CuInP <sub>2</sub> S <sub>6</sub> Heterojunction Based Visible Range Optoelectronic Synapse With Femtojoule Energy Consumption

Abstract The 2D van der Waals material CuInP 2 S 6 , characterized by its memory behavior arising from room‐temperature ferroelectricity and Cu + ions migration, has emerged as a promising candidate material for artificial synaptic devices. Nevertheless, with a bandgap of 2.7 eV, CIPS‐based devices are generally limited to operating in pure electrical mode or under ultraviolet light, making them unsuitable for applications across the entire visible light spectrum. Here, a two‐terminal artificial synapse based on CIPS/MoS 2 /graphene heterojunction is constructed. Compared to ion migration or ferroelectricity under high bias voltage, photogating due to charge trapping is identified as the working mechanism under low bias voltage (&lt; 1.5 V), which can respond to the shortest pulse (∼5 ms) and least energy consumption of 1.7 / 6.3 fJ per pulse up to date for CIPS‐based synapses. Benefiting from the fading memory effect and nonlinear characteristics in visible light range, handwritten digit recognition based on reservoir computing has achieved an accuracy of 90.43% with four times higher efficiency than directly using an artificial neuron network. This work thus paves the way for constructing CIPS heterostructure for artificial vision and neuromorphic computing systems.

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Publication Details

Journal
Advanced Materials Technologies
Published
2024-12-31
DOI
https://doi.org/10.1002/admt.202401589
Citations
6
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.08

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article

CuInP 2 S 6 Heterojunction Based Visible Range Optoelectronic Synapse With Femtojoule Energy Consumption

Zichen Wang, Xinyi Fan, Linjun Li, Wei Tang et al.
6 citations
Advanced Materials Technologies
Neural Networks and Reservoir Computing
2.08
article

CuInP 2 S 6 Heterojunction Based Visible Range Optoelectronic Synapse With Femtojoule Energy Consumption

Zichen Wang, Xinyi Fan, Linjun Li, Wei Tang, Huanfeng Zhu, Jialin Li
article en
6 citations

Abstract

Abstract The 2D van der Waals material CuInP 2 S 6 , characterized by its memory behavior arising from room‐temperature ferroelectricity and Cu + ions migration, has emerged as a promising candidate material for artificial synaptic devices. Nevertheless, with a bandgap of 2.7 eV, CIPS‐based devices are generally limited to operating in pure electrical mode or under ultraviolet light, making them unsuitable for applications across the entire visible light spectrum. Here, a two‐terminal artificial synapse based on CIPS/MoS 2 /graphene heterojunction is constructed. Compared to ion migration or ferroelectricity under high bias voltage, photogating due to charge trapping is identified as the working mechanism under low bias voltage (&lt; 1.5 V), which can respond to the shortest pulse (∼5 ms) and least energy consumption of 1.7 / 6.3 fJ per pulse up to date for CIPS‐based synapses. Benefiting from the fading memory effect and nonlinear characteristics in visible light range, handwritten digit recognition based on reservoir computing has achieved an accuracy of 90.43% with four times higher efficiency than directly using an artificial neuron network. This work thus paves the way for constructing CIPS heterostructure for artificial vision and neuromorphic computing systems.

Advanced Materials TechnologiesVol. 10(9)
Jiaxing University (CN), University of Chinese Academy of Sciences (CN), Zhejiang University (CN)
National Science Foundation, National Natural Science Foundation of China, National Key Research and Development Program of China, Natural Science Foundation of Zhejiang Province
Affordable and clean energy
Openalex Percentile: Top 11%
Neural Networks and Reservoir Computing
2.08
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