Self‐Adaptive TiO 2 Optoelectronic Synapses via Microsecond–Nanosecond Pulse Modulation for Image Denoising

This work employs optical pulses spanning microsecond to nanosecond durations, demonstrating pulse‐length‐dependent photocurrent plasticity in a self‐adaptive manner. Learn‐forget cycles are realized through two distinct synaptic behaviors: depression and potentiation. Both are exploited for in‐sensor denoising of digital images, enhancing recognition accuracy from 92% to 96% and from 93% to 96%, respectively. This research provides an optoelectronic device scheme for high‐speed, low‐power neuromorphic vision systems.

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Publication Details

Journal
physica status solidi (a)
Published
2026-09-01
DOI
https://doi.org/10.1002/pssa.70516
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Self‐Adaptive TiO 2 Optoelectronic Synapses via Microsecond–Nanosecond Pulse Modulation for Image Denoising

T. Wágner, Guangyu Wen, Petr Janíček, Bo Zhang et al.
physica status solidi (a)
Neural Networks and Reservoir Computing
article

Self‐Adaptive TiO 2 Optoelectronic Synapses via Microsecond–Nanosecond Pulse Modulation for Image Denoising

T. Wágner, Guangyu Wen, Petr Janíček, Bo Zhang, Xu Zhao, Wenqiang Dong
article en

Abstract

This work employs optical pulses spanning microsecond to nanosecond durations, demonstrating pulse‐length‐dependent photocurrent plasticity in a self‐adaptive manner. Learn‐forget cycles are realized through two distinct synaptic behaviors: depression and potentiation. Both are exploited for in‐sensor denoising of digital images, enhancing recognition accuracy from 92% to 96% and from 93% to 96%, respectively. This research provides an optoelectronic device scheme for high‐speed, low‐power neuromorphic vision systems.

physica status solidi (a)Vol. 223(17)
University of Pardubice (CZ), Hebei Normal University (CN)
Hebei Normal University
Openalex Percentile: Top 8%
Neural Networks and Reservoir Computing
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