Ferroelectric-Based Optoelectronic Synapses for Visual Perception: From Materials to Systems.
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| Title: | Ferroelectric-Based Optoelectronic Synapses for Visual Perception: From Materials to Systems. |
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| Authors: | Hu, Yuqing1,2 (AUTHOR), Zhu, Yixin1,2 (AUTHOR), Chen, Xinli1 (AUTHOR), Wan, Qing1,2 (AUTHOR) qing-wan@ylab.ac.cn |
| Source: | Nanomaterials (2079-4991). Jun2025, Vol. 15 Issue 11, p863. 30p. |
| Subjects: | Ferroelectric materials, Optical information processing, Artificial intelligence, Biological systems, Visual fields |
| Abstract: | More than 70% of the information humans acquire from the external environment is derived through the visual system, where photosensitive function plays a pivotal role in the biological perception system. With the rapid development of artificial intelligence and robotics technology, achieving human-like visual perception has attracted a great amount of attention. The neuromorphic visual perception system provides a novel solution for achieving efficient and low-power visual information processing by simulating the working principle of the biological visual system. In recent years, ferroelectric materials have shown broad application prospects in the field of neuromorphic visual perception due to their unique spontaneous polarization characteristics and non-volatile response behavior under external field regulation. Especially in achieving tunable retinal neural synapses, visual information storage processing, and constructing dynamic visual sensing, ferroelectric materials have shown unique performance advantages. In this review, recent progress in neuromorphic visual perception based on ferroelectric materials is discussed, elaborating in detail on device structure, material systems, and applications, and exploring the potential future development trends and challenges faced in this field. [ABSTRACT FROM AUTHOR] |
| Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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