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Nanoscale Conductive Filament with Alternating Rectification as an Artificial Synapse Building Block
Dan Berco1, Yu Zhou1, Sankara Rao Gollu1
1School of Electrical and Electronic Engineering , Nanyang Technological University , 50 Nanyang Avenue , Singapore 639798 , Singapore.
ACS Nano
|May 25, 2018
Summary
This study introduces novel artificial synapses using manipulated metal oxide stoichiometry. These nanoscale devices offer dynamic rectification for brain-inspired computing, overcoming limitations of traditional resistive memory approaches.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Resistive memory (RRAM) is popular for artificial neural networks.
- Current RRAM synapses face trade-offs in size, power, and functionality.
- Achieving biological neural network density and energy efficiency remains a challenge.
Purpose of the Study:
- To demonstrate an alternative artificial synapse implementation.
- To overcome the limitations of current RRAM-based artificial synapses.
- To enable more efficient and dense brain-inspired computing platforms.
Main Methods:
- Manipulating local stoichiometry of metal oxide materials.
- Creating nanoscale conductive filaments acting as synaptic gaps.
- Utilizing defect states to form structures with dynamic rectification properties.
Main Results:
- Developed artificial synapses based on single nanoscale conductive filaments.
- Demonstrated dynamic rectification that can be flipped between forward and reverse directions.
- Showcased plasticity alteration via a digital scheme by adjusting connexon ratios.
Conclusions:
- The novel artificial synapses offer a promising alternative to conventional RRAM.
- These devices enable control over excitatory and inhibitory synaptic weights.
- The approach facilitates the development of high-density, low-power brain-inspired computing.
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