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Fully Printed All-Solid-State Organic Flexible Artificial Synapse for Neuromorphic Computing

Qingzhou Liu, Yihang Liu, Ji Li, C.L. Lau, Fanqi Wu, Anyi Zhang, Zhen Li, Mingrui Chen, Hongyu Fu, Jeffrey Draper, Xuan Cao, Chongwu Zhou

Year
2019
Citations
103

Abstract

Nonvolatile, flexible artificial synapses that can be used for brain-inspired computing are highly desirable for emerging applications such as human-machine interfaces, soft robotics, medical implants, and biological studies. Printed devices based on organic materials are very promising for these applications due to their sensitivity to ion injection, intrinsic printability, biocompatibility, and great potential for flexible/stretchable electronics. Herein, we report the experimental realization of a nonvolatile artificial synapse using organic polymers in a scalable fabrication process. The three-terminal electrochemical neuromorphic device successfully emulates the key features of biological synapses: long-term potentiation/depression, spike timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow energy consumption. The artificial synapse network exhibits an excellent endurance against bending tests and enables a direct emulation of logic gates, which shows the feasibility of using them in futuristic hierarchical neural networks. Based on our demonstration of 100 distinct, nonvolatile conductance states, we achieved a high accuracy in pattern recognition and face classification neural network simulations.

Keywords

Neuromorphic engineeringMaterials scienceArtificial neural networkMemristorComputer scienceArtificial intelligenceScalabilityNanotechnologyEmulationSynapse

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