Solar-blind SnO2 nanowire photo-synapses for associative learning and coincidence detection

Abstract With the rapid development of artificial intelligence, memristive devices capable of detecting, processing, and memorizing deep-ultraviolet (DUV) signals are very promising for encoding, recognizing, and performing solar-blind sensitive tasks. Here, we propose a DUV-triggered SnO 2 nanowire synaptic transistor in which the programmable persistent photoconductivity (PPC) effect is used to mimic essential bio-synaptic behaviors. The bio-synapse-like behaviors, such as excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), and spike-timing dependence plasticity (STDP) (named the first law of synaptic plasticity) were investigated and imitated using various gate voltages. Larger gate bias permits a more significant transition from short-term (STP) to long-term plasticity (LTP). A detailed theoretical model is presented herein based on results from a systematic analysis of optoelectronic synaptic performances. Importantly, Pavlov's learning (classical conditioning) was conceptually demonstrated through DUV photoinduction. We also observed a novel situation where the memory effect was slightly inhibited when the devices were simultaneously stimulated by optical and electric pulses, allowing detection of temporally close, spatially distributed DUV incident signals. This approach opens up a new application domain for ultrafast, robust, and adaptive processing in future optoelectronic neural systems.