MoTe synaptic transistor and its application to physical reservoir computing.

In this study, we systematically analyzed the synaptic properties of an MoTe-based transistor and propose a physical reservoir computing system based on it. The device was fabricated as a back-gate structure using mechanically exfoliated MoTe sheets on a SiO/Si substrate, which showed the characteristics of an n-type field effect transistor. It exhibited synaptic properties upon application of voltage pulses to the gate, such as excitatory post-synaptic currents or paired pulse facilitations. A long-term conductance modulation was achieved upon the application of a voltage pulse series, and its potential in hardware-based artificial neural networks was confirmed a simulation study. Furthermore, we demonstrated physical reservoir computing using the device in a classification task involving gray-scale handwritten digits. The nonlinear response and fading memory characteristics of the device played critical roles in achieving good accuracy in physical reservoir computing. The MoTe-based synaptic transistor demonstrates the feasibility of two-dimensional materials in neuromorphic computing for energy efficient AI systems.