Neuromorphic information processing using ultrafast heat dynamics and quench switching of an antiferromagnet

Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low power consumption and possible high integration density are...

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Published inarXiv.org
Main Authors Zubáč, Jan, Surýnek, Miloslav, Olejník, Kamil, Farkaš, Andrej, Krizek, Filip, Nádvorník, Lukáš, Kubaščík, Petr, Kašpar, Zdeněk, Trojánek, František, Campion, Richard P, Novák, Vít, Němec, Petr, Jungwirth, Tomáš
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.10.2024
Subjects
Antiferromagnetism
Data processing
Femtosecond pulsed lasers
Femtosecond pulses
Image coding
Information processing
Information storage
Lasers
Nanosecond pulses
Neural networks
Pattern recognition
Task complexity
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Summary:Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low power consumption and possible high integration density are desirable for information storage and processing or applications in unconventional computing. Among antiferromagnets, CuMnAs stands out for atomic-level scalable magnetic textures, analogue multilevel storage capability, and the magnetic state's control by a single electrical or femtosecond laser pulse. Using a pair of excitation laser pulses, this work examines synaptic and neuronal functionalities of CuMnAs for information processing, readily incorporating two principles of distinct characteristic timescales. Laser-induced transient heat dynamics at sub-nanosecond times represents the short-term memory and causes resistance switching due to quenching into a magnetically fragmented state. This quench switching, detectable electrically from ultrashort times to hours after writing, reminisces the long-term memory. The versatility of the principles' combination is demonstrated by operations commonly used in neural networks. Temporal latency coding, fundamental to spiking neural networks, is utilized to encode data from a grayscale image into sub-nanosecond pulse delays. Applying input laser pulses with distinct amplitudes then allows for pulse-pattern recognition. The results open pathways for designing novel computing architectures.
ISSN:2331-8422