Asymmetric Contact van der Waals Ferroelectric Transistors for Self-Powered Multifunctional Artificial Visual System

The development of self-powered artificial visual systems capable of emulating the multifunctionality of the human eye, such as light adaptation, optical memory, and in-sensor computing, is pivotal for next-generation intelligent bionic technologies. In this study, we present a van der Waals ferroelectric field-effect transistor (vdW-FeFET) based on an asymmetric contact α-In<sub>2</sub>Se<sub>3</sub>/h-BN/CIPS heterostructure, which operates entirely in a self-powered mode. By harnessing the intrinsic opto-ferroelectric coupling of α-In<sub>2</sub>Se<sub>3</sub> and the built-in electric fields induced by asymmetric source-drain contact areas, the device exhibits a range of neuromorphic visual behaviors, including visible light sensing and adaptation (405-660 nm), wavelength-dependent color differentiation, and robust optical memory capabilities. The combined effects of photopyroelectric and photothermoelectric mechanisms enable dynamic modulation of the photocurrent, facilitating visual adaptations analogous to biological systems. The device further demonstrates pronounced synaptic characteristics, such as a high paired-pulse facilitation index (∼180%) and memory consolidation from short- to long-term states under repetitive optical stimulation. In particular, pattern recognition is achieved without any external bias, showcasing the potential for autonomous visual signal processing. This work presents a multifunctional, energy-efficient platform for bioinspired vision systems, offering a promising pathway toward neuromorphic computing and next-generation optoelectronic intelligence.

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Publication Details

Journal
ACS Nano
Published
2025-09-25
DOI
https://doi.org/10.1021/acsnano.5c12376
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Asymmetric Contact van der Waals Ferroelectric Transistors for Self-Powered Multifunctional Artificial Visual System

Nayeong Lee, Rajiv K. Pandey, Sungjoo Lee, Sang‐Min Lee et al.
1 citations
ACS Nano
Advanced Memory and Neural Computing
0.73
article

Asymmetric Contact van der Waals Ferroelectric Transistors for Self-Powered Multifunctional Artificial Visual System

Nayeong Lee, Rajiv K. Pandey, Sungjoo Lee, Sang‐Min Lee, Sungpyo Baek
article en
1 citations

Abstract

The development of self-powered artificial visual systems capable of emulating the multifunctionality of the human eye, such as light adaptation, optical memory, and in-sensor computing, is pivotal for next-generation intelligent bionic technologies. In this study, we present a van der Waals ferroelectric field-effect transistor (vdW-FeFET) based on an asymmetric contact α-In<sub>2</sub>Se<sub>3</sub>/h-BN/CIPS heterostructure, which operates entirely in a self-powered mode. By harnessing the intrinsic opto-ferroelectric coupling of α-In<sub>2</sub>Se<sub>3</sub> and the built-in electric fields induced by asymmetric source-drain contact areas, the device exhibits a range of neuromorphic visual behaviors, including visible light sensing and adaptation (405-660 nm), wavelength-dependent color differentiation, and robust optical memory capabilities. The combined effects of photopyroelectric and photothermoelectric mechanisms enable dynamic modulation of the photocurrent, facilitating visual adaptations analogous to biological systems. The device further demonstrates pronounced synaptic characteristics, such as a high paired-pulse facilitation index (∼180%) and memory consolidation from short- to long-term states under repetitive optical stimulation. In particular, pattern recognition is achieved without any external bias, showcasing the potential for autonomous visual signal processing. This work presents a multifunctional, energy-efficient platform for bioinspired vision systems, offering a promising pathway toward neuromorphic computing and next-generation optoelectronic intelligence.

ACS NanoVol. 19(39)
Institute of Nanotechnology (GB), Sungkyunkwan University (KR)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
0.73
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