用智能采样和机器学习，让射频功放设计快65%还准。
Accelerating RF Power Amplifier Design via Intelligent Sampling and ML-Based Parameter Tuning
- 用最大最小拉丁超立方采样+CatBoost模型，智能选关键参数点。
- 仅用35%仿真量，就达到90.1%的预测准确率，误差小于0.4dBm。
- 适合需要快速迭代的射频电路设计团队，尤其看重效率与精度平衡。
本文提出一种机器学习加速的射频功率放大器设计优化框架，将仿真需求减少65%，同时在多数工作模式下保持±0.4 dBm的精度。该方法结合最大最小拉丁超立方采样与CatBoost梯度提升算法，智能探索多维参数空间。无需穷举所有参数组合以满足P2dB压缩指标，本方法仅战略性选取约35%的关键仿真点。框架处理ADS网表，在缩减数据集上执行谐波平衡仿真，并训练CatBoost模型以预测整个设计空间中的P2dB性能。在15种功放工作模式下的验证显示，平均R²达0.901，系统可按达标可能性对参数组合排序。集成解决方案通过自动化图形界面流程，实现58.24%至77.78%的仿真时间降低，支持快速设计迭代，且不牺牲生产级射频电路所需的精度标准。
原文摘要 · Abstract (English)
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.
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