GaN Distributed RF Power Amplifier Automation Design with Deep Reinforcement Learning

A computer-implemented method for training a multi-agent reinforcement learning (RL) network generating device parameters of circuits, wherein the method uses a processor coupled with a memory storing instructions implementing the method, wherein the instructions, when executed by the processor, carry out at steps of the method, comprising:

• acquiring inputs with respect to a desired circuit specification of a circuit, device parameters of the circuit, a topology of the circuit, a final state corresponding to a maximum step, wherein the desired circuit specification is represented by a gain, bandwidth, phase margin, power consumption, output power and power efficiency, or part of combination thereof, wherein each of the multi-agent RL network is configured to perform steps of:
• transmitting an action selected from a set of actions to an environment module that includes a netlist of the circuit;
• updating the device parameters of the circuit with respect to the desired circuit specification according to the selected action using a data processor of the environment module, wherein the action changes each of the device parameters by a minimum unit value of each device parameter;
• obtaining a current circuit specification of the circuit by simulating the netlist of the circuit based on the updated device parameters using a circuit simulator of the environment module;
• acquiring a reward from the environment module, wherein the reward is computed based on a difference between the current circuit specification and the desired circuit 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 the steps reach a preset value;
• and
• storing the updated device parameters into the memory.