A novel FPGA-programmable switch matrix interconnection element in quantum-dot cellular automata
Hashemi, Sara, Rahimi Azghadi, Mostafa, Zakerolhosseini, Ali, and Navi, Keivan (2015) A novel FPGA-programmable switch matrix interconnection element in quantum-dot cellular automata. International Journal of Electronics, 102 (4). pp. 703-724.
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Abstract
The Quantum-dot cellular automata (QCA) is a novel nanotechnology, promising extra low-power, extremely dense and very high-speed structure for the construction of logical circuits at a nanoscale. In this paper, initially previous works on QCA-based FPGA's routing elements are investigated, and then an efficient, symmetric and reliable QCA programmable switch matrix (PSM) interconnection element is introduced. This element has a simple structure and offers a complete routing capability. It is implemented using a bottom-up design approach that starts from a dense and high-speed 2:1 multiplexer and utilise it to build the target PSM interconnection element. In this study, simulations of the proposed circuits are carried out using QCAdesigner, a layout and simulation tool for QCA circuits. The results demonstrate high efficiency of the proposed designs in QCA-based FPGA routing.
Item ID: | 45720 |
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Item Type: | Article (Research - C1) |
ISSN: | 1362-3060 |
Keywords: | FPGA; multiplexer; programmable switch matrix interconnection element; quantum dot cellular automata |
Funders: | Iran Telecommunication Research Center |
Date Deposited: | 06 Dec 2016 03:16 |
FoR Codes: | 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090601 Circuits and Systems @ 33% 10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 34% 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 33% |
SEO Codes: | 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 33% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 34% 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 33% |
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