Unveiling ECRAM switching mechanisms using variable temperature Hall measurements for accelerated AI computation

Hyunjeong Kwak, Junyoung Choi, Seungmin Han, Eun Ho Kim, Chaeyoun Kim, Paul Solomon, Junyong Lee, Doyoon Kim, Byungha Shin, Donghwa Lee, Oki Gunawan () and Seyoung Kim ()
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Hyunjeong Kwak: Pohang University of Science and Technology
Junyoung Choi: Pohang University of Science and Technology
Seungmin Han: Pohang University of Science and Technology
Eun Ho Kim: Pohang University of Science and Technology
Chaeyoun Kim: Korea Advanced Institute of Science and Technology
Paul Solomon: IBM T. J. Watson Research Center
Junyong Lee: Pohang University of Science and Technology
Doyoon Kim: Pohang University of Science and Technology
Byungha Shin: Korea Advanced Institute of Science and Technology
Donghwa Lee: Pohang University of Science and Technology
Oki Gunawan: IBM T. J. Watson Research Center
Seyoung Kim: Pohang University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Electrochemical random-access memory devices are promising for analog cross-point array-based artificial intelligence accelerators due to their high stability and programmability. However, understanding their switching mechanism is challenging due to complex multilayer structures and the high resistivity of oxide materials. Here, we fabricate multi-terminal Hall-bar devices and conduct alternating current magnetic parallel dipole line Hall measurements to extract transport parameters. Through variable-temperature Hall measurements, we determine the oxygen donor level at approximately 0.1 eV in tungsten oxide and reveal that conductance potentiation even at low temperatures results from increased mobility and carrier density. This behavior is linked to reversible electronic and atomic structure changes, supported by density functional theory calculations. Our findings enhance the understanding of electrochemical random-access memory switching mechanisms and provide insights for improving high-performance, energy-efficient artificial intelligence computation in analog hardware.

Date: 2025
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DOI: 10.1038/s41467-025-58004-0

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