{"title":"单片三维成像系统:碳纳米管计算电路直接集成在硅成像仪上","authors":"T. Srimani, G. Hills, C. Lau, M. Shulaker","doi":"10.23919/VLSIT.2019.8776514","DOIUrl":null,"url":null,"abstract":"Here we show a hardware prototype of a monolithic three-dimensional (3D) imaging system that integrates computing layers directly in the back-end-of-line (BEOL) of a conventional silicon imager. Such systems can transform imager output from raw pixel data to highly processed information. To realize our imager, we fabricate 3 vertical circuit layers directly on top of each other: a bottom layer of silicon pixels followed by two layers of CMOS carbon nanotube FETs (CNFETs) (comprising 2,784 CNFETs) that perform in-situ edge detection in real-time, before storing data in memory. This approach promises to enable image classification systems with improved nrocessing latencies.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"33 1","pages":"T24-T25"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Monolithic Three-Dimensional Imaging System: Carbon Nanotube Computing Circuitry Integrated Directly Over Silicon Imager\",\"authors\":\"T. Srimani, G. Hills, C. Lau, M. Shulaker\",\"doi\":\"10.23919/VLSIT.2019.8776514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we show a hardware prototype of a monolithic three-dimensional (3D) imaging system that integrates computing layers directly in the back-end-of-line (BEOL) of a conventional silicon imager. Such systems can transform imager output from raw pixel data to highly processed information. To realize our imager, we fabricate 3 vertical circuit layers directly on top of each other: a bottom layer of silicon pixels followed by two layers of CMOS carbon nanotube FETs (CNFETs) (comprising 2,784 CNFETs) that perform in-situ edge detection in real-time, before storing data in memory. This approach promises to enable image classification systems with improved nrocessing latencies.\",\"PeriodicalId\":6752,\"journal\":{\"name\":\"2019 Symposium on VLSI Technology\",\"volume\":\"33 1\",\"pages\":\"T24-T25\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSIT.2019.8776514\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIT.2019.8776514","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Here we show a hardware prototype of a monolithic three-dimensional (3D) imaging system that integrates computing layers directly in the back-end-of-line (BEOL) of a conventional silicon imager. Such systems can transform imager output from raw pixel data to highly processed information. To realize our imager, we fabricate 3 vertical circuit layers directly on top of each other: a bottom layer of silicon pixels followed by two layers of CMOS carbon nanotube FETs (CNFETs) (comprising 2,784 CNFETs) that perform in-situ edge detection in real-time, before storing data in memory. This approach promises to enable image classification systems with improved nrocessing latencies.