Authors
Qunrui Deng, Wenjie Chen, Xueting Liu, Yiming Sun, Nengjie Huo
Published in
Advanced materials (Deerfield Beach, Fla.). Pages e73861. Jun 29, 2026. Epub Jun 29, 2026.
Abstract
The exploration of marine environments is crucial, yet the extreme conditions of the deep-sea, combined with the segregated signal processing in current sensor technologies, lead to bulky systems, high energy consumption, and significant latency, which severely constrains the development of real-time intelligent perception systems underwater. Herein, we developed a neuromorphic floating-gate transistor (NFT) that integrates both electrical and optical memory functionalities, emulating simultaneously visual and auditory synaptic behaviors within a single unit, thus enabling in-memory dual-mode processing of visual-auditory signals. Electrically, it achieves rapid switching (∼14 µs), high on/off ratio (106), and robust endurance (>104 cycles). This enables high-accuracy (88%) classification of seafloor minerals and rocks via sonar echo processing using a convolutional neural network (CNN). Optically, the NFT exhibits tunable synaptic weight modulation from short-term to long-term plasticity under 405-808 nm laser pulses. Leveraging the low-attenuation green-light window in seawater, the system, combined with RGB denoising and green-channel enhancement preprocessing, realizes 80% accuracy in marine biological image recognition. This synergistic electro-optical in-memory computing architecture provides an efficient, low-power, and compact hardware solution for intelligent perception in complex underwater environments.
PMID:
42366903
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.
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