Synaptic κ‐Ga ₂ O ₃ Photodetectors for Privacy‐Enhancing Neuromorphic Computing

ABSTRACT 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‐Ga ₂ O ₃ ) 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‐Ga ₂ O ₃ synaptic photodetectors establish a materials‐enabled pathway toward compact, intelligent, and privacy‐enhancing optoelectronic hardware for next‐generation edge systems.