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

TL;DR

This work trains a neural simulator to replace the PDE electromagnetic simulator and demonstrates 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.

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. 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 in the imagined space from the neural simulator. The resulted automatic circuit-design agent achieves super-human design results. 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

Figures (10)

• Figure 1: CNN-based S-Parameters predictor
• Figure 2: Workflow of Alpha-RF. During the training phase (left), given target specifications, the agent outputs a complete set of design parameters in one shot. These parameters are passed through the neural simulator to obtain predicted S-parameters, and the reward function converts the mismatch between measurements and target specifications into a scalar reward to update the agent. During the inference phase (right), the trained agent generates multiple candidate designs for target specifications, which are evaluated by the neural simulator. The candidate yielding the highest reward is selected as the final design.
• Figure 3: a) Training dynamics of the neural S-parameters simulator b) Theoretical scaling of accuracy
• Figure 4: Comparison of predicted S-parameters and true S-parameters
• Figure 5: S-Parameters for automatically-generated filter designs of various specifications