Bio-Inspired Ferroelectric Adaptive Transistors for Intelligent Vision Systems
To address the challenges of low data processing efficiency, spatiotemporal information separation, and high energy consumption in traditional machine vision systems, this work proposes a bio-inspired ferroelectric adaptive transistor based on annealing-free HZO ferroelectric films. The FeTFT achieves the lowest fabrication temperature while demonstrating exceptional performance with a high ON/OFF ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3}.{8}\times {10} ^{{9}}$ </tex-math></inline-formula> and a large memory window of 2.86 V. The FeTFT exhibits bio-synaptic optoelectronic co-response characteristics and implements biological adaptive functions through dynamic reconfiguration of ferroelectric polarization. By constructing a fire vision system based on the spatiotemporal fusion mechanism, the FeTFT achieves 100% motion direction recognition accuracy and three-level speed classification capability. This research establishes a novel paradigm for developing low-power, dynamically adaptable bio-inspired intelligent vision systems.
Authors
- Jiajie Yu
- Jialin Meng (ORCID: https://orcid.org/0000-0002-4622-833X)
- Hao Zhu (ORCID: https://orcid.org/0000-0003-3890-6871)
- Anna Yuan (ORCID: https://orcid.org/0000-0001-5619-5175)
- David Wei Zhang (ORCID: https://orcid.org/0000-0002-7510-140X)
- Zhenhai Li (ORCID: https://orcid.org/0000-0001-9878-3274)
- Zhe Qu (ORCID: https://orcid.org/0000-0001-8568-0460)
- Pei Liu (ORCID: https://orcid.org/0000-0002-5454-9651)
- Tianyu Wang (ORCID: https://orcid.org/0000-0002-9829-5964)
- Qingqing Sun (ORCID: https://orcid.org/0000-0002-6533-1834)
- Lin Chen (ORCID: https://orcid.org/0000-0002-7145-7564)
- Yongkai Liu (ORCID: https://orcid.org/0000-0002-3704-4897)
- Kangli Xu (ORCID: https://orcid.org/0000-0002-8077-3593)
- Ruihong Yuan (ORCID: https://orcid.org/0009-0003-7846-6951)
Institutions
- Shandong University (CN)
- Institute of Microelectronics (CN)
- Shanghai Fudan Microelectronics (China) (CN)
Publication Details
- Journal
- IEEE Electron Device Letters
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1109/led.2025.3597286
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- Taishan Scholar Foundation of Shandong Province
- National Key Research and Development Program of China