Two-terminal β-Ga2O3 photo-synapse for diversified in-sensor computing via self-trapped holes engineering

Abstract The rapid advancement of artificial intelligence has propelled the development of β-Ga 2 O 3 photo-synapses for solar-blind ultraviolet neuromorphic machine vision systems. However, existing β-Ga 2 O 3 photo-synapses not only exhibit reduced stability but also display high weight update nonlinearity. Herein, we propose a novel strategy to construct β-Ga 2 O 3 photo-synapses with low weight update nonlinearity based on self-trapped holes, aiming to achieve multi-level in-sensor computing tasks. Theoretical and experimental investigations revealed that the interaction between the larger effective mass of holes and local lattice distortions in β-Ga 2 O 3 promoted the formation of self-trapped holes, which significantly reduced hole mobility and enhanced the persistent photocurrent effect. The fabricated β-Ga 2 O 3 photo-synapses exhibited excellent short-term plasticity, which could be transited to long-term plasticity by adjusting the characteristics of 252 nm ultraviolet light. Moreover, the devices achieved a low weight update nonlinearity of 0.42, outperforming most previously reported photo-synapses. Finally, β-Ga 2 O 3 photo-synapses were integrated into neuromorphic machine vision systems, enabling tasks ranging from low-level image classification to high-level motion recognition, achieving recognition accuracies of 99.48% and 92.70% on the MNIST and Fashion-MNIST datasets. It also maintained 100% target tracking accuracy under 60% Gaussian noise interference and reached a recognition accuracy of 94.94% for 10 motions in UTD-MHAD dataset. These results highlight great potential of β-Ga 2 O 3 photo-synapses based on self-trapped holes engineering in the era of artificial intelligence.