{"title":"基于设备质量控制数据的虚拟机图像传感器缺陷数预测","authors":"Toshiya Okazaki, Kosuke Okusa, K. Yoshida","doi":"10.1109/issm.2018.8651135","DOIUrl":null,"url":null,"abstract":"This paper describes methods and evaluation results of predicting the number of defects in image sensors. We used regression tree and stepwise AIC for variable selection and generalized linear model for regression, instead of partial least squares (PLS) regression. The results showed improvement in prediction performance in comparison with the conventional method. By this, we could predict other countable values such as defects or dust particles.","PeriodicalId":262428,"journal":{"name":"2018 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Prediction of the Number of Defects in Image Sensors by VM using Equipment QC Data\",\"authors\":\"Toshiya Okazaki, Kosuke Okusa, K. Yoshida\",\"doi\":\"10.1109/issm.2018.8651135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes methods and evaluation results of predicting the number of defects in image sensors. We used regression tree and stepwise AIC for variable selection and generalized linear model for regression, instead of partial least squares (PLS) regression. The results showed improvement in prediction performance in comparison with the conventional method. By this, we could predict other countable values such as defects or dust particles.\",\"PeriodicalId\":262428,\"journal\":{\"name\":\"2018 International Symposium on Semiconductor Manufacturing (ISSM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Symposium on Semiconductor Manufacturing (ISSM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/issm.2018.8651135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Symposium on Semiconductor Manufacturing (ISSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/issm.2018.8651135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of the Number of Defects in Image Sensors by VM using Equipment QC Data
This paper describes methods and evaluation results of predicting the number of defects in image sensors. We used regression tree and stepwise AIC for variable selection and generalized linear model for regression, instead of partial least squares (PLS) regression. The results showed improvement in prediction performance in comparison with the conventional method. By this, we could predict other countable values such as defects or dust particles.