Shape and Positional Anisotropy Based Area Efficient Magnetic Quantum-Dot Cellular Automata Design Methodology for Full Adder Implementation

Abstract

Magnetic quantum-dot cellular automata (MQCA) based computation started emerging as the Moore's law approaching towards its end. Number of nanomagnets and the area occupancy are major constraints in materializing this MQCA-based digital arithmetic circuit design. In this letter, we propose a design methodology and demonstrate the hybrid approach of using slant edged input and 45 $^\circ$ aligned nanomagnets for optimized binary full adder design. Asymmetric shape anisotropy nanomagnets pave the way for standalone inputs, whereas positional anisotropy reduces the signal loss in transmission of data and enables lossless information propagation. This complementary property of both shape and positional anisotropy leads to exploiting the energy minimization nature of nanomagnets, reducing the design footprint. Further, to enable the multipurpose scaling, horizontal and vertical layouts of the nanomagnetic computing design of full adder has been proposed. Our proposed nanomagnetic adder architecture leads to 28% reduction in the total number of nanomagnets compared to the state of the art design, leading to an area efficient architectural design.