Reconfigurable optoelectronic neuromorphic properties of MoS₂-based memristors decorated with Pt nanoparticles for low power spiking neural network applications

Abstract The intriguing properties of two-dimensional (2D) materials render them attractive for energy efficient neuromorphic computations because their atomic scale thickness can alleviate the power requirements of the device. In parallel, their layered structure can be leveraged to optically program the device and further reduce power consumption. Along these lines, in this work, a forming free memory device consisting of a MoS 2 monolayer with dimensions of ∼100 μ m decorated with small (∼3 nm diameter) Pt nanoparticles (NPs) was fabricated. The impact of the Pt NPs’ surface density on the optoelectronic neuromorphic properties under ultraviolet irradiation ( λ = 390 nm) was systematically investigated. More specifically, the reference samples without Pt NPs exhibited only synaptic behavior, while the NPs-based one (surface density of ∼2 × 10 12 NPs cm −2 ) presented a neuron-like response. An elevated surface density (∼5 × 10 12 NPs cm −2 ) just reduced the frequency of the generated spikes. Various synaptic plasticity and neuronal coding schemes were experimentally demonstrated. The underlying origins of this behavior were attributed to band bending in the Pt NPs-MoS 2 interface, leading to a trapping effect of electrons on the metallic NPs, evidenced by photoluminescence quenching, followed by a detrapping process, demonstrated by the reduced firing rate when using the Pt layer with the higher surface density. This versatility of the devices was leveraged to simulate the behavior of a fully optoelectronic spiking neural network. Considering the low energy consumption per spike (∼400 pJ) during the experimental recorded neuromorphic properties, a dramatically reduced power consumption of ∼320 μ W was extracted during the pattern recognition of optical images. Our work provides valuable insights for emulating the artificial synaptic and neuronal behavior and paves the way for the development of next-generation and fully memristive artificial neural networks with a very small energy footprint.