Robustness of the In-Plane Data Crossing for Molecular Field-Coupled Nanocomputing

Beretta, Giuliana; Ardesi, Yuri; Piccinini, Gianluca; Graziano, Mariagrazia

doi:10.1109/nano58406.2023.10231304

Cited by 3 publications

References 23 publications

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Addressing multi-molecule field-coupled nanocomputing for neural networks with SCERPA

Ravera,

Beretta,

Ardesi

et al. 2024

J Comput Electron

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The molecular field-coupled nanocompunting (molFCN) technology encodes the information in the charge distribution of electrostatically coupled molecules, making it an exciting solution for future beyond-CMOS low-power electronics. Recent literature has shown that multi-molecule molFCN enables the design of devices with tailored unconventional characteristics, such as majority voters working as artificial neurons. This work presents a multi-molecule molFCN neuron model based on the weighted-inputs formulation to estimate molFCN neurons behavior. Then, the introduced model is used to design each neuron of molFCN circuits working as neural networks. In particular, we propose a molFCN neural network operating as an input pattern classifier. The results show the model aptitude in predicting the logic output values for individual neurons and, consequently, entire networks. The model accuracy has been evaluated by comparing the results from the neuron mathematical model with those obtained from the circuit-level simulations conducted with the SCERPA tool. Overall, this study highlights the strategic use of diverse molecules in molFCN layouts, customizing circuit operations, and expanding design possibilities for specific molFCN device functioning.

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Addressing multi-molecule field-coupled nanocomputing for neural networks with SCERPA

Ravera,

Beretta,

Ardesi

et al. 2024

J Comput Electron

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Implementation of adders using XOR gates in quantum-dot cellular automata with physical verification

Shaw,

Khan

2024

Phys. Scr.

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This paper presents a promising approach to nanoscale computing, offering significant advantages through the QCA technology. It suggests a highly efficient, scalable, and reliable multilayered QCA half and full adder circuits, leveraging a three-input QCA XOR gate. The proposed full adder layout demonstrates significant improvements in various parameters, including area, latency, and energy dissipation. In particular, it offers 17% greater area efficiency and utilizes 14 fewer cells compared to the best work existing work. We thoroughly evaluated energy dissipation using the QCADesigner-E tool. We also examined the cost functions, with a QCA-specific cost of 22 units, which is ~37% better than earlier designs. The architecture is strategically designed with externally accessible input and output nodes to ensure seamless scalability. Physical reliability is ensured through kink energy calculations for the suitability of higher-order circuit designs. Practical applications of the proposed blocks include their use in arithmetic logic units (ALUs), digital signal processors, and other modern processing and computing systems. This work sets a new benchmark for future developments in QCA technology, offering a robust, efficient, and versatile solution for advanced nano-processing and computing systems.

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Modeling Molecules for Field-Coupled Nanocomputing Circuit Design

Ardesi,

Ravera,

Piccinini

et al. 2024

2024 IEEE 24th International Conference on Nanotechnology (NANO)

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Robustness of the In-Plane Data Crossing for Molecular Field-Coupled Nanocomputing

Cited by 3 publications

References 23 publications

Addressing multi-molecule field-coupled nanocomputing for neural networks with SCERPA

Addressing multi-molecule field-coupled nanocomputing for neural networks with SCERPA

Implementation of adders using XOR gates in quantum-dot cellular automata with physical verification

Modeling Molecules for Field-Coupled Nanocomputing Circuit Design

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