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.
Jianmiao Guo,Yanghui Liu,Feichi Zhou,Fangzhou Li,Yingtao Li,Feng Huang.
Advanced Functional Materials,31:33,2102015(2021)