Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Condensed Matter > Mesoscale and Nanoscale Physics

arXiv:1906.00593 (cond-mat)
[Submitted on 3 Jun 2019]

Title:Impact of spin transfer torque on the write error rate of a voltage-torque-based magnetoresistive random access memory

Authors:Hiroshi Imamura, Rie Matsumoto
View PDF
Abstract:Impact of spin transfer torque (STT) on the write error rate of a voltage-torque-based magnetoresistive random access memory is theoretically analyzed by using the macrospin model. During the voltage pulse the STT assists or suppresses the precessional motion of the magnetization depending on the initial magnetization direction. The characteristic value of the current density is derived by balancing the STT and the external-field torque, which is about 5$\times$ 10$^{11}$ A/m$^{2}$. The results show that the write error rate is insensitive to the STT below the current density of $10^{10}$ A/m$^{2}$.
Comments: 8 pages, 5 figures, submitted to Physical Review Applied
Subjects: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
Cite as: arXiv:1906.00593 [cond-mat.mes-hall]
  (or arXiv:1906.00593v1 [cond-mat.mes-hall] for this version)
  https://doi.org/10.48550/arXiv.1906.00593

Submission history

From: Hiroshi Imamura [view email]
[v1] Mon, 3 Jun 2019 06:36:11 UTC (942 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cond-mat.mes-hall
< prev   |   next >
new | recent | 2019-06
Change to browse by:
cond-mat

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)