Considering that the human brain uses ≈10 synapses to operate, the development of effective artificial synapses is essential to build brain-inspired computing systems. In biological synapses, the voltage-gated ion channels are very important for regulating the action-potential firing. Here, an electrolyte-gated transistor using WO with a unique tunnel structure, which can emulate the ionic modulation process of biological synapses, is proposed. The transistor successfully realizes synaptic functions of both short-term and long-term plasticity. Short-term plasticity is mimicked with the help of electrolyte ion dynamics under low electrical bias, whereas the long-term plasticity is realized using proton insertion in WO under high electrical bias. This is a new working approach to control the transition from short-term memory to long-term memory using different gate voltage amplitude for artificial synapses. Other essential synaptic behaviors, such as paired pulse facilitation, the depression and potentiation of synaptic weight, as well as spike-timing-dependent plasticity are also implemented in this artificial synapse. These results provide a new recipe for designing synaptic electrolyte-gated transistors through the electrostatic and electrochemical effects.

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high‐performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short‐ and long‐term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.