Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learning

Nhat Tran, Chenjie Hao, Alexander Stameroff, Anh-Vu Pham, Yubei Chen

Keywords: Electronic Design Automation, Neural Networks, Deep Reinforcement Learning, Neural Simulator

TL;DR: Automatic radio-frequency filter circuit design using neural simulator and reinforcement learning.

Abstract: Accurate, high-performance radio-frequency (RF) filter circuits are ubiquitous in radio-frequency communication and sensing systems for accepting and rejecting signals at desired frequencies. Conventional RF filter design process involves manual calculations of design parameters, followed by intuition-guided iterations to achieve the desired response for a set of filter specifications. This process is time-consuming due to time- and resource-intensive electromagnetic simulations using full-wave numerical PDE solvers, and requires many intuition-guided adjustments to achieve an practically usable design. This process is also highly sensitive to domain expertise and requires many years of professional training. To address these bottlenecks, we propose an automatic RF filter circuit design tool using neural simulator and reinforcement learning. First we train a neural simulator to replace the PDE electromagnetic simulator. The neural-network-based simulator reduces each of the simulation time from 4 minutes on average to less than 100 millisecond while maintaining a high precision. Such dramatic acceleration enable us to leverage deep reinforcement learning algorithm and train an amortized inference policy to perform automatic design optimization in the imagined space from the neural simulator. The result automatic circuit-design agent achieves super-human design results and exceeds specifications in several cases. The automatic circuit-design agent also reduces the on-average design cycle from days to under a few seconds. Even more surprisingly, we demonstrate that the neural simulator can generalize to design spaces far from the training dataset and in a sense it has learned the underlying physics--Maxwell equations. We also demonstrate that the reinforcement learning has discovered many expert-like design intuitions. This work marks a step in using neural simulators and reinforcement learning in RF circuit design and the proposed method is generally applicable to many other design problems and domains in close affinity.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 8672