, Volume 14, Issue 1, pp 95-118
Date: 27 Oct 2012

Multi-objective optimization of QCA circuits with multiple outputs using genetic programming

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Abstract

Quantum-Dot Cellular Automata (QCA) is a promising nanotechnology that has been recognized as one of the top emerging technologies in future computers. Size density of several orders of magnitude smaller than Complementary Metal-Oxide Semiconductor, fast switching time and extremely low power, has caused QCA to become a topic of intense research. The majority gate and the inverter gate together make a universal set of Boolean primitives in QCA technology. Reducing the number of required primitives to implement a given Boolean function is an important step in designing QCA logic circuits. Previous research has shown how to use genetic programming to minimize the number of gates implementing a given Boolean function with one output. In this paper, we first show how to minimize the gates for the given Boolean truth tables with an arbitrary number of outputs using genetic programming. Then, another criterion, reduction of the delay of the implementing circuit is considered. Multi-objective genetic programming is applied to simultaneously optimize both objectives. The results demonstrate the proposed approach is promising and worthy of further research.