Highly uniform organic nanowire synaptic arrays with excellent performance for associative memory

Abstract

Synaptic devices of optoelectronic nature, which combine light-detection and data-storage capabilities, hold significant promise in the realm of neuromorphic computing. They are especially beneficial for the processing of visual data and the execution of intricate cognitive functions akin to learning, memory retention, and logical reasoning. However, current research is mostly confined to the level of individual devices, with corresponding studies on synaptic arrays being relatively scarce. Type II heterojunctions are widely used in optoelectronics. Bandgap engineering enables efficient separation and trapping of photogenerated excitons in selected materials, reducing carrier recombination. This prolongs carrier retention in the channel, delaying photocurrent decay, crucial for artificial optoelectronic synapses. We designed an organic nanowire/perovskite Type II heterojunction where CsPbBr3 perovskite films and TIPS nanowires serve as the photosensitive and channel layers, respectively. The composite synaptic array was successfully fabricated by in-situ growth of TIPS nanowires on the surface of perovskite thin films. It was further used to fabricate a photonic synapse array that exhibits characteristic photocurrent and stable optical response. Achieving high-performance photonic synapses was facilitated by characteristics such as high mobility and high on/off ratios, stemming from the formation of complete single-crystal nanowires on the perovskite films. Exhibiting synaptic behaviors such as photo-induced enhancements and paired-pulse facilitation, the device enabled a visual sensing system with a 5 × 5 pixel array for simulating memory processes and restoring associative memories. A novel photocurrent model was established for understanding device characteristics. This work provides valuable insights for future utilization of composite organic nanowire arrays in simulating brain-like computations and realizing large-scale intelligent visual sensing systems.