Ultra‐Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe <sub>2</sub> for Neuromorphic Computing

Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe<sub>2</sub>) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.

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Publication Details

Journal
Small
Published
2024-10-21
DOI
https://doi.org/10.1002/smll.202406402
Citations
17
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.55

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article

Ultra‐Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe 2 for Neuromorphic Computing

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17 citations
Small
Advanced Memory and Neural Computing
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article

Ultra‐Low Power Consumption Artificial Photoelectric Synapses Based on Lewis Acid Doped WSe 2 for Neuromorphic Computing

Xiangheng Xiao, Yongfeng Pei, Cheng Lei, Chaoning Huang, Wenqing Li, Yufan Kang, Dong He, Mingjun Ma, Mingyu Yang
article en
17 citations

Abstract

Capitalizing on the extensive spectral capacity and minimal crosstalk properties inherent in optical signals, photoelectric synapses are poised to assume a pivotal stance in the realm of neuromorphic computation. Herein, a photoelectric synapse based on Lewis acid-doped semiconducting tungsten diselenide (WSe<sub>2</sub>) is introduced, exhibiting tunable short-term and long-term plasticity. The device consumes a mere 0.1 fJ per synaptic operation, which is lower than the energy required by a single synaptic event observed in the human brain. Furthermore, these devices demonstrate high-pass filtering capabilities, highlighting their potential in image-sharpening applications. In particular, by synergistically modulating the photoconductivity and electrical gate bias, versatile logic capabilities are demonstrated within a single device, enabling it to flexibly perform both Boolean AND and OR gate operations. This work demonstrates a viable approach for Lewis acid-treated TMDs to realize multifunctional photoelectric synapses for neuromorphic computing.

SmallVol. 20(51)
Wuhan University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.55
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