Tunable Resistive Switching Enabled by Malleable Redox Reaction in the Nano-Vacuum Gap
| Autor: | Chun Chia Tan, Xinglong Ji, Rong Zhao, Kian Guan Lim, Tow Chong Chong, Chao Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Resistive touchscreen Materials science Artificial neural network business.industry 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Neuromorphic engineering 0103 physical sciences Nano Electrode Hardware_INTEGRATEDCIRCUITS Artificial neuron Systems design Optoelectronics General Materials Science 0210 nano-technology business Quantum tunnelling |
| Zdroj: | ACS Applied Materials & Interfaces. 11:20965-20972 |
| ISSN: | 1944-8252 1944-8244 |
| Popis: | Neuromorphic computing has emerged as a highly promising alternative to conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neurons with leaky integrate-and-fire behavior and artificial synapses with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between a bottom electrode and a mixed-ionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filaments dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filaments by tuning the electron tunneling efficiency, in analogy to a pseudo-three-terminal device, resulting in tunable switching behavior in various forms from volatile to nonvolatile switching in a single device. Our device demonstrated cross-functions, in particular, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building large-scale artificial neural networks for neuromorphic computing. |
| Databáze: | OpenAIRE |
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