{"title":"全局多壁碳纳米管互连中继器优化方法","authors":"Peng‐Wei Liu, Wensheng Zhao, Gaofeng Wang","doi":"10.1109/USNC-URSI.2019.8861712","DOIUrl":null,"url":null,"abstract":"In this paper, the optimal repeater number and size are analyzed for multi-walled carbon nanotube interconnects by using the particle swarm optimization (PSO) algorithm. Genetic algorithm (GA) is also used to verify the corresponding results. Further, the neural network (NN) is trained to facilitate the EDA process. It is found that the computational time can be dramatically reduced with the implementation of NN.","PeriodicalId":383603,"journal":{"name":"2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Repeater Optimization Methodology for Global Multi-Walled Carbon Nanotube Interconnects\",\"authors\":\"Peng‐Wei Liu, Wensheng Zhao, Gaofeng Wang\",\"doi\":\"10.1109/USNC-URSI.2019.8861712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the optimal repeater number and size are analyzed for multi-walled carbon nanotube interconnects by using the particle swarm optimization (PSO) algorithm. Genetic algorithm (GA) is also used to verify the corresponding results. Further, the neural network (NN) is trained to facilitate the EDA process. It is found that the computational time can be dramatically reduced with the implementation of NN.\",\"PeriodicalId\":383603,\"journal\":{\"name\":\"2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/USNC-URSI.2019.8861712\",\"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 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/USNC-URSI.2019.8861712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Repeater Optimization Methodology for Global Multi-Walled Carbon Nanotube Interconnects
In this paper, the optimal repeater number and size are analyzed for multi-walled carbon nanotube interconnects by using the particle swarm optimization (PSO) algorithm. Genetic algorithm (GA) is also used to verify the corresponding results. Further, the neural network (NN) is trained to facilitate the EDA process. It is found that the computational time can be dramatically reduced with the implementation of NN.
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