Owl-vision-inspired near sensor computing

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Authors: Zishen Zhao, Yixin Cao, Shuaiwei Huang, Kaiwen Fan, Yuyao Ding, Ganggui Zhu, Linhui Li, Qing Liu, Wei Deng, Chun Zhao, Mario Lanza
Cited by: 2
References: 38

Abstract

Passive target detection in photon-starved environments is crucial to expand machine vision in a wide range of applications such as precision guidance, intelligent surveillance, and early warning. Here, inspired by owl vision, we report a bimodal synaptic transistor with an optoelectronic decoupling mechanism, enabling parallel photonic perception and electrical plasticity emulation. As a result, the device exhibits a high active adaptation index of approximately 331, alongside the ability to perceive low light intensities as faint as 0.146 nW cm-2. We also achieve cyclically stable synaptic weight modulation with long-term enhancement and inhibition, and verify the feasibility of weight deployment across three basic artificial neural levels over a light intensity range of 0.146-11.70 nW cm-2, via adaptive contrast enhancement. The owl-vision-inspired device establishes a hardware foundation forward in energy-efficient and low-light image processing for neuromorphic vision sensors. Target detection at dark conditions remains a significant challenge. Zhao et al report an owl-vision-inspired dual-mode synaptic transistor, which enables parallel photonic perception and electrical plasticity emulation to achieve light sensitivity surpassing that of conventional CMOS-based vision systems.

Fields of Study & Subjects

Advanced Memory and Neural Computing Neural Networks and Reservoir Computing Ferroelectric and Negative Capacitance Devices

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
12 February 2026
Language
EN