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

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Title: Giant memory function based on the magnetic field history of resistive switching under a constant bias voltage
Authors: Kaneda, Masaya, Tsuruoka, Shun, Shinya, Hikari, Fukushima, Tetsuya, Endo, Tatsuro, Tadano, Yuriko, Takeda, Takahito, Masago, Akira, Tanaka, Masaaki, Katayama-Yoshida, Hiroshi, Ohya, Shinobu
Publication Year: 2024
Collection: Physics (Other)
Subject Terms: Physics - Applied Physics
More Details: 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.
Comment: 30 pages, 5 figures in the main text, 8 figures in Supporting Information
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2411.04355
Accession Number: edsarx.2411.04355
Database: arXiv
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  Data: <searchLink fieldCode="AR" term="%22Kaneda%2C+Masaya%22">Kaneda, Masaya</searchLink><br /><searchLink fieldCode="AR" term="%22Tsuruoka%2C+Shun%22">Tsuruoka, Shun</searchLink><br /><searchLink fieldCode="AR" term="%22Shinya%2C+Hikari%22">Shinya, Hikari</searchLink><br /><searchLink fieldCode="AR" term="%22Fukushima%2C+Tetsuya%22">Fukushima, Tetsuya</searchLink><br /><searchLink fieldCode="AR" term="%22Endo%2C+Tatsuro%22">Endo, Tatsuro</searchLink><br /><searchLink fieldCode="AR" term="%22Tadano%2C+Yuriko%22">Tadano, Yuriko</searchLink><br /><searchLink fieldCode="AR" term="%22Takeda%2C+Takahito%22">Takeda, Takahito</searchLink><br /><searchLink fieldCode="AR" term="%22Masago%2C+Akira%22">Masago, Akira</searchLink><br /><searchLink fieldCode="AR" term="%22Tanaka%2C+Masaaki%22">Tanaka, Masaaki</searchLink><br /><searchLink fieldCode="AR" term="%22Katayama-Yoshida%2C+Hiroshi%22">Katayama-Yoshida, Hiroshi</searchLink><br /><searchLink fieldCode="AR" term="%22Ohya%2C+Shinobu%22">Ohya, Shinobu</searchLink>
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  Data: 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.<br />Comment: 30 pages, 5 figures in the main text, 8 figures in Supporting Information
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