A golden template self-generating method for patterned wafer inspection

This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented

A golden block based self-refining scheme for repetitive patterned wafer inspections

This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. It has a learning ability that is able to create a golden block database from the wafer image itself, modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrives, we do not have to re-calculate its periods and building block. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. And the storage overhead of golden templates is also reduced significantly by storing golden blocks only

반도체 제조 공정을 위한 GAN 기반 이종 이미지 정렬 체계

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 김도년.In semiconductor manufacturing process, visual inspection on wafer using template-based detection is widely researched topic. As a prerequisite of detection process, new demand for aligning multimodal image has emerged. To address this issue, this paper proposes a framework with GAN based image translation followed by NCC based template matching algorithm. Different from previous function based approaches, our deep learning based framework effectively transforms an image to another domain where template matching is much easier. Also, for practical usage, we propose a new training data generation strategy which allows our model to train from only 20 pre-aligned images. Experimental results on 4 types of manually aligned data, consisted of 400 pairs of images, demonstrate that our method successfully transforms image regardless of the presence of defect or noise. Also, using transformed image, alignment process with NCC based template matching achieved almost 100% accuracy on every types of image. Moreover, our framework shows great efficiency as it takes only 15 minutes for training and 0.25 seconds per image in test time.반도체 공정에서 템플릿을 이용한 비전 기반의 웨이퍼 검사는 널리 연구되는 분야이다. 이러한 검사 과정의 전제 조건으로 멀티모달 이미지 정렬에 대한 새로운 요구가 대두되었다. 이 문제를 해결하기 위해 본 논문은 GAN을 활용한 이미지 변환과 NCC 기반의 템플릿 정렬 알고리즘을 이용한 프레임워크를 제안한다. 이전의 함수 기반 접근법과 달리 딥러닝 기반 프레임워크는 이미지를 템플릿 정렬이 훨씬 용이한 도메인으로 효과적으로 변환한다. 또한 실용적인 관점에서 고안한 새로운 학습 데이터 생성 방법을 통해 오직 20개의 정렬된 초기 데이터를 통해서 딥러닝 모델을 성공적으로 학습할 수 있다. 각각 100쌍의 이미지로 이루어진 4가지 종류의 수작업으로 정렬한 데이터를 사용한 실험 결과를 통해 고안한 방법이 결함이나 노이즈의 존재여부와 상관없이 효과적으로 이미지를 변환한다는 것을 확인할 수 있다. 또한 변환된 이미지를 사용한 NCC 기반의 템플릿 정렬 알고리즘은 이미지 정렬에서 100%에 가까운 정확도를 보인다. 마지막으로 소요 시간에서 프레임워크는 학습에 15분, 테스트 시 이미지당 0.25 초 만을 소모하며 높은 효율을 보인다.1. Introduction 1 2. Proposed Framework 5 2.1 Training image generation and image preprocessing 6 2.2 GAN based image translation and template matching 9 3. Experimental Results 13 3.1 Performance of image generation 14 3.2 Accuracy of template matching 22 3.3 Running time of framework 24 4. Conclusion 26 References 28 Abstract in Korean 31Maste

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet