Photosensing PUF from an Intrinsically Random SnTe Memristor for Image Encryption and Recognition

Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In2O3:SnO2/SnTe/Nb:SrTiO3structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation.