Nonintrusive Machine Learning-Based Yield Recovery and Performance Recentering for mm-Wave Power Amplifiers: A Two-Stage Class-A Power Amplifier Case Study

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.

I. Introduction

Nowadays, the constant evolution of nanometric integrated technologies with optimized back-end-of-line (BEOL) metal stacks allows the development of high-performance monolithic millimeter-wave (mm-wave) circuits. This evolution is guided by the needs of a wide variety of applications such as high-data-rate communications, healthcare imaging systems, and automotive radar. Monolithic mm-wave integration in conventional silicon technologies has the clear advantage of reducing the cost and enabling the co-integration with complex VLSI systems, including analog, mixed-signal, and digital functions. However, at the same time, integrating mm-wave circuits in advanced nanometric technologies creates new challenges. Nanometric technologies are prone to process variations that may dramatically degrade the performance of sensitive radio frequency (RF) and mm-wave circuits [1], [2]. In addition, mm-wave circuits often include full-custom passive elements, which may complicate evaluating the impact of process variations at the design stage. As a consequence, fabrication yield may be significantly limited, especially for safety-critical applications with stringent performance requirements.