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Zheng Liu

Zheng Liu

Assistant Professor

Zheng Liu received his B.S. in Electronics from Peking University, his M.S. in Electrical Engineering from UCLA, and his Ph.D. in Electrical and Computer Engineering from Princeton University. He previously served as a Senior Design Engineer at Skyworks Solutions, where his RF power amplifier and front-end module designs enabled the shipment of over 40 million units in commercial wireless devices. He later collaborated with Apple on advanced mmWave beamforming ICs and conducted research at Texas Instruments’ Kilby Labs, developing GaN power amplifier modules for emerging 6G wireless systems.

Dr. Liu’s research interests span AI-enabled wireless chip design, ultra-broadband phased-array systems, and RF/mmWave/sub-THz circuits using advanced semiconductor technologies. His work integrates data- and physics-informed optimization for on-chip electromagnetics and high-speed devices, the development of universal phased-array architectures for joint communication–sensing, and the design of high-performance MMIC/RFIC solutions leveraging beyond-silicon platforms such as GaN and InP.

His contributions to microwave engineering and solid-state circuits have earned several prestigious honors, including the IEEE Journal of Solid-State Circuits Best Paper Award (2023), the IMS Advanced Practice Paper Award (2022), the IEEE MTT-S Microwave Fellowship (2021), and the Bede Liu Best Ph.D. Dissertation Award from Princeton University (2023). He also received three IEEE IMS Best Student Paper Awards.
Dr. Liu is a member of the IEEE MTT-S Technical Committee on Microwave and mmWave Integrated Circuits (TC-14), serves on the Technical Program Committee for IEEE IMS 2025 and IMS 2026, and reviews regularly for premier journals such as IEEE JSSC, TMTT, and TCAS-I.

Department

Electrical & Computer Engineering

Contact

Engineering Hall
1415 Engineering Dr
Madison, WI

Featured news

Focus on new faculty: Zheng Liu is using AI to give wireless chips a boost

November 24, 2025

  • BA , Peking University
  • BS , Peking University
  • MS , University of California, Los Angeles
  • PhD , Princeton University

  • RF/mmWave/THz integrated circuits and chip-scale system
  • AI/ML-enabled wireless chip design
  • Beyond-silicon MMIC/RFIC and heterogeneous integration
  • Joint communication and sensing

  • 2025 IEEE Solid-State Circuits Society, 2023 JSSC Best Paper Award
  • 2023 Princeton University, Bede Liu Best Ph.D Dissertation Award
  • 2022 IEEE IMS 2022, Advanced Practice Paper Award
  • 2022 Qualcomm, Inc, Qualcomm Innovation Fellowship Finalist
  • 2022 Analog Devices, Inc, ADI Outstanding Student Designer Award
  • 2022 IEEE, IEEE MTT-S Graduate Fellowship
  • 2021 Princeton University, Yan Huo 94* Graduate Fellowship
  • 2021 IEEE IMS 2021, Best Student Paper Awards (Two papers)
  • 2020 IEEE IMS 2020, Best Student Paper Award
  • 2017 Skyworks Solutions, Inc, Excellent Performance Award

  • Zhou, J., Karahan, E. A., Ghozzy, S., Liu, Z., Jalili, H., & Sengupta, K. (2025). 25.3 AI-Enabled Design Space Discovery and End-to-End Synthesis for RFICs with Reinforcement Learning and Inverse Methods Demonstrating mm-Wave/sub-THz PAs Between 30 and 120GHz. In 2025 IEEE International Solid-State Circuits Conference (ISSCC) (p. 1-3).
  • Liu, Z., Karahan, E. A., & Sengupta, K. (2023). Ultra Broadband Phased-Array Transmitter with Low Phase Error of 1.24-2.8° across 36-91 GHz Supporting 10.8 Gbps 64QAM in 90 nm SiGe. In ESSCIRC 2023-IEEE 49th European Solid State Circuits Conference (ESSCIRC) (p. 497-500).
  • Liu, Z., Karahan, E. A., & Sengupta, K. (2023). A 36--91 GHz broadband beamforming transmitter architecture with phase error between 1.2°--2.8° for joint communication and sensing. IEEE Transactions on Microwave Theory and Techniques, 72(1), 589-605.
  • Karahan, E. A., Liu, Z., & Sengupta, K. (2023). Deep-learning-based inverse-designed millimeter-wave passives and power amplifiers. IEEE Journal of Solid-State Circuits (JSSC 2023 Best Paper Award), 58(11), 3074-3088.
  • Liu, Z., & Sengupta, K. (2022). A 44--64-GHz mmWave broadband linear Doherty PA in silicon with quadrature hybrid combiner and non-foster impedance tuner. IEEE Journal of Solid-State Circuits, 57(8), 2320-2335.
  • Liu, Z., Karahan, E. A., & Sengupta, K. (2022). A compact SiGe stacked common-base dual-band PA with 20/18.8 dBm P sat at 36/64 GHz supporting concurrent modulation. IEEE Microwave and Wireless Components Letters, 32(6), 720-723.
  • Liu, Z., Karahan, E. A., & Sengupta, K. (2022). Deep learning-enabled inverse design of 30--94 ghz p sat, 3db sige pa supporting concurrent multiband operation at multi-gb/s. IEEE Microwave and Wireless Components Letters (IMS Advanced Practice Paper Award), 32(6), 724-727.
  • Liu, Z., Sharma, T., & Sengupta, K. (2021). 80--110-GHz broadband linear PA with 33% peak PAE and comparison of stacked common base and common emitter PA in InP. IEEE Microwave and Wireless Components Letters (IMS Best Student Paper Award), 31(6), 756-759.
  • Liu, Z., Sharma, T., Chappidi, C. R., Venkatesh, S., Yu, Y., & Sengupta, K. (2020). A 42--62 GHz transformer-based broadband mm-Wave InP PA with second-harmonic waveform engineering and enhanced linearity. IEEE Transactions on Microwave Theory and Techniques, 69(1), 756-773.
  • Karahan, E., Liu, Z., Gupta, A., Shao, Z., Zhou, J., Khankhoje, U., & Sengupta, K. (). Deep-learning enabled generalized inverse design of multi-port radio-frequency and sub-terahertz passives and integrated circuits. Nature Communications https://doi.org/https://doi.org/10.1038/s41467-024-54178-1