A QCA placement and routing algorithm based on the SA algorithm

Abstract

ABSTRACT In the Beyond Moore era, QCA (Quantum-dot Cellular Automata) has been widely studied because of its low power consumption and high device concentration. As the core of chip design, EDA technology has become an indispensable step in QCA research. It refers to the use of computer-aided design software to complete large-scale circuit function design, logic synthesis, placement and routing, design rule check and other processes. In the present study, there are still many defects in QCA placement and routing. The main problems it faces are low success rate, too large area and too many cross lines. To address these problems, our team had proposed a hybrid strategy based on the genetic algorithm and the improved A-star algorithm. But the running time of the previously proposed algorithm is not satisfying, besides it lacks self-adaption feature which is important as the problem size varies among different circuits. This paper replaces the genetic algorithm with a simulated annealing algorithm, and introduces a self-adaption strategy to better accommodate different circuit sizes. Experimental results show that the new algorithm combination possesses a higher success rate, lower running time and more intelligent adaption ability.