Artificial synapses based on CIPS/Te vdW heterojunction ferroelectric transistor for traffic light recognition

Neuromorphic computing is a key technology for simulating brain function and plays a crucial role in the next-generation computing, offering a potential solution to the challenges posed by the von Neumann bottleneck. Tellurium (Te) and CuInP2S6 (CIPS), as two-dimensional (2D) materials with excellent properties, have been widely used in advanced electronics and optoelectronics. However, the combination of the stable ferroelectricity of CIPS and the high current characteristics of Te, which both electrical and optical stimuli can modulate, offers great potential for addressing complex application scenarios, yet this type of 2D van der Waals (vdW) device has been largely unexplored. In this study, we developed an optoelectronic neuromorphic device based on CIPS and Te, which exhibits fundamental synaptic behaviors in response to electrical stimulation and demonstrates different current responses under light of varying wavelengths. Additionally, we constructed an RC system based on this device to address the problem of traffic light recognition. In this system, the ferroelectric modulation of CIPS by voltage enables short-term depression (STD) to simulate human braking behavior in response to dangerous signals. This approach effectively enhances the response capabilities of intelligent traffic systems to traffic signals, offering significant application potential.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2025-05-26
DOI
https://doi.org/10.1063/5.0256495
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial synapses based on CIPS/Te vdW heterojunction ferroelectric transistor for traffic light recognition

Yiming Sun, Gang Liu, Nengjie Huo, Zhimin Wang et al.
3 citations
Applied Physics Letters
Advanced Memory and Neural Computing
2.19
article

Artificial synapses based on CIPS/Te vdW heterojunction ferroelectric transistor for traffic light recognition

Yiming Sun, Gang Liu, Nengjie Huo, Zhimin Wang, Weidong Song, Wei Gao, Yifan Zhang, Yuhan Zhang, Zhen Wang, Xinyi Chen, Yiming Zhao, Jing Wang, Mengmeng Yang, Fei Li
article en
3 citations

Abstract

Neuromorphic computing is a key technology for simulating brain function and plays a crucial role in the next-generation computing, offering a potential solution to the challenges posed by the von Neumann bottleneck. Tellurium (Te) and CuInP2S6 (CIPS), as two-dimensional (2D) materials with excellent properties, have been widely used in advanced electronics and optoelectronics. However, the combination of the stable ferroelectricity of CIPS and the high current characteristics of Te, which both electrical and optical stimuli can modulate, offers great potential for addressing complex application scenarios, yet this type of 2D van der Waals (vdW) device has been largely unexplored. In this study, we developed an optoelectronic neuromorphic device based on CIPS and Te, which exhibits fundamental synaptic behaviors in response to electrical stimulation and demonstrates different current responses under light of varying wavelengths. Additionally, we constructed an RC system based on this device to address the problem of traffic light recognition. In this system, the ferroelectric modulation of CIPS by voltage enables short-term depression (STD) to simulate human braking behavior in response to dangerous signals. This approach effectively enhances the response capabilities of intelligent traffic systems to traffic signals, offering significant application potential.

Applied Physics LettersVol. 126(21)
South China Normal University (CN), Wuyi University (CN)
China Postdoctoral Science Foundation, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
2.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.