Analog Circuit Design Automation via Sequential RL Agents and Gm/ID Methodology

This paper studies the problem of designing analog circuits to achieve target specifications, which can be formulated as a multi-objective combinatorial optimization (MOCO) under uncertainty. We address this challenging problem using the
$g_{m} / I_{D}$
methodology and a reinforcement learning (RL) framework. The proposed fast RL-based analog circuit designer (fRL-AD) maintains circuits’ DC bias conditions while determining their sizing parameters associated with AC characteristics. This ensures robust convergence to optimal sizing parameters across target specifications and proficiently captures layout effects. Specifically, by decomposing the problem into a sequence of feasible problems, our pre-trained RL agent can efficiently seek a solution for each feasible problem by generating states (i.e., candidate solutions) following a learned policy. Since the sequence of feasible regions is designed to approach an optimal solution to our main problem, the RL agent can find a near-optimal solution by sequentially tackling the feasible problems. Remarkably, using better initial points (or states), our approach is more efficient than directly solving the last feasible problem. Furthermore, we introduce an adaptive action space in our RL framework, which can dynamically modulate the size of the action space elements. The proposed method provides an effective and stable design of various analog circuits, overcoming their traditionally low productivity due to reliance on human expertise and time-consuming simulations to handle uncertainties. We verify the effectiveness of our algorithm via experiments with various analog circuit topologies.