Multi-Molecule Field-Coupled Nanocomputing for the Implementation of a Neuron

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

doi:10.1109/tnano.2022.3143720

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…In this scenario, among the beyond-CMOS possibilities, the molecular electronics is promising for its potentially massive scalability and the contained power dissipation [7]- [9]. However, even if several computing device prototypes were proposed over the years, there is still a lack of single-molecule devices for memory applications [10].…”

Section: Introductionmentioning

confidence: 99%

Electronic Transport Study of Bistable Cr@C28 Single Molecule Device for High-Density Data Storage Applications

Spano¹,

Mo²,

Ardesi³

et al. 2022

Proceedings of the 8th World Congress on New Technologies

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Section: Introductionmentioning

confidence: 99%

Electronic Transport Study of Bistable Cr@C28 Single Molecule Device for High-Density Data Storage Applications

Spano¹,

Mo²,

Ardesi³

et al. 2022

Proceedings of the 8th World Congress on New Technologies

Self Cite

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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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Versatile Signal Distribution Networks for Scalable Placement and Routing of Field-coupled Nanocomputing Technologies

Walter,

Hien,

Wille

2023

2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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Multi-Molecule Field-Coupled Nanocomputing for the Implementation of a Neuron

Cited by 5 publications

References 26 publications

Electronic Transport Study of Bistable Cr@C28 Single Molecule Device for High-Density Data Storage Applications

Electronic Transport Study of Bistable Cr@C28 Single Molecule Device for High-Density Data Storage Applications

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

Versatile Signal Distribution Networks for Scalable Placement and Routing of Field-coupled Nanocomputing Technologies

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