A Dual-Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing.

Abstract

Neuromorphic computing devices offer promising solutions for next-generation computing hardware, addressing the high throughput data processing demands of artificial intelligence applications through brain-mimicking non-von Neumann architecture. Herein, PEDOT:Tos/PTHF-based organic electrochemical transistors (OECTs) with dual-modal memory functions-both short-term and long-term-are demonstrated. By characterizing memory levels and relaxation times, the device has been efficiently manipulated and switched between the two modes through coupled control of pulse voltage and duration. Both short-term and long-term memory functions are integrated within the same device, enabling its use as artificial neurons for the reservoir unit and synapses in the readout layer to build up a reservoir computing (RC) system. The performance of the dynamic neuron and synaptic weight update are benchmarked with classification tasks on hand-written digit images, respectively, both attaining accuracies above 90%. Furthermore, by modulating the device as both reservoir mode and synaptic mode, a full-OECT RC system capable of distinguishing electromyography signals of hand gestures is demonstrated. These results highlight the potential of simplified, homogeneous integration of dual-modal OECTs to form brain-like computing hardware systems for efficient biological signal processing across a broad range of applications.