{"title":"基于图像的芯片导航","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":"{\"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}","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}
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.