Your browser doesn't support javascript.
loading
Understanding of multiple resistance states by current sweeping in MoS2-based non-volatile memory devices.
Wu, Xiaohan; Ge, Ruijing; Akinwande, Deji; Lee, Jack C.
Afiliação
  • Wu X; Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States of America.
Nanotechnology ; 31(46): 465206, 2020 Nov 13.
Article em En | MEDLINE | ID: mdl-32647100
ABSTRACT
Recently, various two-dimensional materials have been reported to exhibit non-volatile resistance switching phenomenon. The atomristors, featuring memristor effect in atomically thin nanomaterials such as monolayer transition metal dichalcogenides and hexagonal boron nitride, have drawn much attention due to the extremely thin active layer thickness with the advantages of forming-free characteristic, large on/off resistance ratio and fast switching speed. To investigate the switching mechanisms in the 2D monolayers, we introduced an electrical characterization method by current sweeping to illustrate the detailed information hidden in the commonly used voltage-sweep curves. Multiple transition steps have been observed in the SET process of MoS2-based resistance switching devices. The different behaviors of transition steps were attributed to the number of defects or vacancies associated with the switching phenomenon, which is consistent with the previously reported conductive-bridge-like model for 2D atomristors. This work provides an approach using current sweeping to precisely characterize the resistance switching effect and inspires further research to optimize the defect distribution in 2D materials for the applications in multi-bit non-volatile memory and neuromorphic computing.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article