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Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots.
Changhong Wang1, Wei He1, Yi Tong1
1Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372, Singapore.
Small (Weinheim an Der Bergstrasse, Germany)
|March 16, 2017
Summary
Graphene quantum dots improve memristive devices for artificial synapses. These dots enhance analog resistance state repeatability, crucial for controllable synaptic behavior in low-power neural networks.
Area of Science:
- Materials Science
- Nanotechnology
- Neuroscience
Background:
- Memristive devices show promise as artificial synapses for neural networks.
- Controllable analog resistance states are vital for synaptic behavior but are limited by random filament formation.
- Oxygen vacancies are fundamental to conductive filament formation and device repeatability.
Purpose of the Study:
- To enhance the repeatability of analog resistance states in memristive devices.
- To improve the controllable synaptic behavior for artificial neural networks.
- To reduce variations in switching voltages and their distribution.
Main Methods:
- Introduction of graphene quantum dots (GQDs) into memristive devices.
- Utilizing GQDs as nano oxygen-reservoirs to localize filament formation.
- Investigating the effect of GQDs on energy band alignment and tunneling current.
Main Results:
- Achieved highly repeatable analog resistance states with an 85% reduction in variations.
- Demonstrated enhanced localization of conductive filament formation due to GQDs.
- Observed significant reductions in switching voltages and their variations.
Conclusions:
- Graphene quantum dots effectively improve the analog resistance state repeatability of memristive devices.
- The GQD-enhanced devices offer accurate and efficient learning capabilities for artificial neural networks.
- This approach addresses a key challenge in developing practical memristive synaptic devices.
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