Nonintrusive Machine Learning-Based Yield Recovery and Performance Recentering for mm-Wave Power Amplifiers: A Two-Stage Class-A Power Amplifier Case Study - Université Grenoble Alpes
Article Dans Une Revue IEEE Transactions on Microwave Theory and Techniques Année : 2023

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

Résumé

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.

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hal-04253273 , version 1 (22-10-2023)

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Florent Cilici, Marc Margalef-Rovira, Estelle Lauga-Larroze, Sylvain Bourdel, Gildas Leger, et al.. Nonintrusive Machine Learning-Based Yield Recovery and Performance Recentering for mm-Wave Power Amplifiers: A Two-Stage Class-A Power Amplifier Case Study. IEEE Transactions on Microwave Theory and Techniques, 2023, 72 (5), pp.3046 - 3064. ⟨10.1109/TMTT.2023.3322750⟩. ⟨hal-04253273⟩

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