Evidence map›Paper›PMID 38983793›Full record

ArticlePhysical review. B2021

Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions.

Philippe Talatchian, Matthew W Daniels, Advait Madhavan, Matthew R Pufall, Emilie Jué, William H Rippard, Jabez J McClelland, Mark D Stiles

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physical review. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Emergent Chaos-Like Dynamics of Spin-Orbit-Torque-Driven Magnetic Transitions.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  2. Metrics for spin-based computing.Nature reviews. Physics · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Philippe TalatchianPhysical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Matthew W DanielsPhysical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Advait MadhavanPhysical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Matthew R PufallPhysical Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, USA.
Emilie JuéAssociate of the National Institute of Standards and Technology, Boulder, Colorado 80305, USA.
William H RippardPhysical Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, USA.
Jabez J McClellandPhysical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Mark D StilesPhysical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

Abstract

Superparamagnetic tunnel junctions (SMTJs) are promising sources for the randomness required by some compact and energy-efficient computing schemes. Coupling SMTJs gives rise to collective behavior that could be useful for cognitive computing. We use a simple linear electrical circuit to mutually couple two SMTJs through their stochastic electrical transitions. When one SMTJ makes a thermally induced transition, the voltage across both SMTJs changes, modifying the transition rates of both. This coupling leads to significant correlation between the states of the two devices. Using fits to a generalized Néel-Brown model for the individual thermally bistable magnetic devices, we can accurately reproduce the behavior of the coupled devices with a Markov model.

Identifiers

PMID38983793
PMCPMC11231882

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.