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Reversible Quantum-Dot Cellular Automata-Based Arithmetic Logic Unit.

Alharbi, M, Edwards, G and Stocker, R (2023) Reversible Quantum-Dot Cellular Automata-Based Arithmetic Logic Unit. Nanomaterials, 13 (17). ISSN 2079-4991

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Quantum-dot cellular automata (QCA) are a promising nanoscale computing technology that exploits the quantum mechanical tunneling of electrons between quantum dots in a cell and electrostatic interaction between dots in neighboring cells. QCA can achieve higher speed, lower power, and smaller areas than conventional, complementary metal-oxide semiconductor (CMOS) technology. Developing QCA circuits in a logically and physically reversible manner can provide exceptional reductions in energy dissipation. The main challenge is to maintain reversibility down to the physical level. A crucial component of a computer's central processing unit (CPU) is the arithmetic logic unit (ALU), which executes multiple logical and arithmetic functions on the data processed by the CPU. Current QCA ALU designs are either irreversible or logically reversible; however, they lack physical reversibility, a crucial requirement to increase energy efficiency. This paper shows a new multilayer design for a QCA ALU that can carry out 16 different operations and is both logically and physically reversible. The design is based on reversible majority gates, which are the key building blocks. We use QCADesigner-E software to simulate and evaluate energy dissipation. The proposed logically and physically reversible QCA ALU offers an improvement of 88.8% in energy efficiency. Compared to the next most efficient 16-operation QCA ALU, this ALU uses 51% fewer QCA cells and 47% less area.

Item Type: Article
Uncontrolled Keywords: arithmetic logic unit (ALU); energy dissipation; quantum-dot cellular automata (QCA); reversible; 0912 Materials Engineering; 1007 Nanotechnology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Computer Science & Mathematics
Publisher: MDPI AG
SWORD Depositor: A Symplectic
Date Deposited: 15 Sep 2023 13:11
Last Modified: 15 Sep 2023 13:16
DOI or ID number: 10.3390/nano13172445
URI: https://researchonline.ljmu.ac.uk/id/eprint/21448
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