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Cited by 288

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory

Yu, Shimeng, Wu, Yi, Wong, H.-S. Philip·2011·Applied Physics Letters

Abstract

HfO x / AlO x bilayer resistive switching devices were fabricated for the study of the switching dynamics of the metal oxide memory. An exponential voltage-time relationship was experimentally observed as follows: the programming pulse widths need for switching exponentially decreased with the increase in the programming pulse amplitudes. Two following programming schemes were proposed to modulate the high resistance state values: (1) exponentially increase the programming pulse width; (2) linearly increase the programming pulse amplitude. Although both of these schemes were effective to achieve the target resistance, the transient current response measurements suggest the second scheme consumes considerably less energy in the programming. A field-driven oxygen ions migration model was utilized to elucidate the above experimentally observed phenomenon.

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Metadata

Authors
Yu, S., Wu, Y., & Wong, H.-S. P.
Venue
Applied Physics Letters
Published
2011-03-07
Volume
98
Issue
10
ISSN
  • 0003-6951 (ISSN-L)
  • 1077-3118
OpenAlex
W2035054741
Cited by
288
References
13
FWCI
12.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory. https://app.cochat.ai/papers/10.1063/1.3564883
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Yu, S., Wu, Y., & Wong, H.-S. P. (2011). Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory. Applied Physics Letters. https://doi.org/10.1063/1.3564883

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.