基于遗传算法的多目标多输出QCA电路综合与优化
Synthesis and Optimization of Multi-Objective Multi-Output QCA Circuit using Genetic Algorithm
摘要：
CMOS技术的物理限制引发了一些寻找替代技术的研究。QCA是新兴的纳米技术之一，作为CMOS的替代品正在受到关注。QCA的主要潜力在于其超低功耗、较小的面积开销和高速。择多门和反相门是QCA中的基本门，它们共同构成一个通用逻辑门，可以实现任何QCA电路。本文提出了一种有效的任意多输出布尔函数的QCA电路优化综合方法。提出了一种基于多目标遗传算法的QCA电路优化设计方法，用于减少QCA电路最坏情况下的延迟“与”门数。不同的重要性是考虑到最坏情况下的延迟，没有。多数和数量。为了达到最优结果和降低计算复杂性，使用了几种有效的技术，此外还使用了额外的方法来消除最终解中的冗余。与现有最佳技术的比较表明，所提出的技术优于最坏情况下的延迟“与”门数。
英文摘要：
The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA,which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
