Physical Reservoir Computing Using Tellurium-Based Gate-Tunable Artificial Photonic Synapses.

Abstract

We report tellurium (Te) thin-film-based artificial photonic synapses and their application to physical reservoir computing (PRC). The Te-based artificial photonic synapses were fabricated by using sputtered Te thin films and spray-coated MXene (Ti3C2) electrodes. A thorough investigation of the field-dependent persistent photoconductivity (PPC) of the Te channel revealed that the relaxation speed of the transient photocurrent depended on the gate bias. Utilizing the PPC property, the Te device served as an excellent photonic synapse under light pulse stimulus, exhibiting multiple synaptic characteristics such as excitatory postsynaptic current and paired-pulse facilitation, as well as highly linear potentiation-depression characteristics; a simulation-based study further confirmed the effectiveness of the device. Most importantly, by exploiting the nonlinear and fading memory characteristics of the Te photonic synapse, we demonstrate two advanced examples of PRC. In classifying handwritten digits, our system carried out successful digit recognition without binarization or another simplification process with reduced computational cost compared to conventional systems. To solve second-order nonlinear equations, we introduce the strategy of utilizing historical nodes. The combination of historical nodes and the gate-tunable responses of the photonic synapses, which provide an enriched reservoir state, yielded excellent prediction accuracy. Overall, this work will offer an understanding of Te-based optoelectronic devices and their synergetic integration with neuromorphic devices and PRC.