CN117495722B - Image processing method for nanoimprint lithography - Google Patents
Image processing method for nanoimprint lithography Download PDFInfo
- Publication number
- CN117495722B CN117495722B CN202311785282.6A CN202311785282A CN117495722B CN 117495722 B CN117495722 B CN 117495722B CN 202311785282 A CN202311785282 A CN 202311785282A CN 117495722 B CN117495722 B CN 117495722B
- Authority
- CN
- China
- Prior art keywords
- pixel
- pixel point
- image
- fuzzy
- processed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001127 nanoimprint lithography Methods 0.000 title claims abstract description 11
- 238000003672 processing method Methods 0.000 title claims abstract description 9
- 239000000758 substrate Substances 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000001459 lithography Methods 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 abstract description 8
- 230000002159 abnormal effect Effects 0.000 abstract description 2
- 230000005856 abnormality Effects 0.000 abstract description 2
- 238000012216 screening Methods 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000003064 k means clustering Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 1
- 238000010894 electron beam technology Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000001259 photo etching Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
-
- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
- G03F7/0002—Lithographic processes using patterning methods other than those involving the exposure to radiation, e.g. by stamping
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Multimedia (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses an image processing method of nanoimprint lithography, which belongs to the technical field of image processing and comprises the following steps: s1, acquiring a working image of a substrate to be etched in the working process of imprint lithography, preprocessing the working image of the substrate to be etched, and generating an image to be processed of the substrate to be etched; s2, determining a fuzzy pixel point set of an image to be processed of the carved substrate; s3, performing brightness processing on the fuzzy pixel point set to finish image processing. According to the invention, the working image of the etched substrate is subjected to pixel point screening, the pixel points with abnormal pixel values, namely the fuzzy pixel point set, are determined, and the elements of the fuzzy pixel point set are subjected to brightness processing, so that the definition of the image is improved, a user can find the abnormality in time when observing the substrate image, and the nanoimprint lithography technology is improved.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an image processing method for nanoimprint lithography.
Background
Imprint lithography is an important process for microelectronic fabrication and has found wide application in the fields of integrated circuit fabrication, optical device fabrication, and the like. The nano-imprinting technology is used as a novel pattern transfer technology, compared with the traditional photoetching technology, the nano-imprinting technology can be used for manufacturing small linewidth patterns, has extremely low manufacturing cost compared with the high-precision electron beam exposure technology, and is suitable for large-scale industrial production. With the development of nanoimprint technology, the production quality of nanoimprint needs to be controlled, and an imprint lithography substrate needs to be monitored, wherein the monitoring is based on the acquisition of a substrate image, so that the quality of the substrate image needs to be improved.
Disclosure of Invention
The invention provides an image processing method for nanoimprint lithography in order to solve the problems.
The technical scheme of the invention is as follows: an image processing method of nanoimprint lithography includes the steps of:
s1, acquiring a working image of a substrate to be etched in the working process of imprint lithography, preprocessing the working image of the substrate to be etched, and generating an image to be processed of the substrate to be etched;
s2, determining a fuzzy pixel point set of an image to be processed of the carved substrate;
s3, performing brightness processing on the fuzzy pixel point set to finish image processing.
Further, in S1, the specific method for preprocessing the working image of the etched substrate is as follows: and denoising and clipping the working image in sequence.
Further, S2 comprises the following sub-steps:
s21, obtaining pixel values of all pixel points in the image to be processed;
s22, calculating pixel gradient change line values of each line of the image to be processed according to the pixel values of each pixel point, and generating a pixel gradient change line sequence;
s23, clustering the pixel gradient change line sequence to obtain contour coefficients of each class;
s24, taking the pixel point with the maximum pixel value in the row with the maximum pixel gradient change row value as the interest pixel point;
s25, calculating fuzzy similarity between other pixel points and interest pixel points in the image to be processed according to the contour coefficient of each class;
s26, taking all the pixel points with the fuzzy similarity smaller than the fuzzy similarity threshold value as a fuzzy pixel point set.
The beneficial effects of the above-mentioned further scheme are: in the invention, the pixel values of adjacent pixel points in each row of the image to be processed are subjected to logarithmic operation, and the pixel gradient change row value of each row is determined by combining parameters such as the pixel difference value of the last pixel point and the first pixel point in each row, so that a pixel gradient change sequence containing a plurality of row values can be obtained. Because the number of lines of the image to be processed is more, the clustering algorithm (for example, K-means clustering) is adopted to perform clustering processing on the pixel gradient change sequence, so that the complexity of a sequence data set can be reduced, fuzzy similarity calculation with the interest pixel point is facilitated, and the condition that the pixel point with larger similarity difference with the interest pixel point possibly has brightness ambiguity exists. The fuzzy similarity threshold is generally 0.5, and can be manually determined according to actual conditions.
Further, in S22, the pixel gradient change line value H of the ith line of the image to be processed i The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in the image to be processed, wherein I represents the number of rows of pixel points in the image to be processed, J represents the number of columns of pixel points in the image to be processed, and G i,j+1 Representing pixel values of (j+1) -th column pixel points of ith row and G in image to be processed i,J Representing pixel values of pixel points in ith row and jth column in image to be processed, G i,1 The pixel value of the pixel point of the ith row and the 1 st column in the image to be processed is represented, and ln (·) represents logarithmic operation.
Further, in S25, the ith row and jth column of pixels in the image to be processed and the interest imageFuzzy similarity V between pixels i,j The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in image to be processed, G 0 Pixel value, H, representing pixel of interest i A pixel gradient change line value H representing the ith line of the image to be processed 0 Representing pixel gradient change row value of row where interesting pixel points are located in image to be processed, B k The profile coefficient corresponding to the kth class is represented, K represents the number of classes in the clustering process, and c represents a constant.
Further, S3 includes the following steps:
s31, constructing a rectangular coordinate system by taking a fuzzy pixel point with the maximum brightness value in the fuzzy pixel point set as an origin, and determining the position coordinates of the rest fuzzy pixel points in the fuzzy pixel point set;
s32, determining a first edge fuzzy pixel point and a second edge fuzzy pixel point in the fuzzy pixel point set according to the position coordinates of the rest fuzzy pixel points;
s33, determining a brightness adjustment threshold according to the brightness value of the first edge blurring pixel point and the brightness value of the second edge blurring pixel point;
s34, according to the brightness adjustment threshold value, brightness processing is carried out on all pixels of the fuzzy pixel point set.
The beneficial effects of the above-mentioned further scheme are: in the invention, a rectangular coordinate system constructed by taking the pixel point with the maximum brightness value as the origin can be used for determining two edge fuzzy pixel points closest and farthest to the origin, and the two edge fuzzy pixel points can be used as two characteristic pixel points to participate in the calculation of a brightness adjustment threshold value (the pixel point farthest/nearest to the pixel point with the maximum brightness value possibly has larger brightness change), so that the determined brightness adjustment threshold value is used for changing the brightness value of a fuzzy pixel point set.
Further, in S32, the specific method for determining the first edge blurred pixel point and the second edge blurred pixel point is as follows: and calculating the linear distance between each other fuzzy pixel point and the original point, taking the fuzzy pixel point with the farthest linear distance from the original point as a first edge fuzzy pixel point, and taking the fuzzy pixel point with the nearest linear distance from the original point as a second edge fuzzy pixel point.
Further, in S33, the calculation formula of the brightness adjustment threshold E is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 An abscissa, v, representing a first edge-blurred pixel point 1 Representing the ordinate of the first edge blurred pixel point, u 2 An abscissa, v, representing the second edge-blurred pixel point 2 Representing the ordinate of the second edge blurred pixel point, F 1 Representing the luminance value of the first edge blurred pixel point, F 2 And represents the luminance value of the second edge blurred pixel point.
Further, in S34, the specific method for performing the brightness processing is: and taking the brightness adjustment threshold value as the latest brightness value of the pixel point where the rectangular coordinate system origin is located, and taking the product of the brightness value of each other blurred pixel point in the blurred pixel point set and the brightness adjustment threshold value as the latest brightness value of each other blurred pixel point.
The beneficial effects of the invention are as follows: according to the invention, the working image of the etched substrate is subjected to pixel point screening, the pixel points with abnormal pixel values, namely the fuzzy pixel point set, are determined, and the elements of the fuzzy pixel point set are subjected to brightness processing, so that the definition of the image is improved, a user can find the abnormality in time when observing the substrate image, and the nanoimprint lithography technology is improved.
Drawings
Fig. 1 is a flow chart of an image processing method of nanoimprint lithography.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an image processing method of nanoimprint lithography, comprising the steps of:
s1, acquiring a working image of a substrate to be etched in the working process of imprint lithography, preprocessing the working image of the substrate to be etched, and generating an image to be processed of the substrate to be etched;
s2, determining a fuzzy pixel point set of an image to be processed of the carved substrate;
s3, performing brightness processing on the fuzzy pixel point set to finish image processing.
In the embodiment of the invention, in S1, the specific method for preprocessing the work image of the carved substrate is as follows: and denoising and clipping the working image in sequence.
In an embodiment of the present invention, S2 comprises the following sub-steps:
s21, obtaining pixel values of all pixel points in the image to be processed;
s22, calculating pixel gradient change line values of each line of the image to be processed according to the pixel values of each pixel point, and generating a pixel gradient change line sequence;
s23, clustering the pixel gradient change line sequence to obtain contour coefficients of each class;
s24, taking the pixel point with the maximum pixel value in the row with the maximum pixel gradient change row value as the interest pixel point;
s25, calculating fuzzy similarity between other pixel points and interest pixel points in the image to be processed according to the contour coefficient of each class;
s26, taking all the pixel points with the fuzzy similarity smaller than the fuzzy similarity threshold value as a fuzzy pixel point set.
In the invention, the pixel values of adjacent pixel points in each row of the image to be processed are subjected to logarithmic operation, and the pixel gradient change row value of each row is determined by combining parameters such as the pixel difference value of the last pixel point and the first pixel point in each row, so that a pixel gradient change sequence containing a plurality of row values can be obtained. Because the number of lines of the image to be processed is more, the clustering algorithm (for example, K-means clustering) is adopted to perform clustering processing on the pixel gradient change sequence, so that the complexity of a sequence data set can be reduced, fuzzy similarity calculation with the interest pixel point is facilitated, and the condition that the pixel point with larger similarity difference with the interest pixel point possibly has brightness ambiguity exists. The fuzzy similarity threshold is generally 0.5, and can be manually determined according to actual conditions.
In the embodiment of the present invention, in S22, the pixel gradient of the i-th line of the image to be processed changes the line value H i The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in the image to be processed, wherein I represents the number of rows of pixel points in the image to be processed, J represents the number of columns of pixel points in the image to be processed, and G i,j+1 Representing pixel values of (j+1) -th column pixel points of ith row and G in image to be processed i,J Representing pixel values of pixel points in ith row and jth column in image to be processed, G i,1 The pixel value of the pixel point of the ith row and the 1 st column in the image to be processed is represented, and ln (·) represents logarithmic operation.
In the embodiment of the present invention, in S25, the fuzzy similarity V between the ith row and jth column pixel points and the interest pixel points in the image to be processed i,j The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in image to be processed, G 0 Pixel value, H, representing pixel of interest i A pixel gradient change line value H representing the ith line of the image to be processed 0 Representing pixel gradient change row value of row where interesting pixel points are located in image to be processed, B k The profile coefficient corresponding to the kth class is represented, K represents the number of classes in the clustering process, and c represents a constant.
In the embodiment of the present invention, S3 includes the following steps:
s31, constructing a rectangular coordinate system by taking a fuzzy pixel point with the maximum brightness value in the fuzzy pixel point set as an origin, and determining the position coordinates of the rest fuzzy pixel points in the fuzzy pixel point set;
s32, determining a first edge fuzzy pixel point and a second edge fuzzy pixel point in the fuzzy pixel point set according to the position coordinates of the rest fuzzy pixel points;
s33, determining a brightness adjustment threshold according to the brightness value of the first edge blurring pixel point and the brightness value of the second edge blurring pixel point;
s34, according to the brightness adjustment threshold value, brightness processing is carried out on all pixels of the fuzzy pixel point set.
In the invention, a rectangular coordinate system constructed by taking the pixel point with the maximum brightness value as the origin can be used for determining two edge fuzzy pixel points closest and farthest to the origin, and the two edge fuzzy pixel points can be used as two characteristic pixel points to participate in the calculation of a brightness adjustment threshold value (the pixel point farthest/nearest to the pixel point with the maximum brightness value possibly has larger brightness change), so that the determined brightness adjustment threshold value is used for changing the brightness value of a fuzzy pixel point set.
In the embodiment of the present invention, in S32, a specific method for determining the first edge blurred pixel point and the second edge blurred pixel point is as follows: and calculating the linear distance between each other fuzzy pixel point and the original point, taking the fuzzy pixel point with the farthest linear distance from the original point as a first edge fuzzy pixel point, and taking the fuzzy pixel point with the nearest linear distance from the original point as a second edge fuzzy pixel point.
In the embodiment of the present invention, in S33, the calculation formula of the brightness adjustment threshold E is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 An abscissa, v, representing a first edge-blurred pixel point 1 Representing the ordinate of the first edge blurred pixel point, u 2 An abscissa, v, representing the second edge-blurred pixel point 2 Representing the ordinate of the second edge blurred pixel point, F 1 Representing the luminance value of the first edge blurred pixel point, F 2 And represents the luminance value of the second edge blurred pixel point.
In the embodiment of the present invention, in S34, the specific method for performing brightness processing is as follows: and taking the brightness adjustment threshold value as the latest brightness value of the pixel point where the rectangular coordinate system origin is located, and taking the product of the brightness value of each other blurred pixel point in the blurred pixel point set and the brightness adjustment threshold value as the latest brightness value of each other blurred pixel point.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (1)
1. An image processing method of nanoimprint lithography, comprising the steps of:
s1, acquiring a working image of a substrate to be etched in the working process of imprint lithography, preprocessing the working image of the substrate to be etched, and generating an image to be processed of the substrate to be etched;
s2, determining a fuzzy pixel point set of an image to be processed of the carved substrate;
s3, performing brightness processing on the fuzzy pixel point set to finish image processing;
in the step S1, the specific method for preprocessing the work image of the carved substrate comprises the following steps: sequentially carrying out denoising treatment and cutting treatment on the working image;
the step S2 comprises the following substeps:
s21, obtaining pixel values of all pixel points in the image to be processed;
s22, calculating pixel gradient change line values of each line of the image to be processed according to the pixel values of each pixel point, and generating a pixel gradient change line sequence;
s23, clustering the pixel gradient change line sequence to obtain contour coefficients of each class;
s24, taking the pixel point with the maximum pixel value in the row with the maximum pixel gradient change row value as the interest pixel point;
s25, calculating fuzzy similarity between other pixel points and interest pixel points in the image to be processed according to the contour coefficient of each class;
s26, taking all pixel points with the fuzzy similarity smaller than a fuzzy similarity threshold value as a fuzzy pixel point set;
in S22, the pixel gradient change line value H of the ith line of the image to be processed i The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in an image to be processed, wherein J represents the column number of the pixel points of the image to be processed and G i,j+1 Representing pixel values of (j+1) -th column pixel points of ith row and G in image to be processed i,J Representing pixel values of pixel points in ith row and jth column in image to be processed, G i,1 Representing pixel values of pixel points in the ith row and the 1 st column in the image to be processed, wherein ln (·) represents logarithmic operation;
in S25, the fuzzy similarity V between the ith row, jth column and interest pixel in the image to be processed i,j The calculation formula of (2) is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein G is i,j Representing pixel values of pixel points in ith row and jth column in image to be processed, G 0 Pixel value, H, representing pixel of interest i A pixel gradient change line value H representing the ith line of the image to be processed 0 Representing pixel gradient change row value of row where interesting pixel points are located in image to be processed, B k Representing contour coefficients corresponding to the kth class, K representing the number of classes of clustering processing, and c representing a constant;
the step S3 comprises the following steps:
s31, constructing a rectangular coordinate system by taking a fuzzy pixel point with the maximum brightness value in the fuzzy pixel point set as an origin, and determining the position coordinates of the rest fuzzy pixel points in the fuzzy pixel point set;
s32, determining a first edge fuzzy pixel point and a second edge fuzzy pixel point in the fuzzy pixel point set according to the position coordinates of the rest fuzzy pixel points;
s33, determining a brightness adjustment threshold according to the brightness value of the first edge blurring pixel point and the brightness value of the second edge blurring pixel point;
s34, performing brightness processing on all pixels of the fuzzy pixel point set according to a brightness adjustment threshold value;
in the step S32, the specific method for determining the first edge blurred pixel point and the second edge blurred pixel point includes: calculating the linear distance between each other fuzzy pixel point and the original point, taking the fuzzy pixel point with the farthest linear distance from the original point as a first edge fuzzy pixel point, and taking the fuzzy pixel point with the nearest linear distance from the original point as a second edge fuzzy pixel point;
in S33, the calculation formula of the brightness adjustment threshold E is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 An abscissa, v, representing a first edge-blurred pixel point 1 Representing the ordinate of the first edge blurred pixel point, u 2 An abscissa, v, representing the second edge-blurred pixel point 2 Representing the ordinate of the second edge blurred pixel point, F 1 Representing the luminance value of the first edge blurred pixel point, F 2 A luminance value representing a second edge blurred pixel point;
in S34, the specific method for performing the brightness processing is as follows: and taking the brightness adjustment threshold value as the latest brightness value of the pixel point where the rectangular coordinate system origin is located, and taking the product of the brightness value of each other blurred pixel point in the blurred pixel point set and the brightness adjustment threshold value as the latest brightness value of each other blurred pixel point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311785282.6A CN117495722B (en) | 2023-12-25 | 2023-12-25 | Image processing method for nanoimprint lithography |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311785282.6A CN117495722B (en) | 2023-12-25 | 2023-12-25 | Image processing method for nanoimprint lithography |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117495722A CN117495722A (en) | 2024-02-02 |
| CN117495722B true CN117495722B (en) | 2024-03-29 |
Family
ID=89683213
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311785282.6A Active CN117495722B (en) | 2023-12-25 | 2023-12-25 | Image processing method for nanoimprint lithography |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117495722B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101193212A (en) * | 2006-11-29 | 2008-06-04 | 明基电通股份有限公司 | Method and device for processing backlight image |
| TW200828977A (en) * | 2006-12-28 | 2008-07-01 | Altek Corp | Brightness adjusting method |
| CN102693535A (en) * | 2011-03-24 | 2012-09-26 | 深圳市蓝韵实业有限公司 | Method for detecting light bundling device area in DR image |
| CN111127337A (en) * | 2019-11-28 | 2020-05-08 | 稿定（厦门）科技有限公司 | Image local area highlight adjusting method, medium, equipment and device |
| CN113689428A (en) * | 2021-10-25 | 2021-11-23 | 江苏南通元辰钢结构制造有限公司 | Mechanical part stress corrosion detection method and system based on image processing |
| CN115147416A (en) * | 2022-09-02 | 2022-10-04 | 山东大山不锈钢制品有限公司 | Rope disorder detection method and device for rope rewinder and computer equipment |
| CN115775215A (en) * | 2022-12-07 | 2023-03-10 | 百度时代网络技术（北京）有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN116958503A (en) * | 2023-09-19 | 2023-10-27 | 广东新泰隆环保集团有限公司 | Image processing-based sludge drying grade identification method and system |
| CN117094909A (en) * | 2023-08-31 | 2023-11-21 | 青岛天仁微纳科技有限责任公司 | Nanometer stamping wafer image acquisition processing method |
| CN117252870A (en) * | 2023-11-15 | 2023-12-19 | 青岛天仁微纳科技有限责任公司 | Image processing method of nano-imprint mold |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5847471B2 (en) * | 2011-07-20 | 2016-01-20 | キヤノン株式会社 | Image processing apparatus, imaging apparatus, image processing method, and image processing program |
| WO2013031807A1 (en) * | 2011-09-02 | 2013-03-07 | シャープ株式会社 | Three-dimensional image generation method, three-dimensional image generation device, and display device comprising same |
| JP6548907B2 (en) * | 2015-02-24 | 2019-07-24 | 三星ディスプレイ株式會社Ｓａｍｓｕｎｇ Ｄｉｓｐｌａｙ Ｃｏ．，Ｌｔｄ． | IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND PROGRAM |
-
2023
- 2023-12-25 CN CN202311785282.6A patent/CN117495722B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101193212A (en) * | 2006-11-29 | 2008-06-04 | 明基电通股份有限公司 | Method and device for processing backlight image |
| TW200828977A (en) * | 2006-12-28 | 2008-07-01 | Altek Corp | Brightness adjusting method |
| CN102693535A (en) * | 2011-03-24 | 2012-09-26 | 深圳市蓝韵实业有限公司 | Method for detecting light bundling device area in DR image |
| CN111127337A (en) * | 2019-11-28 | 2020-05-08 | 稿定（厦门）科技有限公司 | Image local area highlight adjusting method, medium, equipment and device |
| CN113689428A (en) * | 2021-10-25 | 2021-11-23 | 江苏南通元辰钢结构制造有限公司 | Mechanical part stress corrosion detection method and system based on image processing |
| CN115147416A (en) * | 2022-09-02 | 2022-10-04 | 山东大山不锈钢制品有限公司 | Rope disorder detection method and device for rope rewinder and computer equipment |
| CN115775215A (en) * | 2022-12-07 | 2023-03-10 | 百度时代网络技术（北京）有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN117094909A (en) * | 2023-08-31 | 2023-11-21 | 青岛天仁微纳科技有限责任公司 | Nanometer stamping wafer image acquisition processing method |
| CN116958503A (en) * | 2023-09-19 | 2023-10-27 | 广东新泰隆环保集团有限公司 | Image processing-based sludge drying grade identification method and system |
| CN117252870A (en) * | 2023-11-15 | 2023-12-19 | 青岛天仁微纳科技有限责任公司 | Image processing method of nano-imprint mold |
Non-Patent Citations (6)
| Title |
|---|
| A fast local gradient based super-resolution image reconstruction algorithm with fuzzy hyper-bias learning and sparse monitoring paradigm;Soumya Goswami 等;2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS);20150903;399-404 * |
| A Novel Low-Illumination Image Enhancement Method Based on Dual-Channel Prior;Xinyu Zhao 等;2020 Chinese Automation Congress (CAC);20210129;4244-4248 * |
| A two-fold fusion fuzzy framework to restore non-uniform illuminated blurred image;Jyotismita Chaki;Optik;20200331;1-21 * |
| 一种模糊核聚类的线性滤波多光谱图像增强算法;刘雅莉 等;计算机应用研究;20150531;第32卷(第5期);1536-1539 * |
| 基于模糊相似度融合的图像复原算法;刘卫华 等;计算机辅助设计与图形学学报;20130531;第25卷(第5期);616-621 * |
| 基于模糊聚类算法的边缘图像增强技术;王惠平 等;现代电子技术;20171215;第40卷(第24期);103-105 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117495722A (en) | 2024-02-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108171688B (en) | Wafer surface defect detection method based on Gabor characteristics and random dimensionality reduction | |
| CN110674704A (en) | Crowd density estimation method and device based on multi-scale expansion convolutional network | |
| CN114511516B (en) | Micro LED defect detection method based on unsupervised learning | |
| CN111178261B (en) | Face detection acceleration method based on video coding technology | |
| CN116993718B (en) | TFT array substrate defect detection method based on machine vision | |
| CN114723708A (en) | Handicraft appearance defect detection method based on unsupervised image segmentation | |
| CN114049267A (en) | Improved neighborhood search based statistical and bilateral filtering point cloud denoising method | |
| US11144702B2 (en) | Methods and systems for wafer image generation | |
| CN117094909A (en) | Nanometer stamping wafer image acquisition processing method | |
| CN117495722B (en) | Image processing method for nanoimprint lithography | |
| CN117252870B (en) | Image processing method of nano-imprint mold | |
| CN117422717B (en) | Intelligent mask stain positioning method and system | |
| CN114219762A (en) | Defect detection method based on image restoration | |
| CN116778235A (en) | Wafer surface defect classification method based on deep learning network | |
| CN112561949B (en) | Rapid moving object detection algorithm based on RPCA and support vector machine | |
| CN115587991A (en) | Curve mask extraction method, curve mask extraction device and storage medium | |
| Ivanovska et al. | Tomatodiff: On–plant tomato segmentation with denoising diffusion models | |
| CN112764316A (en) | Control equipment and control method of stepping exposure machine | |
| CN116309758B (en) | OpenCV-based line laser image automatic alignment method and terminal equipment | |
| CN111896038B (en) | Semiconductor process data correction method based on correlation entropy and shallow neural network | |
| CN114488719B (en) | OPC method based on three-dimensional feature reinforcement | |
| CN117576568B (en) | Depth robust non-negative matrix factorization method based on incremental learning | |
| CN113409357B (en) | Correlated filtering target tracking method based on double space-time constraints | |
| CN111986153B (en) | Digital image correlation algorithm stability test method | |
| CN113792506B (en) | MOCVD intracavity state identification method based on image processing and machine learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |