{"title":"蓝宝石生长中的质量控制:从自动化缺陷检测到大数据方法","authors":"I. Orlov, Frédéric Falise","doi":"10.1109/CSTIC49141.2020.9282471","DOIUrl":null,"url":null,"abstract":"We illustrate how automated scanners visualise internal defects in raw sapphire prior to its processing, and present some defect statistics that Scientific Visual has collected over five years of serving key sapphire suppliers in Europe and Asia. The article illustrates use of defect location and morphology data to reveal trends in sapphire quality, compare production modes, and to find out the optimal parameters for sapphire growth.","PeriodicalId":6848,"journal":{"name":"2020 China Semiconductor Technology International Conference (CSTIC)","volume":"107 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Control in Sapphire Growing: From Automated Defect Detection to Big Data Approach\",\"authors\":\"I. Orlov, Frédéric Falise\",\"doi\":\"10.1109/CSTIC49141.2020.9282471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We illustrate how automated scanners visualise internal defects in raw sapphire prior to its processing, and present some defect statistics that Scientific Visual has collected over five years of serving key sapphire suppliers in Europe and Asia. The article illustrates use of defect location and morphology data to reveal trends in sapphire quality, compare production modes, and to find out the optimal parameters for sapphire growth.\",\"PeriodicalId\":6848,\"journal\":{\"name\":\"2020 China Semiconductor Technology International Conference (CSTIC)\",\"volume\":\"107 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 China Semiconductor Technology International Conference (CSTIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSTIC49141.2020.9282471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 China Semiconductor Technology International Conference (CSTIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSTIC49141.2020.9282471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality Control in Sapphire Growing: From Automated Defect Detection to Big Data Approach
We illustrate how automated scanners visualise internal defects in raw sapphire prior to its processing, and present some defect statistics that Scientific Visual has collected over five years of serving key sapphire suppliers in Europe and Asia. The article illustrates use of defect location and morphology data to reveal trends in sapphire quality, compare production modes, and to find out the optimal parameters for sapphire growth.
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