Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152104
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dc.contributor.authorLau, Wendy Wee Yeeen_US
dc.contributor.authorHo, Heng Wahen_US
dc.contributor.authorSiek, Literen_US
dc.date.accessioned2021-08-26T08:43:57Z-
dc.date.available2021-08-26T08:43:57Z-
dc.date.issued2020-
dc.identifier.citationLau, W. W. Y., Ho, H. W. & Siek, L. (2020). Deep neural network (DNN) optimized design of 2.45 GHz CMOS rectifier with 73.6% peak efficiency for RF energy harvesting. IEEE Transactions On Circuits and Systems I: Regular Papers, 67(12), 4322-4333. https://dx.doi.org/10.1109/TCSI.2020.3022280en_US
dc.identifier.issn1549-8328en_US
dc.identifier.urihttps://hdl.handle.net/10356/152104-
dc.description.abstractThis article presents a two-stage rectifier with novel DC-boosted gate bias for RF energy harvesting. The auxiliary gate bias enables rectifier to operate when input amplitude is smaller than its transistor threshold voltage while constraining the positive gate voltage during off state to reduce the reverse leakage current. An automated design optimization methodology using Deep Neural Network (DNN) to maximize efficiency is presented. The DNN is shown to accurately model SPICE simulated response of rectifier. Hence, the design phase turnaround time is minimized with fast prediction of optimized design parameters. The proposed rectifier has been fabricated in 65 nm standard CMOS technology. A maximum power conversion efficiency of 73.6% is measured at 2.45 GHz with input power of -6 dBm. The proposed rectifier has a measured sensitivity of -12 dBm for 1 V output voltage.en_US
dc.description.sponsorshipEconomic Development Board (EDB)en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Circuits and Systems I: Regular Papersen_US
dc.rights© 2020 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineering::Electric poweren_US
dc.titleDeep neural network (DNN) optimized design of 2.45 GHz CMOS rectifier with 73.6% peak efficiency for RF energy harvestingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.organizationGlobal Foundriesen_US
dc.contributor.researchVIRTUS, IC Design Centre of Excellenceen_US
dc.identifier.doi10.1109/TCSI.2020.3022280-
dc.identifier.issue12en_US
dc.identifier.volume67en_US
dc.identifier.spage4322en_US
dc.identifier.epage4333en_US
dc.subject.keywordsEnergy Harvestingen_US
dc.subject.keywordsRectifiersen_US
dc.subject.keywordsRF-DC Convertersen_US
dc.subject.keywordsDeep Neural Networken_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsDesign Automationen_US
dc.subject.keywordsDesign Optimizationen_US
dc.description.acknowledgementThis work was supported in part by the Singapore Economic Development Board (EDB) and in part by the GlobalFoundries Singapore Pte. Ltden_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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