C. Edwards, M L N Swamy, Ravi Garg, Tim Karaniuk, C. Morgan, Debashis Panda
{"title":"Real-Time Tool Health Monitoring and Defect Inspection during Epoxy Dispense Process","authors":"C. Edwards, M L N Swamy, Ravi Garg, Tim Karaniuk, C. Morgan, Debashis Panda","doi":"10.1109/ASMC49169.2020.9185239","DOIUrl":null,"url":null,"abstract":"We demonstrate a new real-time inspection system developed to monitor tool health and detect defects during the epoxy dispense process. The system includes both hardware and software components. The hardware was designed to be low-cost and fit into a small footprint within the existing tools. Our software contains a tool setup/calibration utility and a user interface for recipe creation and real-time inspection. The software also provides extensive logging of key results including tabulated data, annotated images, and live inspection results on the user interface. The algorithm uses a mixture of advanced machine learning and computer vision algorithms to identify unwanted process variation. The new system has provided excellent results, an order of magnitude below the qualification targets, while ensuring the throughput time targets are not impacted.","PeriodicalId":6771,"journal":{"name":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"120 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASMC49169.2020.9185239","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We demonstrate a new real-time inspection system developed to monitor tool health and detect defects during the epoxy dispense process. The system includes both hardware and software components. The hardware was designed to be low-cost and fit into a small footprint within the existing tools. Our software contains a tool setup/calibration utility and a user interface for recipe creation and real-time inspection. The software also provides extensive logging of key results including tabulated data, annotated images, and live inspection results on the user interface. The algorithm uses a mixture of advanced machine learning and computer vision algorithms to identify unwanted process variation. The new system has provided excellent results, an order of magnitude below the qualification targets, while ensuring the throughput time targets are not impacted.