A Genetic Algorithm Based Logic Optimization for Majority Gate-Based QCA Circuits in Nanoelectronics

摘要

Traditional CMOS technology is approaching its physical limits, so employing novel technologies such as nano-scale ones are being deployed. Quantum dots cellular automata is a new computing method in the nanotechnology that has considerable features such as low power, small dimension and high speed switch. The fundamental QCA logic primitives are the majority gate and the inverter gate which can be employed to design various QCA circuits. Several studies around reducing the number of gates have been proposed based on genetic algorithm by applying 3-input majority gate. In this paper, a reducing technique is presented; it synthesizes combination of 3-input and 5-input majority gate-based QCA circuits. A novel proposed method based on genetic algorithm takes advantage of both 3-input and 5-input majority. In order to verify the functionality of the proposed method, it is checked by means of computer simulations using QCADesigner tool. Additionally experimental results demonstrate that compared to previous works the proposed method performs equally well or better in many cases.