Linear Classification Function Emulated by Pectin‐Based Polysaccharide‐Gated Multiterminal Neuron Transistors
Jianmiao Guo, Yanghui Liu, Feichi Zhou, Fangzhou Li, Yingtao Li, Feng Huang
Abstract
Abstract Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi‐input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi‐terminal pectin‐gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in‐plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.