{"title":"Image-based navigation on a chip","authors":"M. Lifshits, E. Rivlin, M. Rudzsky","doi":"10.1109/IMTC.2004.1351098","DOIUrl":null,"url":null,"abstract":"In semiconductor industry, where highest levels of precision and robustness are required, machine vision tools evolved to become a mainstream automation tools that guide robotic handling, assembly and inspection processes. This paper presents an algorithm for navigation on a chip that is based on localization of microscopic eye-point images using a previously acquired wafer map. It is fast enough for in-line microscopy and robust to visual changes occurring during the manufacturing process, such as contrast variations, re-scaling, rotation and partial feature obliteration. The algorithm uses geometric hashing, a highly efficient technique drawn from the object recognition field. Experimental results indicate high reliability of the algorithm.","PeriodicalId":386903,"journal":{"name":"Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMTC.2004.1351098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In semiconductor industry, where highest levels of precision and robustness are required, machine vision tools evolved to become a mainstream automation tools that guide robotic handling, assembly and inspection processes. This paper presents an algorithm for navigation on a chip that is based on localization of microscopic eye-point images using a previously acquired wafer map. It is fast enough for in-line microscopy and robust to visual changes occurring during the manufacturing process, such as contrast variations, re-scaling, rotation and partial feature obliteration. The algorithm uses geometric hashing, a highly efficient technique drawn from the object recognition field. Experimental results indicate high reliability of the algorithm.