Giant memory function based on the magnetic field history of resistive switching under a constant bias voltage

Memristors, which are characterized by their unique input-voltage-history-dependent resistance, have garnered significant attention for the exploration of next-generation in-memory computing, reconfigurable logic circuits, and neural networks. Memristors are controlled by the applied input voltage;...

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Bibliographic Details
Published inarXiv.org
Main Authors Kaneda, Masaya, Tsuruoka, Shun, Hikari Shinya, Fukushima, Tetsuya, Endo, Tatsuro, Tadano, Yuriko, Takeda, Takahito, Masago, Akira, Tanaka, Masaaki, Katayama-Yoshida, Hiroshi, Ohya, Shinobu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.11.2024
Subjects
Bias
Electric potential
Ferromagnetism
Logic circuits
Magnesium oxide
Magnetic fields
Magnetoresistance
Magnetoresistivity
Memory devices
Memristors
Neural networks
Spintronics
Substrates
Voltage
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Summary:Memristors, which are characterized by their unique input-voltage-history-dependent resistance, have garnered significant attention for the exploration of next-generation in-memory computing, reconfigurable logic circuits, and neural networks. Memristors are controlled by the applied input voltage; however, the latent potential of their magnetic field sensitivity for spintronics applications has rarely been explored. In particular, valuable functionalities are expected to be yielded by combining their history dependence and magnetic field response. Here, for the first time, we reveal a giant memory function based on the magnetic field history of memristive switching, with an extremely large magnetoresistance ratio of up to 32,900% under a constant bias voltage, using a two-terminal Ge-channel device with Fe/MgO electrodes. We attribute this behavior to colossal magnetoresistive switching induced by the d0 ferromagnetism of Mg vacancies in the MgO layers and impact ionization breakdown in the Ge substrate. Our findings may lead to the development of highly sensitive multi-field sensors, high-performance magnetic memory, and advanced neuromorphic devices.
ISSN:2331-8422