Intelligent mm-Wave Front-Ends: Bridging High-Resolution Radar, Wireless Communications, and Biomedical Sensing

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Technical seminar by Dr. Yanjie (Jay) Wang, Chair Professor and Associate Dean of the School of Microelectronics at the South China University of Technology (SCUT) with the following abstract:

This talk presents a wideband millimeter-wave (mm-wave) phased-array transceiver (TRX) front-end in CMOS designed for high-resolution radar sensing and wireless communications. We explore a TRX architecture featuring high-resolution, calibration-free phase shifters integrated with PAs, paired with a noise-canceling LNA receiver to maximize system dynamic range. To minimize silicon footprint and facilitate compact phased-array scaling, a reconfigurable bidirectional PA/LNA topology is presented. A key innovation includes the use of AI-driven passive component matching networks, employing machine learning to optimize complex impedance transformations across wide bandwidths. Furthermore, the talk extends these high-efficiency design principles to the healthcare sector, discussing our recent research on a low-power, wirelessly powered multi-band RF antenna front-end tailored for non-invasive biomedical sensing.

 


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  • 2356 Main Mall
  • Vancouver, British Columbia
  • Canada V6T 1Z4
  • Building: MacLeod Building
  • Room Number: MCLD 3038
  • Click here for Map
  • Contact Event Host

  • sudip@ece.ubc.ca


  Speakers

Yanjie (Jay) Wang of Eastern Institute of Technology

Topic:

Intelligent mm-Wave Front-Ends: Bridging High-Resolution Radar, Wireless Communications, and Biomedical Sensing

Technical seminar with the following abstract:

This talk presents a wideband millimeter-wave (mm-wave) phased-array transceiver (TRX) front-end in CMOS designed for high-resolution radar sensing and wireless communications. We explore a TRX architecture featuring high-resolution, calibration-free phase shifters integrated with PAs, paired with a noise-canceling LNA receiver to maximize system dynamic range. To minimize silicon footprint and facilitate compact phased-array scaling, a reconfigurable bidirectional PA/LNA topology is presented. A key innovation includes the use of AI-driven passive component matching networks, employing machine learning to optimize complex impedance transformations across wide bandwidths. Furthermore, the talk extends these high-efficiency design principles to the healthcare sector, discussing our recent research on a low-power, wirelessly powered multi-band RF antenna front-end tailored for non-invasive biomedical sensing.

 

Biography:

Yanjie (Jay) Wang is a Chair Professor and Associate Dean of the School of Microelectronics at the South China University of Technology (SCUT). Prior to joining SCUT in 2019, he served as a Senior Staff Scientist at Intel Labs in Hillsboro, Oregon since 2008. His research focuses on high-performance millimeter-wave (mmW) and RF CMOS integrated circuits, AI-assisted RF design for radar sensing and wireless communications, and wirelessly powered biomedical systems.
Prof. Wang serves on the Technical Program Committees (TPC) for the IEEE RFIC and CICC and is an active member of the IEEE Solid-State Circuits Society. He has authored over 100 journal and conference articles and holds more than 25 U.S. and Chinese patents. His work has received numerous awards, including the ISSCC 2020 Lewis Winner Award, the CICC 2017 Best Paper Award for 2nd place student and invited papers, and most recently, a top-6 student paper finalist at IEEE RFIC 2025.

 

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