Two-Dimensional InSe Artificial Synapses with fJ Energy Consumption

As Moore’s Law approaches its physical limits, the traditional von Neumann architecture faces challenges, and memristors offer hope as a new type of device structure. In this study, we realized the ultralow energy consumption (1.1 fJ) artificial synapse based on 2D InSe with small effective electron mass and high electron mobility. Furthermore, we simulated the synaptic behaviors of post-tetanic potentiation, classical conditioning, spike-rate-dependent plasticity (SRDP), and Bienenstock–Cooper–Munro (BCM) learning rule. This study not only provides a simulation of synaptic behavior but also achieves edge recognition in images through an effective adjustability.

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Publication Details

Journal
ACS Applied Energy Materials
Published
2025-05-07
DOI
https://doi.org/10.1021/acsaem.4c03324
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.65
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article

Two-Dimensional InSe Artificial Synapses with fJ Energy Consumption

Caihong Jia, Weifeng Zhang, Kun Zhang, Na Xu et al.
5 citations
ACS Applied Energy Materials
Advanced Memory and Neural Computing
3.65
article

Two-Dimensional InSe Artificial Synapses with fJ Energy Consumption

Caihong Jia, Weifeng Zhang, Kun Zhang, Na Xu, Mengyuan Duan
article en
5 citations

Abstract

As Moore’s Law approaches its physical limits, the traditional von Neumann architecture faces challenges, and memristors offer hope as a new type of device structure. In this study, we realized the ultralow energy consumption (1.1 fJ) artificial synapse based on 2D InSe with small effective electron mass and high electron mobility. Furthermore, we simulated the synaptic behaviors of post-tetanic potentiation, classical conditioning, spike-rate-dependent plasticity (SRDP), and Bienenstock–Cooper–Munro (BCM) learning rule. This study not only provides a simulation of synaptic behavior but also achieves edge recognition in images through an effective adjustability.

ACS Applied Energy MaterialsVol. 8(10)
Henan University (CN), Quantum (Australia) (AU), Henan Academy of Sciences (CN)
Affordable and clean energy
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.65
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