Simulator-Driven Deep Reinforcement Learning for Analog Circuit Design

This work addresses the use of reinforcement learning in the design of analog and mixed-signal (AMS) circuits. With recent advanced angstrom-technology-nodes adding new complexities, this highly manual process has grown increasingly challenging and less aligned with conventional design intuition. The presented approach modifies circuit topologies at the transistor-level to meet design requirements. We present, for the first time, a deep reinforcement learning (RL) framework capable of generating novel circuit topologies by using graph encodings for targeted specifications, starting from a minimal expert design and a user-specified testbench. To highlight the capabilities of the approach, we demonstrate the topological modification and expansion of incomplete sub-circuits to satisfy user-provided performance for three different types of circuits: 1) a ring oscillator, 2) a comparator, and 3) an operational transconductance amplifier. Our results demonstrate that our method is capable of generating previously unseen topologies that reach user-defined performance targets. In each design case, 100% of generated circuit netlists are correct by construction and over 90% of generated circuits demonstrate intended functionality and targeted performance when simulated with commercial tools.