Deep Neural Network (DNN) optimized design of 2.45 GHz CMOS rectifier with 73.6% peak efficiency for RF energy harvesting

• Main Authors: Lau, Wendy Wee Yee, Ho, Heng Wah, Siek Liter
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2021
• 主題: Engineering::Electrical and electronic engineering; Energy Harvesting; Rectifiers; RF-DC Converters; Deep Neural Network; Deep Learning; Design Automation; Design Optimization
• 在線閱讀: https://hdl.handle.net/10356/152174
• 機構: Nanyang Technological University

This 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...