All‐Metal‐Oxide Heterojunction Optoelectronic Synapses with Multilevel Memory for Artificial Visual Perception Applications
Metal-oxide semiconductor-based optoelectronic synaptic transistors have attracted considerable attention due to their high energy efficiency and stability. This work proposes a novel WO<sub>3</sub>/InWZnO heterojunction optoelectronic synaptic transistor, demonstrating the strong potential for emulating the human visual system. The fabricated heterojunction synaptic transistor achieves an impressive optical responsivity of 58.37 A W<sup>-1</sup> when exposed to 650 nm light. Furthermore, it successfully emulates transitions from short-term memory (STM) to long-term memory (LTM) by modulating pulse duration, illumination intensity, and pulse number. The heterojunction transistor also exhibits an optimal paired-pulse facilitation (PPF) index of 176% and post-tetanic potentiation (PTP) index of 890% under 460 nm light illumination. It also demonstrates long-term multilevel storage capability via the photogating effect. A multilayer perceptron (MLP) model, employing synaptic weights modulated by 650, 525, and 460 nm light in the long-term potentiation (LTP) region and by electrical stimuli in the long-term depression (LTD) region, achieves a high recognition accuracy of 87.4% for severely distorted handwritten digits. Finally, the U-Net architecture is adopted to evaluate the image segmentation performance through RGB channels, revealing an optimal accuracy of 74.5%, demonstrating the feasibility of the proposed WO<sub>3</sub>/InWZnO heterojunction synaptic transistor for advanced neuromorphic vision system applications.
Authors
- Po‐Tsun Liu (ORCID: https://orcid.org/0000-0001-8560-3668)
- Jo‐Lin Chen (ORCID: https://orcid.org/0000-0002-9449-5176)
- Tsung‐Che Chiang (ORCID: https://orcid.org/0000-0002-0722-8214)
Institutions
- National Yang Ming Chiao Tung University (TW)
Publication Details
- Journal
- Small
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1002/smll.202502271
- Citations
- 8
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 5.84
Funders
- National Science and Technology Council