Accelerating RF Power Amplifier Design via Intelligent Sampling and ML-Based Parameter Tuning
This work addresses the time-intensive simulation bottleneck for RF circuit designers, offering a domain-specific incremental improvement that accelerates design iterations without sacrificing production accuracy.
This paper tackles the problem of reducing simulation time in RF power amplifier design by introducing a machine learning framework that cuts simulation requirements by 65% while maintaining ±0.4 dBm accuracy across most modes, achieving an average R² of 0.901 in validation.
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.