Ultralow power artificial synapses using nanotextured magnetic Josephson junctions
Michael L Schneider1, Christine A Donnelly1, Stephen E Russek1
1National Institute of Standards Technology, Boulder, CO 80305, USA.
Science Advances
|February 2, 2018
Summary
Researchers developed novel artificial synapses using superconducting Josephson junctions. These synapses are significantly more energy-efficient than the human brain, paving the way for advanced neuromorphic computing.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Neuromorphic computing aims to enhance computational efficiency for tasks like perception and decision-making.
- Current artificial neural networks, despite advancements, are far less energy-efficient than the human brain.
Purpose of the Study:
- To demonstrate a new type of artificial synapse for more energy-efficient neuromorphic computing.
- To develop a synapse technology that approaches the efficiency of biological synapses.
Main Methods:
- Utilized dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier.
- Engineered artificial synapses using niobium (Nb) electrodes and a silicon (Si) barrier with manganese (Mn) nanoclusters.
- Demonstrated synaptic weight tuning via electrical pulses that alter magnetic spin alignment.
Main Results:
- Achieved spiking energy per pulse below 1 attojoule (aJ), significantly lower than the human brain's ~10 femtojoule (fJ).
- Successfully demonstrated synaptic weight training using electrical pulses as low as 3 aJ.
- Observed Josephson plasma frequencies exceeding 100 GHz, indicating fast dynamical time scales.
Conclusions:
- The developed artificial synapses represent a significant advancement for creating faster and more energy-efficient neuromorphic platforms.
- This technology enables the potential for greater computational complexity in neuromorphic systems compared to existing technologies.
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