Self-Healing Techniques for Robust mm-Wave Power Amplification

Jenny Yi Chun Liu*, Zhiwei Xu, Qun Jane Gu, Mau Chung Frank Chang

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    1 Scopus citations


    Integrated mm-Wave system-on-a-chip (SoC) in silicon CMOS has become increasingly popular due to a variety of high potential applications of mm-Wave systems, such as radars for automobiles, multi-Gbps file-transfer, and point-to-point communication links for backhaul networks. Power amplification is one of the most critical functions within mm-Wave systems since it determines the transmission data quality and the achievable communication distance. Although CMOS technology demonstrates unparalleled digital signal processing capability, delivering large output power is extremely challenging due to their low breakdown voltage and lossy substrate. In addition, the process variations during fabrication and high sensitivity to environment changes further degrade mm-Wave power amplifier performance and reduce the fabrication yield. In order to boost performance and save cost, self-healing techniques have been proposed to adaptively adjust power amplifier working configurations through many embedded control knobs. A self-healing controller is applied to accomplish the adjustment by detecting the performance and environment conditions of the target power amplifier, such as output power, nonlinearity, and temperature, hence adapting the power amplifier to an optimum condition for the best performance. To validate the concept, we will present a power amplification system with self-healing capabilities and adaptive bias network working in 60-GHz band, which have achieved significant performance boost and more than 10× yield improvement.

    Original languageEnglish
    Title of host publicationRF and mm-Wave Power Generation in Silicon
    PublisherElsevier Inc.
    Number of pages28
    ISBN (Print)9780124080522
    StatePublished - 1 Jan 2016


    • CMOS
    • Mm-Wave
    • Performance yield
    • Process variation
    • Reconfigurability
    • Self-healing
    • Transformers


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