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Multilayer ferromagnetic spintronic devices for neuromorphic computing applications
Aijaz H Lone1, Xuecui Zou1, Kishan K Mishra1
1Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia. aijaz.lone@kaust.edu.sa.
Nanoscale
|June 21, 2024
Summary
Researchers developed a spintronic device for neuromorphic computing, demonstrating multilevel resistance switching for synaptic weights. This technology achieves over 90% accuracy on the MNIST dataset, showing promise for energy-efficient hardware neural networks.
Area of Science:
- Spintronics and Neuromorphic Computing
- Ferromagnetic Thin Films
- Solid State Physics
Background:
- Spintronic devices offer energy-efficient solutions for memory, logic, and unconventional computing.
- Neuromorphic computing aims to mimic the human brain's structure and function.
- Multilevel resistance states are crucial for efficient synaptic weight representation.
Purpose of the Study:
- To experimentally and computationally realize a multilayer ferromagnetic spintronic device for neuromorphic applications.
- To investigate the scalability and performance of these devices in hardware neural networks.
- To demonstrate the device's potential for energy-efficient artificial intelligence.
Main Methods:
- Fabrication and characterization of multilayer ferromagnetic spintronic devices.
- Micromagnetic simulations of scaled devices (down to 64 nm width) using magnetic tunnel junction (MTJ) configuration.
- Implementation of a varying pulse amplitude scheme for improved weight linearity.
- Training and testing a neural network on the MNIST dataset using the spintronic devices as synaptic weights.
Main Results:
- The device exhibits temperature-dependent, magnetic field, and current-controlled multilevel resistance state switching.
- Simulations show scalability down to 64 nm width with an energy dissipation of 1.23 fJ for potentiation/depression.
- A 3-layer fully connected neural network using these devices achieved over 90% recognition accuracy on the MNIST dataset.
- The proposed varying pulse amplitude scheme enhances synaptic weight linearity.
Conclusions:
- The developed multilayer spintronic device shows significant potential for energy-efficient neuromorphic computing applications.
- The device's multilevel resistance states effectively function as synaptic weights in hardware neural networks.
- Scalability and high recognition accuracy demonstrate the practical viability of spintronic devices for artificial intelligence.
