GateRL: Automated Circuit Design Framework of CMOS Logic Gates Using Reinforcement Learning

This paper proposes a GateRL that is an automated circuit design framework of CMOS logic gates based on reinforcement learning. Because there are constraints in the connection of circuit elements, the action masking scheme is employed. It also reduces the size of the action space leading to the improvement on the learning speed. The GateRL consists of an agent for the action and an environment for state, mask, and reward. State and reward are generated from a connection matrix that describes the current circuit configuration, and the mask is obtained from a masking matrix based on constraints and current connection matrix. The action is given rise to by the deep Q-network of 4 fully connected network layers in the agent. In particular, separate replay buffers are devised for success transitions and failure transitions to expedite the training process. The proposed network is trained with 2 inputs, 1 output, 2 NMOS transistors, and 2 PMOS transistors to design all the target logic gates, such as buffer, inverter, AND, OR, NAND, and NOR. Consequently, the GateRL outputs one-transistor buffer, two-transistor inverter, two-transistor AND, two-transistor OR, three-transistor NAND, and three-transistor NOR. The operations of these resultant logics are verified by the SPICE simulation.

A COMBINED SIMPLEX–TRUST-REGION METHOD FOR ANALOG CIRCUIT OPTIMIZATION

The analog-integrated circuits industry is exerting increasing pressure to shorten the analog circuit design time. This pressure is put primarily on the analog circuit designers that in turn demand automated circuit design tools evermore vigorously. Such tools already exist in the form of circuit optimization software packages but they all suffer a common ailment — slow convergence. Even taking into account the increasing computational power of modern computers the convergence times of such optimization tools can range from a few days to even weeks. Different authors have tried diverse approaches for speeding up the convergence with varying success. In this paper authors propose a combined optimization algorithm that attempts to improve the speed of convergence by exploiting the positive properties of the underlying optimization methods. The proposed algorithm is tested on a number of test cases and the convergence results are discussed.