{"title":"基于二元分类算法和蒙特卡罗模拟的半导体制造操作风险研究","authors":"D. Patnaik, S. R., D. Suresh","doi":"10.1109/irtm54583.2022.9791608","DOIUrl":null,"url":null,"abstract":"The manufacturing processes involved in the fabrication of semiconductor devices are very prone to error due to its extremely intricate nature. There are several hundred processes and the process of detection of a defect is extremely capital and time consuming. In this paper, we aim to analyze the fabrication process and analyze manufacturing machine data in order to determine the average probability of excursion and the loss associated with these excursions using binary classification prediction algorithms and Monte Carlo simulations.","PeriodicalId":426354,"journal":{"name":"2022 Interdisciplinary Research in Technology and Management (IRTM)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Operational Risk in Semiconductor Fabrication Using Binary Classification Algorithms and Monte Carlo Simulation, a Systemic Review\",\"authors\":\"D. Patnaik, S. R., D. Suresh\",\"doi\":\"10.1109/irtm54583.2022.9791608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The manufacturing processes involved in the fabrication of semiconductor devices are very prone to error due to its extremely intricate nature. There are several hundred processes and the process of detection of a defect is extremely capital and time consuming. In this paper, we aim to analyze the fabrication process and analyze manufacturing machine data in order to determine the average probability of excursion and the loss associated with these excursions using binary classification prediction algorithms and Monte Carlo simulations.\",\"PeriodicalId\":426354,\"journal\":{\"name\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"volume\":\"169 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/irtm54583.2022.9791608\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Interdisciplinary Research in Technology and Management (IRTM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/irtm54583.2022.9791608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Operational Risk in Semiconductor Fabrication Using Binary Classification Algorithms and Monte Carlo Simulation, a Systemic Review
The manufacturing processes involved in the fabrication of semiconductor devices are very prone to error due to its extremely intricate nature. There are several hundred processes and the process of detection of a defect is extremely capital and time consuming. In this paper, we aim to analyze the fabrication process and analyze manufacturing machine data in order to determine the average probability of excursion and the loss associated with these excursions using binary classification prediction algorithms and Monte Carlo simulations.