{"title":"三端RRAM的蒙特卡罗模拟及其在神经形态计算中的应用","authors":"Akhilesh Balasingam, Akash Levy, Haitong Li, Priyanka Raina","doi":"10.23919/SISPAD49475.2020.9241659","DOIUrl":null,"url":null,"abstract":"We developed a Monte Carlo simulator to compute the state-dependent I-V characteristics of three-terminal (3T) RRAM devices. State switching in these devices is modeled using a combination of vacancy migration and trap-assisted-tunneling mechanisms. We describe key elements of the simulator, compute hysteresis curves under typical voltage cycling conditions, and demonstrate agreement with experimental results. We then study the response of 2T- and 3T-RRAMs under pulsed operation and show that 3T-RRAM conductance values have both greater dynamic range than 2T-RRAMs and the potential to deliver superior inference accuracy in neuromorphic applications.","PeriodicalId":206964,"journal":{"name":"2020 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monte Carlo Simulation of a Three-Terminal RRAM with Applications to Neuromorphic Computing\",\"authors\":\"Akhilesh Balasingam, Akash Levy, Haitong Li, Priyanka Raina\",\"doi\":\"10.23919/SISPAD49475.2020.9241659\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We developed a Monte Carlo simulator to compute the state-dependent I-V characteristics of three-terminal (3T) RRAM devices. State switching in these devices is modeled using a combination of vacancy migration and trap-assisted-tunneling mechanisms. We describe key elements of the simulator, compute hysteresis curves under typical voltage cycling conditions, and demonstrate agreement with experimental results. We then study the response of 2T- and 3T-RRAMs under pulsed operation and show that 3T-RRAM conductance values have both greater dynamic range than 2T-RRAMs and the potential to deliver superior inference accuracy in neuromorphic applications.\",\"PeriodicalId\":206964,\"journal\":{\"name\":\"2020 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)\",\"volume\":\"136 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SISPAD49475.2020.9241659\",\"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 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SISPAD49475.2020.9241659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monte Carlo Simulation of a Three-Terminal RRAM with Applications to Neuromorphic Computing
We developed a Monte Carlo simulator to compute the state-dependent I-V characteristics of three-terminal (3T) RRAM devices. State switching in these devices is modeled using a combination of vacancy migration and trap-assisted-tunneling mechanisms. We describe key elements of the simulator, compute hysteresis curves under typical voltage cycling conditions, and demonstrate agreement with experimental results. We then study the response of 2T- and 3T-RRAMs under pulsed operation and show that 3T-RRAM conductance values have both greater dynamic range than 2T-RRAMs and the potential to deliver superior inference accuracy in neuromorphic applications.