A Model for the Evaluation of Monostable Molecule Signal Energy in Molecular Field-Coupled Nanocomputing

Author:

Ardesi YuriORCID,Graziano MariagraziaORCID,Piccinini GianlucaORCID

Abstract

Molecular Field-Coupled Nanocomputing (FCN) is a computational paradigm promising high-frequency information elaboration at ambient temperature. This work proposes a model to evaluate the signal energy involved in propagating and elaborating the information. It splits the evaluation into several energy contributions calculated with closed-form expressions without computationally expensive calculation. The essential features of the 1,4-diallylbutane cation are evaluated with Density Functional Theory (DFT) and used in the model to evaluate circuit energy. This model enables understanding the information propagation mechanism in the FCN paradigm based on monostable molecules. We use the model to verify the bistable factor theory, describing the information propagation in molecular FCN based on monostable molecules, analyzed so far only from an electrostatic standpoint. Finally, the model is integrated into the SCERPA tool and used to quantify the information encoding stability and possible memory effects. The obtained results are consistent with state-of-the-art considerations and comparable with DFT calculation.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unveiling field-coupled nanocomputing: Leaning molecules to shape readable bits;Nano Research;2024-07-12

2. Unveiling Charge Dynamics in Molecular Field-Coupled Nanocomputing;2024 IEEE 24th International Conference on Nanotechnology (NANO);2024-07-08

3. Modeling Molecules for Field-Coupled Nanocomputing Circuit Design;2024 IEEE 24th International Conference on Nanotechnology (NANO);2024-07-08

4. Comprehensive and Comparative Analysis of QCA-based Circuit Designs for Next-generation Computation;ACM Computing Surveys;2023-11-25

5. Conformation-based Molecular Memories for Nanoscale MemComputing;2023 IEEE 23rd International Conference on Nanotechnology (NANO);2023-07-02

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