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Doping modulated carbon nanotube synapstors for a spike neuromorphic module
Alex Ming Shen1, Kyunghyun Kim, Andrew Tudor
1Department of Mechanical and Aerospace Engineering, California Nano Systems Institute, University of California, Los Angeles, California, 90095, USA.
Small (Weinheim an Der Bergstrasse, Germany)
|November 27, 2014
Summary
This study introduces a novel carbon nanotube (CNT) synapstor that mimics biological synapses. Doping modulation enhances synaptic performance, enabling efficient neuromorphic computing modules with low power consumption.
Area of Science:
- Materials Science
- Neuroscience
- Electronics Engineering
Background:
- Biological synapses are fundamental to neural computation.
- Existing artificial synapse devices face challenges in performance and power efficiency.
- Carbon nanotube (CNT) field-effect transistors offer potential for synaptic emulation.
Purpose of the Study:
- To develop a doping-modulated carbon nanotube (CNT) device, termed a "synapstor," that emulates biological synapse function.
- To enhance synaptic plasticity, postsynaptic current, and reduce power consumption in artificial synapses.
- To demonstrate a neuromorphic module integrating CNT synapstors for advanced signal processing.
Main Methods:
- Fabrication of a CNT synapstor using a field-effect transistor structure with a random CNT network channel.
- Deposition of an aluminum oxide (Al2 O3) film to create a p-n junction and modulate CNT conductivity.
- Integration of CNT synapstors with a silicon-based "soma" circuit to form a spike neuromorphic module.
Main Results:
- The doping-modulated CNT synapstor demonstrated significantly improved postsynaptic current (PSC).
- An extended tuning range of synaptic plasticity and reduced power consumption were achieved.
- The integrated neuromorphic module successfully exhibited spike parallel processing, memory, and plasticity functions.
Conclusions:
- The doping-modulated CNT synapstor represents a significant advancement in artificial synapse technology.
- The developed neuromorphic module shows potential for scalable, high-speed, low-power neural network emulation.
- This approach paves the way for more sophisticated brain-inspired computing systems.
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