Pub Date : 2023-01-22DOI: 10.1109/RWS55624.2023.10046341
Mengfu Di, Weiquan Hao, Xunyu Li, Zijin Pan, Runyu Miao, Albert Z. Wang
This paper presents co-design and analysis of a 38GHz traveling wave single-pole double-through (SPDT) RF switch with robust ESD protection implemented in 45nm PDSOI technology 5G systems in millimeter-wave bands. Substrate coupling is minimized by a buried oxide (BOX) layer. The original switches exhibit insertion loss (IL) of −1.4dB and isolation (Iso) of ~59dB at 38GHz. The design goal of robust charged device model (CDM) ESD protection was realized, achieving ~5A in measurement. Human body model (HBM) ESD of ~2KV was also achieved. The ESD-protected SPDT achieves IL of −3.49dB and Iso of −49dB at 38GHz. Design analysis reveals significant impact of ESD protection on SPDT performance, which was minimized through careful co-design effort, improving insertion loss by ~0.8dB at 38GHz.
{"title":"A 38GHz SPDT Traveling Wave Switch with 5A CDM ESD Protection in 45nm PDSOI for 5G System","authors":"Mengfu Di, Weiquan Hao, Xunyu Li, Zijin Pan, Runyu Miao, Albert Z. Wang","doi":"10.1109/RWS55624.2023.10046341","DOIUrl":"https://doi.org/10.1109/RWS55624.2023.10046341","url":null,"abstract":"This paper presents co-design and analysis of a 38GHz traveling wave single-pole double-through (SPDT) RF switch with robust ESD protection implemented in 45nm PDSOI technology 5G systems in millimeter-wave bands. Substrate coupling is minimized by a buried oxide (BOX) layer. The original switches exhibit insertion loss (IL) of −1.4dB and isolation (Iso) of ~59dB at 38GHz. The design goal of robust charged device model (CDM) ESD protection was realized, achieving ~5A in measurement. Human body model (HBM) ESD of ~2KV was also achieved. The ESD-protected SPDT achieves IL of −3.49dB and Iso of −49dB at 38GHz. Design analysis reveals significant impact of ESD protection on SPDT performance, which was minimized through careful co-design effort, improving insertion loss by ~0.8dB at 38GHz.","PeriodicalId":110742,"journal":{"name":"2023 IEEE Radio and Wireless Symposium (RWS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121072648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-22DOI: 10.1109/RWS55624.2023.10046325
Thomas Stadelmayer, Lorenzo Servadei, Avik Santra, R. Weigel, F. Lurz
The paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.
{"title":"Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning","authors":"Thomas Stadelmayer, Lorenzo Servadei, Avik Santra, R. Weigel, F. Lurz","doi":"10.1109/RWS55624.2023.10046325","DOIUrl":"https://doi.org/10.1109/RWS55624.2023.10046325","url":null,"abstract":"The paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.","PeriodicalId":110742,"journal":{"name":"2023 IEEE Radio and Wireless Symposium (RWS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126515986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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