CASIA OpenIR  > 毕业生  > 硕士学位论文
集成电路显微图像大面积缺陷修补技术研究
Alternative TitleResearch on IC Image with Large Area of Defect Inpainting Algorithm
胡敏
Subtype工程硕士
Thesis Advisor韩华
2014-05-26
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机技术
Keyword集成电路 图像修补 薄板样条 病态条件问题 图像拼接 Integrated Circuit Image Inpainting Thin Plate Spline Ill-conditioned Problem Image Mosaics
Abstract近年来,集成电路工艺特征尺寸缩小到数十纳米,内部布线层次达到12层,层间间距最小可达30纳米,对于大规模集成电路的逆向分析工程而言,一次性获得所有电路层次的完美表面的样片非常困难,往往需要进行多次多样片处理,互相填补表面污染等缺陷。因此,对于纳米级集成电路分析工程,污染的集成电路图像修补已经成为全芯片显微图像库制作的必不可少的环节。相对于纳米级的精细电路结构来说,数微米甚至数十微米的污染缺陷会覆盖多幅显微图像,常用的基于缺陷区域邻域信息的方法,比如基于偏微分方程的图像修补方法和基于纹理合成的图像修补方法,不适用于较大面积显微图像拼接背景下的图像修补问题。 针对集成电路显微图像缺陷区域的特点,本文分析了大面积图像拼接背景下缺陷区域修补面临的困难,提出了一种基于薄板样条变换的集成电路显微图像大面积缺陷修补方法。该方法通过人工定位污染区域,并利用薄板样条变换对补采图像进行变形来修补图像,有效地增加了算法的适用性。同时针对求解中遇到的病态问题,本文对最小二乘法、正交化方法和Moore-Penrose广义逆方法展开研究,其中Moore-Penrose广义逆方法取得了较好的缝合效果。该方法对目标图像是否存在相似区域没有要求,因此也可以应用于其他没有重复结构的图像处理领域,如大脑切片图像缺陷的修补等等。 本文将上述方法应用于解决大面积拼接背景下的集成电路修补问题,设计了相应的修补工程流程,开发了实用化的显微图像缺陷修补软件工具。该工具已经集成到大规模电镜图像拼接配准软件Mosaic,成功用于多款亿门级集成电路芯片的分析工程中,有效的解决了纳米级集成电路分析过程中的显微图像缺陷修补问题。
Other AbstractFor the past few years, the scale of integrated circuit chip technology feature has been narrowed down to tens of nanometers. Intrinsic wiring hierarchy has reached twelve layers, and the spacing between layers can reach a minimum length of thirty nanometers. It’s a great challenge to acquire chips with perfect surface of all circuit hierarchy once for the large scale integrated circuit reverse analysis. Instead, it usually needs several times of chips processing to compensate the defects mutually, such as surface contamination. So it is necessary to inpaint the spotted IC images for making the whole-chip image library in the nanometer level integrated circuit analysis. Due to the nanometer level sophisticated circuit structure, it is inapplicable to large defect area image inpainting issue with methods based on neighborhood information of the spotted area, such as image inpainting based on partial differential equations or image inpainting based on texture synthesis in the background of microscopic image mosaic. According to the characteristics of defect area in IC chips, we design a large defect area image inpainting approach based on thin-plate spline algorithm, after analyzing the difficulty of large defect area image inpainting issue in the background of image mosaic. Through locating the spotted area by manual method, we warp the additional acquisition images to repair the spotted images with TPS, which greatly increase the applicability of the algorithm. Additionally, several methods are discussed to solve the ill-conditioned problem that we encounter in above disposal, including the Least Squares method, the Regularization method and the Moore-Penrose method, which has draw a conclusion that the Moore-Penrose method can get the best stitching result. Since without the dependence on similar structures in the IC image, the method can apply to other image processing fields, such as spotted brain slices images inpainting. The method proposed in this dissertation has been applied to solve large defect area image inpainting issue in the background of microscopic image mosaic. And corresponding inpainting process has been designed and a practical software tool for defect microscopic image inpainting has been developed, which has been integrated into large scale electron microscope image mosaic software Mosaic and has been applied to several million gate level integrated circuit chips analysis project successfully and has solved the defect microscopi...
shelfnumXWLW2071
Other Identifier2011e8014661094
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7722
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
胡敏. 集成电路显微图像大面积缺陷修补技术研究[D]. 中国科学院自动化研究所. 中国科学院大学,2014.
Files in This Item:
File Name/Size DocType Version Access License
CASIA_2011e801466109(5847KB) 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[胡敏]'s Articles
Baidu academic
Similar articles in Baidu academic
[胡敏]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[胡敏]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.