{"title":"基于人工神经网络的实时调度决策算法","authors":"Shiladitya Chakravorty, N. Nagarur","doi":"10.1109/ASMC49169.2020.9185213","DOIUrl":null,"url":null,"abstract":"In semiconductor manufacturing fabs, presence of queue time restricted zones within manufacturing routes present some unique challenges for fab dispatching and scheduling systems. In this study we discuss some of these challenges and present a cycle time prediction methodology based on backpropagation trained artificial neural network which can be used for making real time dispatching decisions at trigger steps of queue time restricted zones.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"18 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Artificial Neural Network Based Algorithm For Real Time Dispatching Decisions\",\"authors\":\"Shiladitya Chakravorty, N. Nagarur\",\"doi\":\"10.1109/ASMC49169.2020.9185213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In semiconductor manufacturing fabs, presence of queue time restricted zones within manufacturing routes present some unique challenges for fab dispatching and scheduling systems. In this study we discuss some of these challenges and present a cycle time prediction methodology based on backpropagation trained artificial neural network which can be used for making real time dispatching decisions at trigger steps of queue time restricted zones.\",\"PeriodicalId\":6771,\"journal\":{\"name\":\"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"volume\":\"18 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASMC49169.2020.9185213\",\"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 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASMC49169.2020.9185213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Artificial Neural Network Based Algorithm For Real Time Dispatching Decisions
In semiconductor manufacturing fabs, presence of queue time restricted zones within manufacturing routes present some unique challenges for fab dispatching and scheduling systems. In this study we discuss some of these challenges and present a cycle time prediction methodology based on backpropagation trained artificial neural network which can be used for making real time dispatching decisions at trigger steps of queue time restricted zones.
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