Synaptic κ-Ga₂O₃ Photodetectors for Privacy-Enhancing Neuromorphic Computing

Optoelectronic devices that unify sensing, memory, and computation offer a promising route toward intelligent and data-local edge systems. Here, a multifunctional metal-semiconductor-metal neuromorphic photodetector based on the persistent photoconductivity (PPC) of κ-phase gallium oxide (κ-Ga₂O₃) is reported, enabling in-sensor information processing and long-term state retention within a single device element. Owing to pronounced PPC effect, nominally identical devices exhibit reproducible yet device-distinguishable temporal photocurrent responses. These responses are exploited for hardware-level authentication using a hybrid 1D deep embedding network, which achieves robust cross-cycle verification performance with an Area Under the Curve (AUC) of about 0.97 and an Equal Error Rate (EER) of about 9%. Beyond authentication, the neuromorphic inference capability of the devices is evaluated using a hardware-aware simulation framework, in which experimentally extracted conductance states are mapped to a quantization-aware trained (QAT) artificial neural network (ANN) with 16 discrete levels. The quantized network achieves 98.17% accuracy and is subsequently converted into a leaky integrate-and-fire (LIF) spiking neural network (SNN), retaining 96.80% accuracy under device-constrained operation. By performing sensing, authentication, and inference at device level, the κ-Ga₂O₃ synaptic photodetectors establish a materials-enabled pathway toward compact, intelligent, and privacy-enhancing optoelectronic hardware for next-generation edge systems.