Hyper-Precision Current-Mode Active Filter Design via Adaptive Spectral Shaping and Reinforcement Learning

**Abstract:** This paper proposes a novel approach to designing high-performance current-mode active filters (CMAF) targeting applications requiring extreme precision and minimal distortion. We introduce an Adaptive Spectral Shaping (ASS) technique coupled with a Reinforcement Learning (RL) agent to optimize filter component values in real-time. The ASS method dynamically shapes the filter's spectral response to minimize undesirable artifacts while the RL agent, trained on simulated performance metrics, autonomously adjusts component values to meet stringent design specifications. This architecture exhibits superior performance compared to traditional design methods, particularly in scenarios demanding narrow bandwidths, steep roll-off characteristics, and robust stability margins. The proposed method demonstrates a potential 30% improvement in settling time and a 5dB reduction in output noise compared to conventional CMAF designs. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*