Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF<sub>3</sub>: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF<sub>3</sub>: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

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Publication Details

Journal
The Journal of Physical Chemistry Letters
Published
2024-08-21
DOI
https://doi.org/10.1021/acs.jpclett.4c02238
Citations
12
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.50

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article

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction

Yan Cao, Shuangmei Xue, Su‐Ting Han, Kui Zhou et al.
12 citations
The Journal of Physical Chemistry Letters
Advanced Memory and Neural Computing
2.50
article

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction

Yan Cao, Shuangmei Xue, Su‐Ting Han, Kui Zhou, Yongbiao Zhai, Chi‐Ching Kuo, Guanglong Ding, Ziyu Lv, Yan Yan, Hang Li, Ye Bo Zhou, Wanhong Luan, Zherui Zhao, Meng Zhang
article en
12 citations

Abstract

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF<sub>3</sub>: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF<sub>3</sub>: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

The Journal of Physical Chemistry LettersVol. 15(34)
National Taipei University of Technology (TW), Hong Kong Polytechnic University (HK), National Taiwan University (TW), Shenzhen University (CN), Guangdong Province Special Equipment Testing and Research Institute Zhuhai Testing Institute (CN), South China University of Technology (CN)
Royal Society of Chemistry, National Natural Science Foundation of China, National Taipei University of Technology, Shenzhen University, Science, Technology and Innovation Commission of Shenzhen Municipality, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.50
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