DOI: 10.1109/TMTT.2025.3619021   IEEEXplore: https://ieeexplore.ieee.org/document/11215871    

Machine learning-based defect detection, output matching assessment, and performance recentering of a four-way CMOS power amplifier

79 GHz for this demonstration

Defect detection is based on a defect filtering algorithm that employs outlier identification techniques. Mismatch evaluation employs a machine learning regressor. Performance calibration is based on a one-shot statistical calibration that predicts the optimum configuration of the PA

To enhanced testability and performance calibration of power amplifiers (PAs) designed for 5G and beyond networks, automotive radar systems and the Internet-of-Things (IoT).

Power-added efficiency (PAE) RMS calibration error of 0.77% with predictive calibration model R2 of 0.71; Drain efficiency (DE) RMS calibration error of 0.68% with predictive calibration model R2 of 0.78; Power gain (Gp) RMS calibration error of 0.17 dB with predictive calibration model R2 of 0.93; Saturated output power (Psat) RMS calibration error of 0.19 dBm with predictive calibration model R2 of 0.92.