Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning
- Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning
- 이현정; 임정아; 주현수; 정재승; 김수진; 장호원; 양회창
- artificial neural networks; cyclic endurance; fiber-shaped electronic devices; neuromorphic devices; organic electrochemical transistors; polythiophene; redox mechanism
- Issue Date
- Advanced materials
- VOL 33, NO 26-210047(12)
- Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.
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