神经形态工程学
双金属片
终端(电信)
材料科学
计算机科学
纳米技术
人工神经网络
人工智能
电信
冶金
金属
作者
Subhra Jyoti Panda,Kanha Ram Khator,Priyanka Deswal,Shashwat Nayak,Durgesh Pandey,Suraj K. Patel,Suraj Kumar Agrawalla,Dibyajyoti Ghosh,Satyaprasad P. Senanayak,Chandra Shekhar Purohit
出处
期刊:Materials horizons
[Royal Society of Chemistry]
日期:2024-12-16
卷期号:12 (7): 2208-2222
被引量:1
摘要
, and volatile memory functionality. The atomistic computations corroborate the dominant influence of the organic framework on controlling ionic diffusion through porous channels. Finally, this capability to tune the ionic conduction in these MOFs was utilized to emulate synaptic plasticity, such as long-term potentiation/depression (LTP/LTD) and complex multi-terminal heterosynaptic plasticity. Attributes of spiking neural networks (SNNs) such as spike time-dependent plasticity (STDP) featuring a unique symmetric anti-Hebbian learning with an impressive STDP ratio of 109, and a paired-pulse facilitation (PPF) index of 60 were recorded, which is among the best for MOF-based neuromorphic devices. Overall, our technique of designing novel metal-organic frameworks with facile porous channels for controlled ionic motion could pave the way for a novel class of materials, allowing seamless integration for bio-synaptic electronic devices.
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