{"title":"Secure and Reliable Power Monitoring for Low Consumption Factory Equipment via Programmable IoT Devices","authors":"Sergio Garnica, R. Wieland","doi":"10.1109/ISSM55802.2022.10026914","DOIUrl":null,"url":null,"abstract":"This paper reports on the implementation of low cost Internet-of-Things enabled power sockets, deployed on the Fraunhofer institute for Microsystems and Solid State Technologies clean room on one of the inspection microscopes used on the CMOS compatible line. The devices were flashed with open source software to ensure local, secure and reliable control without the necesity of an external cloud provider. The architecture of the physical deployment is shown and experimental data is analysed in order to obtain insight into the usage and statistics behind a previously unknown station in the clean room.","PeriodicalId":130513,"journal":{"name":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Symposium on Semiconductor Manufacturing (ISSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSM55802.2022.10026914","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
This paper reports on the implementation of low cost Internet-of-Things enabled power sockets, deployed on the Fraunhofer institute for Microsystems and Solid State Technologies clean room on one of the inspection microscopes used on the CMOS compatible line. The devices were flashed with open source software to ensure local, secure and reliable control without the necesity of an external cloud provider. The architecture of the physical deployment is shown and experimental data is analysed in order to obtain insight into the usage and statistics behind a previously unknown station in the clean room.