Accelerating RF Power Amplifier Design via Intelligent Sampling and ML-Based Parameter Tuning

TL;DR

This paper introduces a machine learning-based framework that accelerates RF power amplifier design by reducing simulation efforts by over 65% while maintaining high accuracy, enabling faster and efficient design iterations.

Contribution

It combines intelligent sampling with gradient boosting to significantly cut simulation requirements and automate RF amplifier optimization processes.

Findings

Reduced simulation time by up to 78%.

Achieved high prediction accuracy with R^2 of 0.901.

Automated workflow enables rapid design iterations.

Abstract

This paper presents a machine learning-accelerated optimization framework for RF power amplifier design that reduces simulation requirements by 65% while maintaining $\pm0.4$ dBm accuracy for the majority of the modes. The proposed method combines MaxMin Latin Hypercube Sampling with CatBoost gradient boosting to intelligently explore multidimensional parameter spaces. Instead of exhaustively simulating all parameter combinations to achieve target P2dB compression specifications, our approach strategically selects approximately 35% of critical simulation points. The framework processes ADS netlists, executes harmonic balance simulations on the reduced dataset, and trains a CatBoost model to predict P2dB performance across the entire design space. Validation across 15 PA operating modes yields an average $R^2$ of 0.901, with the system ranking parameter combinations by their likelihood of meeting target specifications. The integrated solution delivers 58.24% to 77.78% reduction in simulation time through automated GUI-based workflows, enabling rapid design iterations without compromising accuracy standards required for production RF circuits.