Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron
<|reference_start|>Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron: In spiking neural networks, neuron dynamics are described by the biologically realistic integrate-and-fire model that captures membrane potential accumulation and above-threshold firing behaviors. Among the hardware implementations of integrate-and-fire neuron devices, one important feature, reset, has been largely ignored. Here, we present the design and fabrication of a magnetic domain wall and magnetic tunnel junction based artificial integrate-and-fire neuron device that achieves reliable reset at the end of the integrate-fire cycle. We demonstrate the domain propagation in the domain wall racetrack (integration), reading using a magnetic tunnel junction (fire), and reset as the domain is ejected from the racetrack, showing the artificial neuron can be operated continuously over 100 integrate-fire-reset cycles. Both pulse amplitude and pulse number encoding is demonstrated. The device data is applied on an image classification task using a spiking neural network and shown to have comparable performance to an ideal leaky, integrate-and-fire neural network. These results achieve the first demonstration of reliable integrate-fire-reset in domain wall-magnetic tunnel junction-based neuron devices and shows the promise of spintronics for neuromorphic computing.<|reference_end|>
@article{cui12024domain,
    title={Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron},
    author={Can Cui1, Sam Liu, Jaesuk Kwon and Jean Anne C. Incorvia},
    journal={arXiv preprint arXiv:2405.14851},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14851},
    primaryClass={cs.NE cond-mat.mes-hall}
}