An Area Efficient and Optimized Code Converters using Quantum-Dot Cellular Automata

Abstract

QCA is one of the novel and  potentially attractive technology for implementation of the digital circuits at Nanoscale. While implementing many digital applications using conventional CMOS circuits, they have limitations due to its high leakage, low switching speed, power dissipation, occupies more area and need more power. To overcome these limitations and to meet the requirements of industry parameters, ITRS has proposed several Nano technologies [1-3]. QCA is the one of the best Nano technology which is completely transistor less computing paradigm and provides  an attractive solution to surpass the physical limitations that CMOS devices face during further downscaling of its dimensional sizes. QCA technology is expected to achieve high  deveice density its ultra small sizes and extremely low power consumption relative to CMOS. The code conversion circuits play vital role in encoding and decoding the information in electronic and computer based communication systems in order to protect the data from spying. In this paper, different types of code conversion QCA layout designs have been proposed and their performance is measured in parameters like cell count, design area and latency over the previous code converters. With the proposed Binary to Gray code converter design there is an percentage improvement of 60.87% in the cell count, 51% in the area occupied by system and  latency of 33.33% . The proposed design of Gray to Binary code conversion provides an percentage of improvement of cell count and in the design area 33% and 30% respectively. Also, with the proposed Binary to BCD code conversion using QCA full adder, there is an 12.4% improvement of cell count and 10.8% in the design area over the best existing designs. The proposed designs of Binary to Excess-3 and BCD to Excess-3  design provides an percentage of improvement of cell count (36% and 50.5%), (46 % and 56%) in the design area over the best existing designs. These designs have been implemented using QCA Designer 2.0.3 tool.