Plasmon-enhanced Sb2Te3/FTO heterojunction optoelectronic synapses for image compression encoding and high-accuracy recognition

Z Zhiyong Yu (State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering) Z Zhenhua Wu W Wan Xia (School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,) Y Yutian Wang (School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,) B Bixuan Huang (School of Mechanical Engineering, Shanghai Jiao Tong University 4 , Shanghai 200240,) X Xiaotian Zhang J Junwei Fu (Hunan Joint International Research Center for Carbon Dioxide Resource Utilization, School of Physics) Z Zengji Yue (School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,) M Min Gu B Boyuan Cai (School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,)

Abstract

Broadband, energy-efficient optoelectronic synaptic devices are essential for neuromorphic computing systems and embodied intelligent perception. Here, we present a plasmon-enhanced optoelectronic synapse based on an Ag nanoparticle-Sb2Te3/FTO (fluorine-doped tin oxide) heterojunction. The incorporation of Ag and FTO enhances light absorption in Sb2Te3 across a broad spectral range, while the localized surface plasmon resonance effect of the Ag nanoparticles and the p-Sb2Te3/n-FTO heterojunction improves the generation efficiency and subsequent separation and transport of photocarriers, resulting in a significantly enhanced photocurrent response. The device exhibits tunable photo-responsive memristive behavior and multilevel optoelectronic synaptic plasticity even at a low voltage of 1 V. Furthermore, we demonstrate compressed image encoding and recognition using two artificial neural networks, achieving an average accuracy of ∼95%. This work not only provides a strategy for designing high-performance, broadband, low-power optoelectronic synapses but also offers a viable device foundation for image compression encoding and intelligent visual perception.

Article Details

Volume / Issue Vol. 127, Issue 22
Published December 01, 2025
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (10)

Z

Zhiyong Yu

State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering

Z

Zhenhua Wu

W

Wan Xia

School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,

Y

Yutian Wang

School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,

B

Bixuan Huang

School of Mechanical Engineering, Shanghai Jiao Tong University 4 , Shanghai 200240,

X

Xiaotian Zhang

J

Junwei Fu

Hunan Joint International Research Center for Carbon Dioxide Resource Utilization, School of Physics

Z

Zengji Yue

School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,

M

Min Gu

B

Boyuan Cai

School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology 1 , Shanghai 200093,