Machine Learning-Based Defect Detection, Output Matching Assessment, and Performance Recentering of a 79-GHz Four-Way CMOS Power Amplifier

This work proposes the use of machine learning-based techniques for enhanced testability and performance calibration of an industrial 79-GHz power amplifier (PA) designed for an automotive radar system. The proposed techniques are based on a set of built-in test structures including nonintrusive process monitors based on digital ring oscillators and a built-in reflectometer. The indirect signatures provided by these test structures allow us to do the following: 1) identify defective instances of the PA under test; 2) evaluate the quality of the output matching adaptation; and 3) calibrate the performance of the PA to compensate for process variations. Defect detection is based on a defect filtering algorithm that employs outlier identification techniques; mismatch evaluation employs a machine learning regressor; and, finally, the proposed performance calibration is based on a one-shot statistical calibration that predicts the optimum configuration of the PA. The proposed techniques are experimentally demonstrated on a set of 32 fabricated chips designed in 28 nm CMOS technology.