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Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing

Li Zhu, Junchen Lin, Yixin Zhu, Jie Wu, Xiang Wan, Huabin Sun, Zhihao Yu, Yong Xu, Cheeleong Tan

2024Nanomaterials18 citationsDOIOpen Access PDF

Abstract

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device’s electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at −0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Topics & Concepts

Neuromorphic engineeringTransistorMaterials scienceElectrochemistryWearable computerOrganic semiconductorOptoelectronicsNanotechnologyComputer scienceElectrodeElectronic engineeringElectrical engineeringEmbedded systemArtificial neural networkPhysicsEngineeringVoltageArtificial intelligenceQuantum mechanicsAdvanced Memory and Neural ComputingConducting polymers and applicationsNeuroscience and Neural Engineering
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