장소: 판교 테크노밸리 산업수학혁신센터 세미나실
발표자: 이창옥 교수(한국과학기술원)
Most defect inspection methods used in semiconductor manufacturing require design layout or golden die images. Unlike methods that require such additional information, this talk presents a method for automatic inspection of defects in semiconductor images with a single image. First, we devise a method to classify images into four types: flat, linear, patterned, and complex using a cosine similarity. For linear and patterned images, we obtain defect-free images that retain the structure. Then, we subtract defect-free image from input image to get a flat image. The Fast-MCD method then estimates the parameters of the inlier distribution of the flat image and uses them to detect defects. A segmentation neural network is used to detect defects in complex images.
** 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청 페이지에 업데이트 하겠습니다.
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
발표자: 이창옥 교수(한국과학기술원)
Most defect inspection methods used in semiconductor manufacturing require design layout or golden die images. Unlike methods that require such additional information, this talk presents a method for automatic inspection of defects in semiconductor images with a single image. First, we devise a method to classify images into four types: flat, linear, patterned, and complex using a cosine similarity. For linear and patterned images, we obtain defect-free images that retain the structure. Then, we subtract defect-free image from input image to get a flat image. The Fast-MCD method then estimates the parameters of the inlier distribution of the flat image and uses them to detect defects. A segmentation neural network is used to detect defects in complex images.
** 유튜브 실시간 스트리밍 : 현장 참석이 어려운 분들을 위해 온라인으로 실시간 방송할 예정입니다. 주소는 당일 신청 페이지에 업데이트 하겠습니다.