Solid-State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing

As the core component of an intelligent neuromorphic computer system, reliable synaptic devices process vast amounts of data with high computing speed and low energy consumption. In this work, the ion-doped eco-friendly solution-processed indium oxide (InO_x)/aluminum oxide (AlO_x) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlO_x solid-state layer to facilitate the generation of electrical double layers and doped into InO_x to improve the stability of long-term potentiation/depression cyclic update and enhance the synaptic plasticity. Finally, an artificial neural network simulator is well designed to electrocardiogram signal recognition based on the G_max/G_min ratio and nonlinearity of weight update curve. According to the results, the device possesses tremendous potential for biosignal prediction and neural intervention. Moreover, for the first time, the recognition accuracy of the abnormality of the cardiovascular can reach over 94.8% obtained from the confusion matrix. Consequently, this research article presents a stable and robust neuromorphic device for biosignal recognition based on solid-state EGTs via the synaptic long-term plasticity.