Method of rf analog circuits electronic design automation based on gcn and deep reinforcement learning

Abstract

A computer-implemented method is provided for generating device parameters of circuits using a pretrained reinforcement learning (RL) agent composed of a graph neural network (GNN) and a fully connected neural network (FCNN). The method is performed by steps including acquiring inputs with respect to a set of desired specifications or one desired specification of a circuit, device parameters, a fixed topology of the circuit and providing the inputs to the RL agent. The desired circuit description includes a graph modeling the topology of the circuit and device parameters of the circuit, and the desired specifications include gain, bandwidth, phase margin, power consumption, output power and power efficiency. The pretrained RL agent performs steps including transmitting an action selected from a set of actions to an environment module, updating the device parameters of the circuit according to the selected action using a data processor of the environment module, obtaining a current specification of the circuit by simulating a netlist of the circuit, acquiring a reward from the environment module, and generating the updated device parameters of the circuit.