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Graphene interconnects have been projected to out-perform Copper interconnects in the next generation Magnetic Quantum-dot Cellular Automata (MQCA) based nano-electronic applications. In this paper a simple two-step lithography process for patterning CVD monolayer graphene on SiO 2 /Si substrate has been used that resulted in the current density of one order higher magnitude as compared to the state-of-the-art graphene-based interconnects. Electrical performances of the fabricated graphene interconnects were evaluated, and the impact of temperature and size on the current density and reliability was investigated. The maximum current density of 1.18 ×10 8 A/cm 2 was observed for 0.3 μm graphene interconnect on SiO 2 /Si substrate, which is about two orders and one order higher than that of conventionally used copper interconnects and CVD grown graphene respectively, thus demonstrating huge potential in outperforming copper wires for on-chip clocking. The drop in current at 473 K as compared to room temperature was found to be nearly 30%, indicating a positive temperature coefficient of resistivity (TCR). TCR for all cases were studied and it was found that with decrease in width, the sensitivity of temperature also reduces. The effect of resistivity on the breakdown current density was analysed on the experimental data using Matlab and found to follow the power-law equations. The breakdown current density was found to have a reciprocal relationship to graphene interconnect resistivity suggesting Joule heating as the likely mechanism of breakdown.

In this paper, we propose a dipole coupled magnetic quantum-dot cellular automata-based approximate nanomagnetic (APN) architectural design approach for subtractor and adder. In addition, we also introduce an APN architecture which offers runtime reconfigurability using a single design layout comprising only four nanomagnets. Subsequently, we propose the APN add/sub architecture by exploiting shape anisotropy and ferromagnetically coupled fixed input majority gate. The proposed APN architecture designs have been implemented using a micromagnetic simulation tool and performance has been compared with the state-of-the-art approach resulting in a ∼50%–80% reduction in the number of nanomagnets and clock cycles without degradation in the accuracy leading to area and energy efficiency.

In this letter, we introduce the magnetic quantum-dot cellular automata (MQCA) based area and speed efficient design approach for nanomagnetic full adder implementation. We exploited the physical properties of three input MQCA majority gate (MG), where the fixed input of the MG is coupled ferromagnetically to one of the primary input operands. Subsequently we propose a design methodology, mapping logic and micromagnetic software implementation, validation of the binary full adder architecture built using two–three inputs MQCA MGs. In addition, we also analyzed our proposed design for switching errors to ensure bit stability and reliability. Our proposed design leads to ∼36%–69% reduction in the number of nanomagnets, ∼50%–75% reduction in the number of clock cycles and ∼33%–50% reduction in the number of MG operations required for the binary full adder implementation compared to the state of art designs.

In this paper, we report on the interesting phenomenon of magnetic phase transitions (MPTs) observed under the combined influence of an electric field (E) and temperature (T) leading to a thermo-electromagnetic effect on the pristine single-layer zigzag graphene nanoribbon (szGNR). Density functional theory-based first principles calculations have been deployed for this study on the intrinsic magnetic properties of graphene. Interestingly, by tuning electric field (E) and temperature (T), three distinct magnetic phase behaviors, para-, ferro- and antiferromagnetic are exhibited in pristine szGNR. We have investigated the unrivaled positional parameters of these MPTs. MPT occurring in the system also follows a positional trend and the change in these positional parameters with regard to the size of the szGNR along with the varied E and T is studied. We propose a bow-tie schematic to induce the intrinsic magnetism in graphene and present the envisaged model of the processor application with the reported intrinsic MPT in szGNR. This fundamental insight into the intrinsic MPTs in graphene is an essential step towards developing graphene-based spin-transfer torque magnetoresistive random access memory, quantum computing devices, magnonics and spintronic memory application.