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

Optoelectronic neuromorphic devices, which mimic the functionalities of the human eye and brain neural systems, have attracted significant interest for enabling highly energy-efficient computing systems for next-generation artificial intelligence applications. However, several key challenges persist, including a limited dynamic range for light-induced synaptic weights, low optical photogain, lack of spectral selectivity, and incompatibility with heterogeneous integration. Addressing these issues is essential for unlocking the full potential of optosynaptic devices in advanced AI systems. In this work, we develop artificial solar-blind optoelectronic synaptic devices exhibiting high pattern recognition rates (>92%) in neural network training using ultrawide-bandgap amorphous gallium oxide (a-GaO x ) thin-film transistors (TFTs). The device functions through deep ultraviolet (DUV) optically induced potentiation and gate-terminal electrical depression processes, exhibiting excellent plasticity and a wide conductance weight update range. This performance is attributed to its superior TFT switching characteristics, strong DUV photoresponse with a dynamic gain exceeding 10 8, and UV-triggered persistent photoconductivity (PPC) lasting over 1000 s. Moreover, the device can be fabricated at a low temperature of 450 °C, ensuring compatibility with the complementary metal-oxide–semiconductor (CMOS) back-end-of-line (BEOL) process.