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Zheng Liu

Zheng Liu

Assistant Professor
Lab profile

My group’s research envisions a new paradigm for wireless hardware that unifies AI, reconfigurable system architectures, and advanced device technologies to overcome three fundamental challenges in high-frequency systems: limited design productivity, inefficient spectrum utilization, and constrained energy efficiency. As RF, millimeter-wave, and sub-terahertz applications expand from 6G communications and autonomous sensing to intelligent infrastructure and wellness monitoring, existing design methodologies and hardware platforms struggle to keep pace with growing complexity and performance demands. We address these challenges by developing AI-enabled RF design optimization and automation, creating universal phased-array platforms with adaptive multi-band operation, and advancing heterogeneous and material-aware circuit technologies beyond conventional silicon. By co-optimizing algorithms, architectures, circuits, and devices within a holistic design framework, our research aims to shorten innovation cycles and enable scalable, energy-efficient, and spectrum-agile wireless systems for the next era of wireless technologies.

Zheng Liu received his B.S. from Peking University, an M.S from UCLA, and a Ph.D from Princeton University. Before joining UW–Madison, he served as a Senior Design Engineer at Skyworks Solutions, where he led the development of RF power amplifier modules that supported the shipment of over 40 million commercial wireless handset devices. He later collaborated with Apple on advanced mmWave beamforming ICs and conducted research at Texas Instruments’ Kilby Labs, developing GaN front-end modules and device technologies for emerging 6G wireless systems. He has received multiple IEEE best paper awards and a Best Ph.D. Thesis Award from Princeton. He is a member of the IEEE MTT-S Technical Committee on Microwave and mmWave Integrated Circuits (TC-14) and serves as a Subcommittee Vice-Chair/Chair on the Technical Program Committee for IEEE IMS (2025–2027).

If you are interested in working with me, please contact me by email to arrange a meeting.

Department

Electrical & Computer Engineering

Contact

4617, Engineering Hall
1415 Engineering Dr
Madison, WI
p: (608) 262-5579

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