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Institution

University of Grenoble

EducationSaint-Martin-d'Hères, France
About: University of Grenoble is a education organization based out in Saint-Martin-d'Hères, France. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 25658 authors who have published 45143 publications receiving 909760 citations.


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Journal ArticleDOI
Abstract: Chalcogenide Phase-Change Materials (PCMs), such as Ge-Sb-Te alloys, are showing outstanding properties, which has led to their successful use for a long time in optical memories (DVDs) and, recently, in non-volatile resistive memories. The latter, known as Phase-Change Material memories or Phase-Change Random Access Memories (PCRAMs), are the most promising candidate among emerging Non-Volatile Memory (NVM) technologies to replace the current FLASH memories at CMOS technology nodes under 28 nm. Chalcogenide PCMs exhibit fast and reversible phase transformations between crystalline and amorphous states with very different transport and optical properties leading to a unique set of features for PCRAMs, such as fast programming, good cyclability, high scalability, multi-level storage capability and good data retention. Nevertheless, PCM memory technology has to overcome several challenges to definitively invade the NVM market. In this review paper we examine the main technological challenges that PCM memory technology must face and we illustrate how new memory architecture, innovative deposition methods and PCM composition optimization can contribute to further improvements of this technology. In particular, we examine how to lower the programming currents and increase data retention. Scaling down PCM memories for large scale integration means incorporation of the phase-change material into more and more confined structures and raises material science issues to understand interface and size effects on crystallization. Other material science issues are related to the stability and ageing of the amorphous state of phase-change materials. The stability of the amorphous phase, which determines data retention in memory devices, can be increased by doping the phase-change material. Ageing of the amorphous phase leads to a large increase of the resistivity with time (resistance drift), which has hindered up-to-now the development of ultra-high multilevel storage devices. A review of the current understanding of all these issues is provided from a material science point of view.

180 citations

Journal ArticleDOI
Hannah Moshontz1, Lorne Campbell2, Charles R. Ebersole3, Hans IJzerman4, Heather L. Urry5, Patrick S. Forscher6, Jon Grahe7, Randy J. McCarthy8, Erica D. Musser9, Jan Antfolk10, Christopher M. Castille11, Thomas Rhys Evans12, Susann Fiedler13, Jessica Kay Flake14, Diego A. Forero, Steve M. J. Janssen15, Justin Robert Keene16, John Protzko17, Balazs Aczel18, Sara Álvarez Solas, Daniel Ansari2, Dana Awlia19, Ernest Baskin20, Carlota Batres21, Martha Lucia Borras-Guevara22, Cameron Brick23, Priyanka Chandel24, Armand Chatard25, Armand Chatard26, William J. Chopik27, David Clarance, Nicholas A. Coles28, Katherine S. Corker29, Barnaby J. W. Dixson30, Vilius Dranseika31, Yarrow Dunham32, Nicholas W. Fox33, Gwendolyn Gardiner34, S. Mason Garrison35, Tripat Gill36, Amanda C. Hahn37, Bastian Jaeger38, Pavol Kačmár39, Gwenaël Kaminski, Philipp Kanske40, Zoltan Kekecs41, Melissa Kline42, Monica A. Koehn43, Pratibha Kujur24, Carmel A. Levitan44, Jeremy K. Miller45, Ceylan Okan43, Jerome Olsen46, Oscar Oviedo-Trespalacios47, Asil Ali Özdoğru48, Babita Pande24, Arti Parganiha24, Noorshama Parveen24, Gerit Pfuhl, Sraddha Pradhan24, Ivan Ropovik49, Nicholas O. Rule50, Blair Saunders51, Vidar Schei52, Kathleen Schmidt53, Margaret Messiah Singh24, Miroslav Sirota54, Crystal N. Steltenpohl55, Stefan Stieger56, Daniel Storage57, Gavin Brent Sullivan12, Anna Szabelska58, Christian K. Tamnes59, Miguel A. Vadillo60, Jaroslava Varella Valentova61, Wolf Vanpaemel62, Marco Antonio Correa Varella61, Evie Vergauwe63, Mark Verschoor64, Michelangelo Vianello65, Martin Voracek46, Glenn Patrick Williams66, John Paul Wilson67, Janis Zickfeld59, Jack Arnal68, Burak Aydin, Sau-Chin Chen69, Lisa M. DeBruine70, Ana María Fernández71, Kai T. Horstmann72, Peder M. Isager73, Benedict C. Jones70, Aycan Kapucu74, Hause Lin50, Michael C. Mensink75, Gorka Navarrete76, Silan Ma77, Christopher R. Chartier19 
Duke University1, University of Western Ontario2, University of Virginia3, University of Grenoble4, Tufts University5, University of Arkansas6, Pacific Lutheran University7, Northern Illinois University8, Florida International University9, Åbo Akademi University10, Nicholls State University11, Coventry University12, Max Planck Society13, McGill University14, University of Nottingham Malaysia Campus15, Texas Tech University16, University of California, Santa Barbara17, Eötvös Loránd University18, Ashland University19, Saint Joseph's University20, Franklin & Marshall College21, University of St Andrews22, University of Cambridge23, Pandit Ravishankar Shukla University24, Centre national de la recherche scientifique25, University of Poitiers26, Michigan State University27, University of Tennessee28, Grand Valley State University29, University of Queensland30, Vilnius University31, Yale University32, Rutgers University33, University of California, Riverside34, Vanderbilt University35, Wilfrid Laurier University36, Humboldt State University37, Tilburg University38, University of Pavol Jozef Šafárik39, Dresden University of Technology40, Lund University41, Massachusetts Institute of Technology42, University of Sydney43, Occidental College44, Willamette University45, University of Vienna46, Queensland University of Technology47, Üsküdar University48, University of Prešov49, University of Toronto50, University of Dundee51, Norwegian School of Economics52, Southern Illinois University Carbondale53, University of Essex54, University of Southern Indiana55, University of Health Sciences Antigua56, University of Illinois at Urbana–Champaign57, Queen's University Belfast58, University of Oslo59, Autonomous University of Madrid60, University of São Paulo61, Katholieke Universiteit Leuven62, University of Geneva63, University of Groningen64, University of Padua65, Abertay University66, Montclair State University67, McDaniel College68, Tzu Chi University69, University of Glasgow70, University of Santiago, Chile71, Humboldt University of Berlin72, Eindhoven University of Technology73, Ege University74, University of Wisconsin–Stout75, Adolfo Ibáñez University76, University of the Philippines Diliman77
01 Oct 2018
TL;DR: The Psychological Science Accelerator is a distributed network of laboratories designed to enable and support crowdsourced research projects that will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.
Abstract: Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.

180 citations

Journal ArticleDOI
TL;DR: In this paper, it was shown that the thermal theory gives a good description of the flow in the slope angle range 5° [lsim ] ≤ 90°, and that the spatial growth rates of the cloud height and length are constant for a given slope angle and show a linear dependence on θ.
Abstract: Two-dimensional buoyant clouds moving along inclined boundaries under a gravitational force are investigated theoretically and experimentally. It is found that the ‘thermal theory’ gives a good description of the flow in the slope angle range 5° [lsim ] θ ≤ 90°. In this range the spatial growth rates of the cloud height and length are constant for a given slope angle and show a linear dependence on θ. For a cloud released with zero initial velocity the front velocity Uf first increases and then decreases, with the characteristic time of acceleration predicted by theory. In the decelerating state Uf/(g0′Q0/xf)½ is 2·6 ± 0·2 at θ ≃ 15°, and then reduces uniformly with increasing θ to a value of 1·5 ≃ 0·2 at 90° (where g0′Q0 is the released buoyancy and xf is the front position measured from a virtual origin). The shape of the cloud is well approximated by a half-ellipse. The variation of the ratio of the principal axes of the half-ellipse with slope angle is identical with that of the head of an inclined starting plume (Britter & Linden 1980). However, the cloud has a greater growth rate than the head of a starting plume.

180 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Dale Charles Abbott3  +3001 moreInstitutions (220)
TL;DR: In this paper, the decays of B0 s! + and B0! + have been studied using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016.
Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...

180 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015-2100 as part of the Ice Sheet Model Comparison for CMIP6 (ISMIP6).
Abstract: . Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.

180 citations


Authors

Showing all 25961 results

NameH-indexPapersCitations
Dieter Lutz13967167414
Marcella Bona137139192162
Nicolas Berger137158196529
Cordelia Schmid135464103925
J. F. Macías-Pérez13448694715
Marina Cobal132107885437
Lydia Roos132128489435
Tetiana Hryn'ova131105984260
Johann Collot131101882865
Remi Lafaye131101283281
Jan Stark131118687025
Sabine Crépé-Renaudin129114282741
Isabelle Wingerter-Seez12993079689
James Alexander12988675096
Jessica Levêque129100670208
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022698
20215,126
20205,328
20195,192
20184,999