Gan distributed rf power amplifier automation design with deep reinforcement learning

Abstract

A computer-implemented method is provided for training multi RL agent networks generating device parameters of circuits. The method includes acquiring inputs with respect to a desired circuit specification of a circuit, a device parameter, a topology of the circuit, a final state corresponding to a maximum step, wherein the desired circuit specification includes a gain, bandwidth, phase margin, power consumption, output power and power efficiency, wherein each of the RL agent networks is configured to perform transmitting an action selected from a set of actions to an environment module, updating the device parameters of the circuit with respect to a circuit specification, obtaining a current specification of the circuit by simulating a netlist of the circuit based on the updated device parameters using a circuit simulator of the environment module, wherein the environment module includes the netlist of the circuit, acquiring a reward from the environment module, wherein the reward is computed based on a difference between the current specification and the desired specification, wherein the steps of the transmitting, updating, obtaining and acquiring are continued until the reward reaches to a threshold value or a number of steps reach a preset value, and generating the satisfied updated device parameters via the interface.