Bioinspired Tunable‐Tunneling Heterostructure for Dynamic Grayscale Perception and Spike Encoding

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Authors: Xuhao Fan, Shian Mi, Xiaohan Wei, Fengyi Zhu, Sheng Ni, Chongyu Li, 茹文君, Yuhang Ma, Changyi Pan, Haibiao Guan, Weiwei Tang, Songyuan Ding
+5 more Haibo Shu, Yi Zhou, Guanhai Li, Changlong Liu, Xiaoshuang Chen
Cited by: 1
References: 17

Abstract

ABSTRACT Bionic vision systems employ spike‐based neural encoding—inspired by biological vision—to achieve high perceptual efficiency and adaptability. However, most current implementations commonly rely on carrier relaxation, charge trapping/detrapping, or ion migration processes along with peripheral circuits to emulate neural dynamics, which limit response speed, increase energy consumption, and hinder efficient device‐level spike encoding. Here, we demonstrate a bioinspired tunneling photodetector using an SnSe 2 /MLG/WS 2 junction, in which an electrically triggered switch from direct tunneling (DT) to Fowler–Nordheim tunneling (FNT) enables optical‐power‐dependent grayscale resolution and spiking‐encoded output within a single structure. By leveraging tunable tunneling transports for efficient photocarrier collection, the device achieves a responsivity of ≈233 A/W and a response time of ≈97 µs at 638 nm. We further demonstrate the device's capability for light‐intensity encoding and dynamic perception through grayscale image transmission. Specifically, spike signals encoded via the nonlinear photocurrent response allow a trained spiking neural network (SNN) to achieve 96.5% accuracy in grayscale letter recognition. Our results demonstrate that the tunable tunneling‐encoding strategy provides a practical pathway toward compact bionic visual sensors capable of grayscale imaging in neuromorphic vision front‐ends.

Fields of Study & Subjects

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

Publication details

Journal / source
Advanced Functional Materials
Publisher
Wiley
Volume
36
Issue
49
Publication date
15 May 2026
Language
EN