Multimode transistors and neural networks based on ion-dynamic capacitance

Abstract Electrolyte-gated transistors can function as switching elements, artificial synapses and memristive systems, could be used to create compact powerful neuromorphic computing networks. However, insight into the underlying physics of such devices, including complex ion dynamics resulting capacitances, remains limited. Here we report a concise model for transient ion-dynamic capacitance in electrolyte-gated transistors. The theory predicts that plasticity, high apparent mobility, sharp subthreshold swing conductance achieved—on demand—in single transistor by appropriately programming interfacial concentrations or matching scan speed with motions. We then fabricate multimode using common solid-state electrolyte films experimentally confirm different capabilities. also show software devices neural networks switched between conventional networks, recurrent spiking