Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors

TL;DR

This paper introduces a reinforcement learning framework for automating the design of VCO inductors, enabling efficient and adaptable inductor creation that meets specific electrical specifications.

Contribution

It presents a novel RL-based method for sequentially drawing inductors and an adaptive variant for varying target specifications, improving automation in inductor design.

Findings

Successfully generates inductors meeting target specs

Outperforms manual design in efficiency and accuracy

Adapts quickly to new design specifications

Abstract

Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and time-consuming task that is conventionally done manually by human experts. In this paper, we propose a framework for automating the design of VCO inductors, using Reinforcement Learning (RL). We formulate the problem as a sequential procedure, where wire segments are drawn one after another, until a complete inductor is created. We then employ an RL agent to learn to draw inductors that meet certain target specifications. In light of the need to tweak the target specifications throughout the circuit design cycle, we also develop a variant in which the agent can learn to quickly adapt to draw new inductors for moderately different target specifications. Our empirical results show that the proposed framework is successful at automatically generating VCO inductors that meet or exceed the target specification.