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Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

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Artificial Solar-Blind Optosynapses Using Amorphous Gallium Oxide Phototransistors for Optical In-Sensor Neuromorphic

Yong Zhang¹, Kevin Chang¹, Huilong Yan¹

¹Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States.

|October 1, 2025

Summary

This summary is machine-generated.

Researchers developed novel solar-blind optoelectronic synaptic devices using amorphous gallium oxide thin-film transistors. These devices achieve high pattern recognition rates for artificial intelligence, overcoming key limitations in current neuromorphic computing systems.

Keywords:

amorphous oxide semiconductor artificial optosynaptic device dynamic optical gain gallium oxide phototransistor solar-blind photodetector thin-film transistor

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Area of Science:

Materials Science
Neuroscience
Computer Science

Background:

Optoelectronic neuromorphic devices are crucial for energy-efficient AI computing.
Current devices face challenges like limited dynamic range, low photogain, and poor spectral selectivity.
Overcoming these limitations is key for advancing AI systems.

Purpose of the Study:

To develop artificial solar-blind optoelectronic synaptic devices.
To enhance pattern recognition capabilities in neural network training.
To address limitations in existing optosynaptic devices.

Main Methods:

Utilized ultrawide-bandgap amorphous gallium oxide (a-GaOx) thin-film transistors (TFTs).
Employed deep ultraviolet (DUV) optically induced potentiation and electrical depression for synaptic weight updates.
Investigated device performance including plasticity, conductance weight update range, and photoresponse.

Main Results:

Achieved high pattern recognition rates (>92%) in neural network training.
Demonstrated superior TFT switching characteristics and a dynamic gain exceeding 10^8.
Exhibited UV-triggered persistent photoconductivity (PPC) lasting over 1000 s.
Device fabrication is compatible with CMOS back-end-of-line processes at 450 °C.

Conclusions:

The developed a-GaOx TFT-based devices show significant promise for next-generation AI applications.
The devices overcome critical challenges in dynamic range, photogain, and spectral selectivity.
Low-temperature fabrication enables integration with existing semiconductor manufacturing processes.