Interface‐Defect Synergy Engineering of Amorphous Ga 2 O 3 for Voltage‐Tunable Dual‐Mode Optoelectronic Devices: Self‐Powered Imaging and Neuromorphic Vision
Year: 2026
Type: article
Abstract: ABSTRACT Integrating disparate optoelectronic functionalities—ranging from self‐powered photodetection to neuromorphic computing—within a unified, single‐material architecture is pivotal for the next generation of bias‐programmable intelligent vision systems. Here, we report a voltage‐tunable, monolithic platform based on amorphous Ga 2 O 3 (ITO/GaO x /Al) that achieves bias‐programmable dual‐mode operation, uniting self‐powered solar‐blind photodetection with low‐power optoelectronic synaptic functionalities. The dual‐modality originates from the synergistic coupling between asymmetric interfacial potential barriers and oxygen‐vacancy engineering, which is in situ induced by interfacial redox reactions at the GaO x /Al junction. Benefiting from this precise interface‐defect co‐engineering, the device exhibits exceptional self‐powered solar‐blind detection with high responsivity, a rapid response of ∼100 µs, and a detection limit down to 13 nW. Under a low bias, it emulates bio‐realistic synaptic dynamics—including paired‐pulse facilitation and learning–forgetting–relearning processes—with an ultralow energy footprint of 18 pJ. Reversible voltage modulation enables both single‐pixel and 10 × 10 array devices to bridge the gap between high‐contrast transient imaging and long‐term visual memory. Moreover, a neuromorphic visual system constructed from this platform performs robust “detection–storage–recognition” tasks even under external noise. This study establishes a transformative route for bias‐programmable optoelectronic integration, effectively uniting sensing and cognitive functions within a simplified, high‐performance single‐material system.
Source: Advanced Functional Materials
Language: English
Topic: Ga2O3 and related materials
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,790
Awards: G4302998416, G5250503331, G6558714528
OpenAlex ID: W7170477995
