to be tested on the second silicon wafer to obtain a second original image. The second original image needs to contain the complete alignment mark to be tested, that is, the alignment mark to be tested needs to be within the imaging field of view of the second image acquisition unit.

[0085] It should be understood that if the second silicon wafer has multiple alignment marks to be tested, the second image acquisition unit can take pictures of each alignment mark to be tested on the second silicon wafer one by one, thereby obtaining multiple second original images. That is, each second original image contains only one alignment mark to be tested.

[0086] Since the second original image acquired by the second image acquisition unit is relatively large (the area occupied by the alignment mark to be tested in the second original image is relatively small), in this embodiment, the portion of the second original image containing the alignment mark to be tested is cropped as the second detection image. This results in a smaller size and fewer pixels in the second detection image, reducing the difficulty and data volume of subsequent image processing and increasing the speed of image processing. For example, the portion containing the alignment mark to be tested can be cropped from the second original image using predetermined position and size information; alternatively, the portion containing the alignment mark to be tested in the second detection image can be cropped using an image pixel feature processing method, such as an image segmentation and localization method or an image symmetry calculation method.

[0087] Of course, as an optional embodiment, the second original image may not be cropped. In this case, the second original image can be directly used as the second detection image for subsequent steps.

[0088] In this embodiment, the first detection image and the second detection image are the same size to facilitate the subsequent similarity comparison step. Therefore, if the portion containing the alignment mark to be tested is cropped from the second original image using predetermined position and size information as the second detection image, the approximate position of the alignment mark to be tested on the second silicon wafer can be obtained in advance, and the second detection image can be cropped from the second original image using the same size as the first detection image. When cropping the portion containing the alignment mark to be tested from the second detection image using the image pixel feature processing method, the size of the image to be cropped also needs to be preset in the image pixel feature processing method according to the size of the first detection image.

[0089] Next, the pixel feature statistics of the second detected image are obtained (hereinafter referred to as the second pixel feature statistics). The second pixel feature statistics are used to characterize the grayscale projection signals in at least two directions of the second detected image, and the second pixel feature statistics are obtained using the image grayscale projection method.

[0090] In this embodiment, the second pixel feature statistics are used to characterize the grayscale projection signals in the horizontal and vertical directions of the second detection image. When the second pixel feature statistics are obtained by the image grayscale projection method, the second detection image is grayscale projected along the horizontal and vertical directions respectively to obtain the third grayscale projection signal along the horizontal direction and the fourth grayscale projection signal along the vertical direction respectively.

[0091] As an optional embodiment, before obtaining the second pixel feature statistics, neighborhood pixel feature calculation can be performed on each pixel of the second detection image to remove noise from the second detection image. The neighborhood pixel feature calculation includes neighborhood pixel mean calculation, pixel weighted mean calculation, pixel gradient calculation, or pixel extreme value range calculation, etc.

[0092] Next, pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is obtained (hereinafter referred to as the second pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image). The second pixel feature information may include one or more of the following: the overall pixel value, geometric feature information, and grayscale feature information of the rectangular region where the alignment mark to be tested is located in the second detection image. The geometric feature information includes the gradient information and / or edge information of the rectangular region.

[0093] It should be understood that if the first pixel feature information contains only one feature, the obtained second pixel feature information should contain the corresponding feature information. For example, if the first pixel feature information contains only geometric feature information, the second pixel feature information should also contain geometric feature information. If the first pixel feature information contains multiple features, the obtained second pixel feature information may contain one or more of the corresponding feature information. For example, if the first pixel feature information contains geometric feature information and grayscale feature information, the second pixel feature information may contain geometric feature information and / or grayscale feature information.

[0094] Specifically, when obtaining the second pixel feature information, the location and size information of the region of interest where the alignment mark to be tested is located in the second detection image can be obtained first based on the second pixel feature statistics, and then the second pixel feature information can be obtained based on the location and size information of the region of interest where the alignment mark to be tested is located in the second detection image.

[0095] For example, Figure 5a and Figure 5b This is a schematic diagram of the third grayscale projection signal and the fourth grayscale projection signal obtained by performing grayscale projection on the second detection image along the horizontal and vertical directions, respectively, as provided in this embodiment. Figure 5a and Figure 5b As shown, when obtaining the second pixel feature information, autocorrelation calculations can be performed on the third grayscale projection signal and the fourth grayscale projection signal respectively to obtain the position information (including position information along the horizontal and vertical directions) of the region of interest where the alignment mark to be tested is located in the second detection image. Based on the positions of the peaks and troughs of the third grayscale projection signal and the fourth grayscale projection signal, the size information (including size information along the horizontal and vertical directions) of the region of interest where the alignment mark to be tested is located in the second detection image can be obtained. After obtaining the position information and size information of the region of interest where the alignment mark to be tested is located in the second detection image, the second matrix image of the region of interest where the alignment mark to be tested is located in the second detection image can be extracted, and then one or more of the overall pixel values, geometric feature information, and grayscale feature information of the second matrix image can be obtained as the second pixel feature information.

[0096] As an optional embodiment, the image pixel feature processing method can also be used to process the third grayscale projection signal and the fourth grayscale projection signal to obtain the position information and size information of the region of interest where the alignment mark to be tested is located in the second detection image.

[0097] As an optional embodiment, the second matrix image may not be extracted from the second detection image. The second pixel feature information can be directly calculated based on the second detection image and the position and size information of the region of interest where the alignment mark to be tested is located in the second detection image.

[0098] Furthermore, the second pixel feature statistics and the second pixel feature information together constitute the second feature information of the second detected image. It can be understood that the pixel feature statistics can characterize the overall pixel features of the second detected image, while the second pixel feature information can characterize the pixel features of a small region of interest in the second detected image that can accommodate the alignment mark to be tested.

[0099] Next, step S300 is executed, in which the first feature information and the second feature information are compared item by item for similarity.

[0100] Specifically, the second pixel feature statistics are compared with the first pixel feature statistics one by one to obtain a number of candidate known alignment marks whose confidence level (used to evaluate the authenticity of the similarity between the second pixel feature statistics and the first pixel feature statistics in the knowledge base) is greater than a first set value. The first feature information of the first detection image of the candidate known alignment marks is called the candidate first feature information (including the candidate first pixel feature statistics and the candidate first pixel feature information).

[0101] As an optional embodiment, the phase correlation method based on Fast Fourier Transform is used to compare the similarity of the second pixel feature statistics with the first pixel feature statistics in the knowledge base one by one. Specifically, the third and fourth gray-level projection signals in the second pixel feature statistics can be converted to the frequency domain firstly, and then the first and second gray-level projection signals in all the first pixel feature statistics can be converted to the frequency domain. The similarity between the second pixel feature statistics and each of the first pixel feature statistics can then be obtained based on the phase correlation method.

[0102] As an optional embodiment, a template matching search method can also be used to compare the similarity of the second pixel feature statistics with the first pixel feature statistics one by one. Specifically, the similarity between the second pixel feature statistics and each of the first pixel feature statistics is calculated using a similarity metric based on a sliding window. The similarity metric includes L1 norm, L2 norm, p-norm, infinity norm, cosine similarity, etc.

[0103] Furthermore, when obtaining the similarity between the second pixel feature statistics and the first pixel feature statistics using the phase correlation method based on Fast Fourier Transform or the template matching search method, a first similarity signal and a second similarity signal are generated. The first similarity signal is used to characterize the confidence level of the similarity between the third gray-scale projection signal of the second pixel feature statistics and the first gray-scale projection signals of all the first pixel feature statistics. The second similarity signal is used to characterize the confidence level of the similarity between the fourth gray-scale projection signal of the second pixel feature statistics and the second gray-scale projection signals of all the first pixel feature statistics. Based on the first similarity signal and the second similarity signal, the confidence level of the similarity between the second pixel feature statistics and each of the first pixel feature statistics can be obtained.

[0104] In this embodiment, candidate known alignment marks are obtained based on the confidence level of similarity, rather than simply using similarity to obtain candidate known alignment marks. The resulting candidate known alignment marks are more likely to match the alignment mark to be tested, thus improving the detection accuracy.

[0105] Figure 6a and Figure 6b This is a schematic diagram of the first similarity signal and the second similarity signal provided in this embodiment. Figure 6a and Figure 6b In this context, the location of the peak value is the position of the first pixel feature statistics in the knowledge base, and the specific value of the peak value can be converted into a confidence level that measures the similarity between the second pixel feature statistics and the corresponding first pixel feature statistics.

[0106] Next, the second pixel feature information is compared with the candidate second pixel feature information one by one to obtain a number of preferred known alignment marks corresponding to a similarity greater than a second set value.

[0107] Further, as an optional embodiment, after obtaining a number of candidate known alignment marks, the second pixel feature statistics can be cross-correlated with the candidate first pixel feature statistics one by one (specifically, the third grayscale projection signal can be cross-correlated with each candidate first grayscale projection signal, and the fourth grayscale projection signal can be cross-correlated with each candidate second grayscale projection signal) to obtain the best candidate known alignment mark from the candidate known alignment marks. In this way, a best candidate known alignment mark can be selected from the candidate known alignment marks. When comparing the second pixel feature information with the candidate second pixel feature information one by one, only the second pixel feature information needs to be compared with the second pixel feature information of the best candidate, thereby improving the comparison efficiency.

[0108] As an optional embodiment, the second pixel feature information can be compared with the candidate second pixel feature information one by one using the pixel grayscale template alignment method and / or the geometric template alignment method. The pixel grayscale template alignment method may include the standard optical flow method or the reverse order synthetic image alignment method, etc.

[0109] Furthermore, the position information of the alignment mark to be tested on the second silicon wafer can be obtained based on the several preferred known alignment marks obtained through comparison. Specifically, the second detection image can be traversed using the first pixel feature information corresponding to the preferred known alignment marks to obtain the position in the second detection image that is most similar to the first pixel feature information corresponding to the preferred known alignment marks, and finally the position information of the alignment mark to be tested on the silicon wafer can be obtained.

[0110] It is understood that if there is only one preferred known alignment mark, the first pixel feature information corresponding to the preferred known alignment mark can be used to traverse the second detection image. If there are at least two preferred known alignment marks, the most preferred known alignment mark can be selected from the preferred known alignment marks, and the first pixel feature information corresponding to the most preferred known alignment mark can be used to traverse the second detection image; alternatively, the average value of the first pixel feature information corresponding to the preferred known alignment mark can be taken, and then the average value of the first pixel feature information corresponding to the preferred known alignment mark can be used to traverse the second detection image.

[0111] As an optional embodiment, after obtaining the position information of the alignment mark to be tested on the silicon wafer, the second feature information of the alignment mark to be tested can be stored in the knowledge base, thereby expanding the content of the knowledge base.

[0112] Based on this, this embodiment also provides a detection and positioning system for silicon wafer alignment marks. Figure 7 This is a structural block diagram of the silicon wafer alignment mark detection and positioning system provided in this embodiment, as shown below. Figure 7 As shown, the silicon wafer alignment mark detection and positioning system includes:

[0113] Storage module 10 is used to store a knowledge base, wherein the knowledge base contains first feature information of a first detection image with several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image;

[0114] The feature acquisition module 20 is used to provide a second detection image of the alignment mark to be tested, and to acquire second feature information of the second detection image. The second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image; and,

[0115] The similarity comparison module 30 is used to perform a similarity comparison between the first feature information and the second feature information item by item, and to obtain the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained by the comparison.

[0116] This embodiment also provides an electronic device, including a processor and a memory, wherein the memory stores instructions, and when the instructions are executed by the processor, the above-mentioned steps of detecting and locating the silicon wafer alignment mark are implemented.

[0117] The processor can execute various actions and processes according to instructions stored in the memory. Specifically, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 architecture or an ARM architecture, etc.

[0118] The memory stores executable instructions that are executed by a processor using the silicon alignment mark detection and positioning method described above. The memory can be volatile or non-volatile, or may include both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0119] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which instructions are stored, which, when executed, can implement the steps in the silicon wafer alignment mark detection and positioning method described above.

[0120] Similarly, the non-transitory computer-readable storage medium in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the computer-readable storage medium described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0121] Example 2

[0122] The difference from Embodiment 1 is that, in this embodiment, the known alignment markers in the knowledge base are distributed in a clustered manner to facilitate similarity comparison.

[0123] Specifically, the known alignment marks are divided into at least two categories based on the first pixel feature statistics. For example, clustering algorithms are used to cluster the first pixel feature statistics to classify the known alignment marks. The first feature information of the first detection image of the known alignment marks belonging to the same category will be grouped together instead of being scattered in the knowledge base.

[0124] Next, obtain the center pixel feature statistics of all the first feature information in each category. For example, calculate the mean of all the first pixel feature statistics in each category to obtain the center pixel feature statistics of all the first pixel feature statistics in each category.

[0125] When comparing the second pixel feature statistics with the first pixel feature statistics one by one, the second pixel feature statistics are first compared with the center pixel feature statistics of each category to obtain a number of candidate categories with a similarity confidence greater than a third set value. All known alignment marks in the candidate categories are then used as candidate known alignment marks.

[0126] In this embodiment, since it is not necessary to compare the second pixel feature statistics with the first pixel feature statistics one by one, the comparison efficiency can be improved.

[0127] Furthermore, as an optional embodiment, after obtaining several candidate categories, the second pixel feature statistics can be cross-correlated with the center pixel feature statistics of each candidate category to obtain the best candidate category from the candidate categories. This allows for the selection of an optimal candidate category from the candidate categories. When performing similarity comparisons between the second pixel feature information and the second pixel feature information of each candidate category, only the second pixel feature information needs to be compared with the second pixel feature information of the optimal candidate category, thereby improving comparison efficiency.

[0128] In summary, in the silicon wafer alignment mark detection and positioning method provided in the embodiments of the present invention, a knowledge base is first established, which contains first feature information of a first detection image of a number of known alignment marks. Then, second feature information of a second detection image of the alignment mark to be tested is obtained. The first feature information and the second feature information are compared item by item for similarity. Based on the number of known alignment marks obtained from the comparison, the position information of the alignment mark to be tested on the silicon wafer is obtained. Since the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment mark is located in the first detection image, and the second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image, when comparing the similarity of the pixel feature statistics of the first feature information and the second feature information, the similarity of the first detection image and the second detection image is actually compared as a whole, so as to filter out a smaller number of known alignment marks from a large number of known alignment marks, narrowing the scope of the next comparison. Then, when comparing the similarity of the pixel feature information of the first feature information and the second feature information, the similarity of the region of interest where the known alignment mark is located in the first detection image is actually compared with the region of interest where the alignment mark to be tested is located in the second detection image, thereby accurately finding the known alignment mark and improving the detection and positioning accuracy and efficiency of the alignment mark to be tested. Accordingly, the present invention also provides a silicon wafer alignment mark detection and positioning system, electronic device, and non-transitory computer-readable storage medium.

[0129] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0130] It should also be noted that although the present invention has been disclosed above with reference to preferred embodiments, these embodiments are not intended to limit the present invention. For any person skilled in the art, many possible variations and modifications can be made to the technical solutions of the present invention based on the disclosed technical content, or equivalent embodiments can be modified accordingly, without departing from the scope of the present invention. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the present invention shall still fall within the scope of protection of the present invention.

[0131] It should also be understood that, unless otherwise specified or indicated, the terms “first,” “second,” “third,” etc., in the specification are used only to distinguish the various components, elements, and steps in the specification, and not to indicate the logical or sequential relationships between the various components, elements, and steps.

[0132] Furthermore, it should be recognized that the terminology described herein is used only to describe particular embodiments and not to limit the scope of the invention. It must be noted that the singular forms “a” and “an” used herein and in the appended claims include plural bases unless the context clearly indicates otherwise. For example, a reference to “a step” or “an apparatus” means a reference to one or more steps or apparatuses, and may include secondary steps and secondary apparatuses. All conjunctions used should be understood in the broadest sense. Also, the word “or” should be understood to have the definition of logical “or” rather than logical “exclusive OR”, unless the context clearly indicates otherwise. Furthermore, implementation of the methods and / or devices in embodiments of the invention may include performing selected tasks manually, automatically, or in combination.

Claims

1. A method for detecting and locating alignment marks on a silicon wafer, characterized in that, include: A knowledge base is provided, wherein the knowledge base contains first feature information of a first detection image containing several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image; A second detection image of the alignment mark to be tested is provided, and second feature information of the second detection image is obtained. The second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image. as well as, The first feature information and the second feature information are compared item by item for similarity, and the position information of the alignment mark to be tested on the silicon wafer is obtained based on the known alignment marks obtained from the comparison. The steps of performing a similarity comparison between the first feature information and the second feature information item by item, and obtaining the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained from the comparison, include: The pixel feature statistics of the second detection image are compared one by one with the pixel feature statistics of the first detection image of the known alignment mark to obtain a number of candidate known alignment marks whose confidence in similarity is greater than a first set value. The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared one by one with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the candidate known alignment marks to obtain a number of preferred known alignment marks corresponding to a similarity greater than a second set value; and, The position information of the alignment mark to be tested on the silicon wafer is obtained based on several preferred known alignment marks.

2. The method for detecting and locating silicon wafer alignment marks as described in claim 1, characterized in that, The first detection image and the second detection image have the same size.

3. The method for detecting and locating silicon wafer alignment marks as described in claim 1 or 2, characterized in that, Take a picture of the silicon wafer with the known alignment mark and use the picture as the first detection image; or, take a picture of the silicon wafer with the known alignment mark and crop the portion of the picture with the known alignment mark as the first detection image.

4. The method for detecting and locating alignment marks on silicon wafers as described in claim 3, characterized in that, The portion of the captured image containing the known alignment mark is manually drawn and selected as the first detection image.

5. The method for detecting and locating silicon wafer alignment marks as described in claim 1 or 2, characterized in that, Take a picture of the silicon wafer with the alignment mark to be tested, and use the pictured image as the second detection image; or, take a picture of the silicon wafer with the alignment mark to be tested, and crop the portion of the pictured image containing the alignment mark to be tested as the second detection image.

6. The method for detecting and locating alignment marks on a silicon wafer as described in claim 5, characterized in that, The portion of the captured image containing the alignment mark to be tested is cropped using predetermined position and size information and used as the second detection image; or, the portion of the captured image containing the alignment mark to be tested is cropped using an image pixel feature processing method and used as the second detection image.

7. The method for detecting and locating silicon wafer alignment marks as described in claim 6, characterized in that, The image pixel feature processing method includes image segmentation and localization method or image symmetry calculation method.

8. The method for detecting and locating alignment marks on a silicon wafer as described in claim 1, characterized in that, The steps for obtaining the first feature information include: Obtain the pixel feature statistics of the first detected image; Based on the pixel feature statistics of the first detected image, obtain the position and size information of the region of interest where the known alignment mark is located in the first detected image; and, Pixel feature information of the region of interest where the known alignment mark is located in the first detection image is obtained based on the position and size information of the region of interest in the first detection image. And / or, the step of obtaining the second feature information includes: Obtain the pixel feature statistics of the second detected image; Based on the pixel feature statistics of the second detection image, the position and size information of the region of interest where the alignment mark to be tested is located in the second detection image are obtained; and, Pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is obtained based on the position and size information of the region of interest.

9. The method for detecting and locating silicon wafer alignment marks as described in claim 8, characterized in that, The pixel feature statistics are used to characterize the grayscale projection signals in at least two directions. The pixel feature statistics of the first detection image and / or the second detection image are obtained by using the image grayscale projection method.

10. The method for detecting and locating silicon wafer alignment marks as described in claim 9, characterized in that, Autocorrelation calculation is performed on the grayscale projection signal to obtain the location information of the region of interest, and the size information of the region of interest is obtained based on the positions of the peaks and troughs of the grayscale projection signal. Alternatively, the grayscale projection signal can be processed using image pixel feature processing methods to obtain the location and size information of the region of interest.

11. The method for detecting and locating silicon wafer alignment marks as described in claim 10, characterized in that, The image pixel feature processing method includes image segmentation and localization method or image symmetry calculation method.

12. The method for detecting and locating silicon wafer alignment marks as described in claim 9, characterized in that, Before obtaining the pixel feature statistics of the first detection image, neighborhood pixel features are calculated for each pixel of the first detection image; and / or, before obtaining the pixel feature statistics of the second detection image, neighborhood pixel features are calculated for each pixel of the second detection image.

13. The method for detecting and locating silicon wafer alignment marks as described in claim 12, characterized in that, The calculation of neighborhood pixel features includes the calculation of the mean value of pixels in the neighborhood, the calculation of the weighted mean value of pixels, the calculation of pixel gradient, or the calculation of the extreme value range of pixels.

14. The method for detecting and locating alignment marks on a silicon wafer as described in claim 1, characterized in that, The knowledge base divides the known alignment marks into at least two categories based on the pixel feature statistics of the first detection image, and obtains the center pixel feature statistics of the first detection image for all the known alignment marks in each category; as well as, When performing a similarity comparison between the pixel feature statistics of the second detected image and the pixel feature statistics of the first detected image with the known alignment mark, the pixel feature statistics of the second detected image are compared with the center pixel feature statistics of each category to obtain several candidate categories with a similarity confidence greater than a third set value. All the known alignment marks in the candidate categories are then used as candidate known alignment marks.

15. The method for detecting and locating silicon wafer alignment marks as described in claim 1 or 14, characterized in that, The pixel feature statistics of the second detection image are compared one by one with the pixel feature statistics of the first detection image with the known alignment mark by using a phase correlation method or template matching search method based on fast Fourier transform.

16. The method for detecting and locating silicon wafer alignment marks as described in claim 1, characterized in that, After obtaining several candidate known alignment marks, the pixel feature statistics of the second detection image and the pixel feature statistics of the first detection image of the candidate known alignment marks are cross-correlated one by one to obtain the best candidate known alignment mark from the candidate known alignment marks; as well as, The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the best candidate known alignment mark to obtain the preferred known alignment mark.

17. The method for detecting and locating silicon wafer alignment marks as described in claim 14, characterized in that, After obtaining several candidate categories, the pixel feature statistics of the second detection image are cross-correlated with the center pixel feature statistics of the candidate categories one by one to obtain the best candidate category from the candidate categories. as well as, The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared with the pixel feature information of the regions of interest where all the known alignment marks in the best candidate category are located to obtain the preferred known alignment mark.

18. The method for detecting and locating silicon wafer alignment marks as described in claim 1 or 14, characterized in that, The pixel feature information includes one or more of the following: the overall pixel value of the rectangular region, geometric feature information, and grayscale feature information. The geometric feature information includes gradient information and / or edge information of the rectangular region.

19. The method for detecting and locating alignment marks on a silicon wafer as described in claim 18, characterized in that, The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared one by one with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the candidate known alignment mark. The similarity is compared using the pixel grayscale template alignment method and / or the geometric template alignment method.

20. The method for detecting and locating silicon wafer alignment marks as described in claim 19, characterized in that, The pixel grayscale template alignment method includes the standard optical flow method or the reverse-order synthetic image alignment algorithm.

21. The method for detecting and locating alignment marks on a silicon wafer as described in claim 1, characterized in that, After obtaining several known alignment marks through comparison, the second detection image is traversed using the pixel feature information corresponding to the known alignment marks obtained through comparison, and the position in the second detection image that is most similar to the pixel feature information corresponding to the known alignment marks obtained through comparison is obtained, thereby obtaining the position information of the alignment mark to be tested on the silicon wafer.

22. The method for detecting and locating alignment marks on a silicon wafer as described in claim 1, characterized in that, After obtaining the position information of the alignment mark to be tested on the silicon wafer by comparing several known alignment marks, the second feature information of the alignment mark to be tested is stored in the knowledge base.

23. A detection and positioning system for silicon wafer alignment marks, characterized in that, include: A storage module is used to store a knowledge base, wherein the knowledge base contains first feature information of a first detection image with several known alignment marks, and the first feature information includes pixel feature statistics of the first detection image and pixel feature information of the region of interest where the known alignment marks are located in the first detection image; The feature acquisition module is used to provide a second detection image of the alignment mark to be tested, and to acquire second feature information of the second detection image. The second feature information includes pixel feature statistics of the second detection image and pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image. as well as, The similarity comparison module is used to perform item-by-item similarity comparison between the first feature information and the second feature information, and to obtain the position information of the alignment mark to be tested on the silicon wafer based on the known alignment marks obtained from the comparison. The similarity comparison module is further configured to perform a similarity comparison between the pixel feature statistics of the second detection image and the pixel feature statistics of the first detection image with the known alignment mark, one by one, to obtain a number of candidate known alignment marks with a similarity confidence greater than a first set value. The pixel feature information of the region of interest where the alignment mark to be tested is located in the second detection image is compared one by one with the pixel feature information of the region of interest where the known alignment mark is located in the first detection image of the candidate known alignment marks to obtain a number of preferred known alignment marks corresponding to a similarity greater than a second set value; and, The position information of the alignment mark to be tested on the silicon wafer is obtained based on several preferred known alignment marks.

24. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores instructions that, when executed by the processor, implement the method for detecting and locating silicon wafer alignment marks as described in any one of claims 1 to 22.

25. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores instructions that, when executed, implement the method for detecting and locating silicon wafer alignment marks as described in any one of claims 1 to 22.

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