Nanochannel-Based Transport in an Interfacial Memristor Can Emulate the Analog Weight Modulation of Synapses
| Autor: | Boyi Dong, Yaoyao Fu, Yi Li, Kecheng Yang, Pan Zhang, Zhenyu Wang, Yuhui He, Min Xia, Ralph H. Scheicher, Xiangshui Miao, Fuwei Zhuge, Yue Zhou |
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| Rok vydání: | 2019 |
| Předmět: |
Work (thermodynamics)
Materials science Bioengineering 02 engineering and technology Memristor law.invention chemistry.chemical_compound Biomimetic Materials law Nanotechnology General Materials Science Nanoscopic scale business.industry Mechanical Engineering Electric Conductivity Conductance General Chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics Nanostructures chemistry Neuromorphic engineering Modulation Synapses Ionic liquid Optoelectronics 0210 nano-technology business Transport phenomena |
| Zdroj: | Nano Letters. 19:4279-4286 |
| ISSN: | 1530-6992 1530-6984 |
| Popis: | By exploiting novel transport phenomena such as ion selectivity at the nanoscale, it has been shown that nanochannel systems can exhibit electrically controllable conductance, suggesting their potential use in neuromorphic devices. However, several critical features of biological synapses, particularly their conductance modulation, which is both memorable and gradual, have rarely been reported in these types of systems due to the fast flow property of typical inorganic electrolytes. In this work, we demonstrate that electrically manipulating the nanochannel conductance can result in nonvolatile conductance tuning capable of mimicking the analog behavior of synapses by introducing a room-temperature ionic liquid (IL) and a KCl solution into the two ends of a nanochannel system. The gradual conductance-tuning mechanism is identified through fluorescence measurements as the voltage-induced movement of the interface between the immiscible IL and KCl solution, while the successful memorization of the conductance tuning is ascribed to the large viscosity of the IL. We applied a nanochannel-based synapse to a handwritten digit-recognition task, reaching an accuracy of 94%. These promising results provide important guidance for the future design of nanochannel-based neuromorphic devices and the manipulation of nanochannel transport for computing. |
| Databáze: | OpenAIRE |
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