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State-of-the-art nanometric fabrication processes enable the integration of monolithic millimeter-wave (mm-wave) circuits. However, nanometric technologies are prone to process variations that may significantly impact the performance of the fabricated mm-wave circuits and dramatically reduce the fabrication yield. In order to improve the fabrication yield, extensive resources are required for tuning the functionality of each fabricated die in the production line, especially in the mm-wave domain. In this work, we implement and experimentally validate a machine learning-based calibration strategy for mm-wave circuits that significantly simplifies this tuning process. A machine learning algorithm is employed to predict the optimum values of a set of on-chip tuning knobs based on nonintrusive measurements provided by embedded process monitor circuits. The proposed technique is demonstrated on a 69-GHz power amplifier (PA) with one-shot calibration capabilities integrated in STMicroelectronics 55-nm CMOS technology. The experimental results on a set of 39 fabricated samples demonstrate the feasibility and performance of the proposed machine learning-based calibration for yield recovery and performance recentering applications.

A 76–81-GHz dual-path receiver in 28-nm FD-SOI CMOS technology is presented for automotive radar applications. It introduces a new accurate phase control of phased-array radars using MOS varactors. The proposed solution is analytically described and implemented in the front-end low-noise amplifier (LNA) of each receiver path. It allows a maximum phase control of 23° with less than a 1° resolution for minimum degradation of the receiver performances. The receiver contains a two-stage LNA, a passive mixer, a local oscillator (LO) driver, and a baseband amplifier (BBA). The receiver achieves a 6.2 dB noise figure (NF) at 81 GHz. The receiver exhibits input compression points (ICPs $_{\text {1 dB}}$ ) of −29.5 and −14.2 dBm with and without BBA, respectively. The active area of a single receiver path is only 0.057 mm2 for a 46.1 mW power consumption and the active area of the dual-path receiver is 0.28 mm2.