Plasmon-enhanced Sb2Te3/FTO heterojunction optoelectronic synapses for image compression encoding and high-accuracy recognition
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.
Authors
- Zhenhua Wu (ORCID: https://orcid.org/0000-0002-5527-0310)
- Miṅ Gu (ORCID: https://orcid.org/0000-0003-4078-253X)
- Zhiyong Yu (ORCID: https://orcid.org/0000-0002-7463-0473)
- Zengji Yue (ORCID: https://orcid.org/0000-0001-5917-5057)
- Boyuan Cai (ORCID: https://orcid.org/0000-0003-4909-4055)
- Xiaotian Zhang (ORCID: https://orcid.org/0000-0002-1571-1257)
- Bixuan Huang (ORCID: https://orcid.org/0000-0003-3204-1934)
- Yutian Wang (ORCID: https://orcid.org/0009-0006-1798-0393)
- Xia Wan (ORCID: https://orcid.org/0009-0004-2612-0817)
- Junwei Fu (ORCID: https://orcid.org/0009-0004-9250-1562)
Institutions
- Jiujiang University (CN)
- University of Shanghai for Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1063/5.0304300
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00