AI Model for Analog Circuit
Description of Reinforcement model for optimizing circuit parameters.
This documentation is for the AI-accelerated analog circuit optimization project, where we use reinforcement learning to tune key parameters in analog design.
🎯 Objective:
Leverage deep reinforcement learning to automatically optimize transistor sizing and compensation parameters under modern CMOS processes (e.g., 45nm, 55nm), aiming for high gain, large bandwidth, and strong driving capability.
📚 What You’ll Find Here:
- A step-by-step explanation of the design theory (with companion videos)
- Detailed simulation setup using NGSPICE and Spectre
- Reinforcement learning setup and training details (e.g., PPO + MLP)
- Full code breakdown and how to run the system
Code URL
Let’s begin with the Project Overview →