Self-rectifying NiO_X/WO_X heterojunction synaptic memristor for crossbar architectured reservoir computing system

In this study, we examine an ITO/NiO_X/WO_X/Pt p-n heterojunction memristor for neuromorphic applications as a synaptic crossbar array. The transition in the depletion region at the interface between p-type NiO_X and n-type WO_X crucially influences the self-rectifying characteristics of the device depending on the voltage polarity. Furthermore, long- and short-term memory coexist depending on the switching voltage condition, thus enabling various neuromorphic applications, such as reservoir computing and the use of the synaptic device at the off-chip trained network. The reliable operational characteristics are confirmed by obtaining an average memory window (>9) and rectification ratio (>58) between device-to-device and cycle-to-cycle. Furthermore, synaptic functions were successfully implemented, such as spike-rate-dependent plasticity, spike-number-dependent plasticity, paired-pulse facilitation, post-tetanic potentiation, paired-pulse depression, and post-tetanic depression, in conjunction with repeatability. Ultimately, reservoir computing is accomplished based on the I–V nonlinearity and short-term memory characteristics. A high pattern recognition rate (>96.2 %) is achieved in MNIST tasks using 16 reservoir states, thus affirming the trustworthiness of the reservoir computing system. With its comprehensive approach to synaptic applications for neuromorphic computing systems, the heterojunction device highlights its considerable potential to advance artificial neural networks in conjunction with novel memristor technology directions.