VSWR-Resilient Mm-Wave and Cm-Wave PAs for Large-Scale Phased Arrays

Large-scale mm-Wave and cm-Wave phased arrays have become central to wireless communication and sensing systems, including terrestrial 5G/6G links and base stations, non-terrestrial networks (NTNs), satellite communication (SATCOM), radar, and relay applications. In dense arrays, power amplifiers (PAs) and antenna array interact with each other: array radiation depends on the amplitude/phase of PA output signals, while the load impedance experienced by each PA varies with frequency, scan angle, and element position. This active antenna impedance, described as voltage standing wave ratio (VSWR) variation, arises mainly from antenna inter-element mutual coupling and is shaped by package/interconnect parasitics. Each PA can deviate from its optimum large-signal operating condition, degrading output power, power gain, power-added efficiency, AM-AM/AM-PM, and reliability margin. These variations further affect array EIRP consistency, EVM headroom, link budget, thermal density, and beamforming calibration, complicating PA design for wideband, wide-scan-angle arrays. This review introduces the origins of antenna VSWR and antenna-PA interactions connecting the antenna reflection coefficient $Γ_{\mathrm{ant}}$ and PA output matching $S_{22}$ to delivered-power and transmitted-phase variation through the $S_{22}Γ_{\mathrm{ant}}$ dependence. Reverse-coupled excitation and reverse intermodulation distortion (RIMD) are discussed. This motivates PA designs that achieve simultaneous output and loadline matching (SOLM), enabling a small output reflection coefficient $|S_{22}|$ without significantly compromising large-signal performance. Recent mm-Wave and cm-Wave VSWR-resilient integrated PA techniques and demonstrations are reviewed. Finally, challenges and opportunities for compact, load-insensitive, energy-efficient, high-power-density, and calibration-scalable integrated PAs are discussed.

Transfer Learning Assisted Fast Design Migration Over Technology Nodes: A Study on Transformer Matching Network

In this study, we introduce an innovative methodology for the design of mm-Wave passive networks that leverages knowledge transfer from a pre-trained synthesis neural network (NN) model in one technology node and achieves swift and reliable design adaptation across different integrated circuit (IC) technologies, operating frequencies, and metal options. We prove this concept through simulation-based demonstrations focusing on the training and comparison of the coefficient of determination (R2) of synthesis NNs for 1:1 on-chip transformers in GlobalFoundries(GF) 22nm FDX+ (target domain), with and without transfer learning from a model trained in GF 45nm SOI (source domain). In the experiments, we explore varying target data densities of 0.5%, 1%, 5%, and 100% with a complete dataset of 0.33 million in GF 22FDX+, and for comparative analysis, apply source data densities of 25%, 50%, 75%, and 100% with a complete dataset of 2.5 million in GF 45SOI. With the source data only at 30GHz, the experiments span target data from two metal options in GF 22FDX+ at frequencies of 30 and 39 GHz. The results prove that the transfer learning with the source domain knowledge (GF 45SOI) can both accelerate the training process in the target domain (GF 22FDX+) and improve the R2 values compared to models without knowledge transfer. Furthermore, it is observed that a model trained with just 5% of target data and augmented by transfer learning achieves R2 values superior to a model trained with 20% of the data without transfer, validating the advantage seen from 1% to 5% data density. This demonstrates a notable reduction of 4X in the necessary dataset size highlighting the efficacy of utilizing transfer learning to mm-Wave passive network design. The PyTorch learning and testing code is publicly available at https://github.com/ChenhaoChu/RFIC-TL.