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High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory 被引量:1

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摘要 Metal oxide-based Resistive Random-Access Memory(RRAM)exhibits multiple resistance states,arising from the activation/deactivation of a conductive filament(CF)inside a switching layer.Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM.Here a phase-field model is developed,based on materials properties determined by ab initio calculations,to investigate the role of electrical bias,heat transport and defect-induced Vegard strain in the resistive switching behavior.
出处 《npj Computational Materials》 SCIE EI CSCD 2020年第1期1-10,共10页 计算材料学(英文)
基金 P.G.was supported by the Center for Nanophase Materials Sciences,which is a DOE Office of Science User Facility.J.-J.W.acknowledges the partial support from the Army Research Office under grant number W911NF-17-1-0462 L.Q.Chen acknowledges partial support from the Computational Materials Sciences Program funded by the US Department of Energy,Office of Science,Basic Energy Sciences,under Award Number DE-SC0020145 J.-J.W.and L.-Q.C.also acknowledges the partial support from the Donald W.Hamer Foundation through the Hamer Professorship at Penn State.Y.H.H.acknowledges support from National Natural Science Foundation of China under grant number 51802280 This manuscript has been authored by UT-Battelle,LLC under Contract No.DE-AC05-00OR22725 with the U.S.Department of Energy.
关键词 BEHAVIOR LAYER PHASE
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