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Artificial Synapses Based on WSe2 Homojunction via Vacancy Migration.
Ren, Junwen; Shen, Hongzhi; Liu, Zeyi; Xu, Ming; Li, Dehui.
Affiliation
  • Ren J; School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Shen H; School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Liu Z; School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Xu M; School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Li D; Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
ACS Appl Mater Interfaces ; 14(18): 21141-21149, 2022 May 11.
Article de En | MEDLINE | ID: mdl-35481365
Artificial synapses based on two-dimensional (2D) transition metal dichalcogenides (TMDs) materials have attracted wide attention to boost the development of neuromorphic computing in recent years. Various structures have been adopted to build 2D-material-based artificial synapses. In lateral- and vertical-structures, the realization of synaptic function mainly results from the migration of the defects and vacancies, which requires the strong ion diffusion ability. Here, we successfully demonstrate an artificial synapse based on lateral WSe2 homojunction. The migration of Se vacancies from the thin region to the thick region has been promoted by applying negative gate voltage, resulting in n-type doping in the thick region due to the accumulation of Se vacancies, which would diminish the barrier width of the metal-semiconductor junctions in the thick region. Consequently, the transformation from a high-resistance state (HRS) to a low-resistance state (LRS) is achieved. Significantly, our device can efficiently emulate the biological synaptic functions with a large synaptic weight change. Additionally, the transition from short-term memory (STM) to long-term memory (LTM) can be accomplished with a simpler structure, which would be beneficial to realizing the large-scale integration of transistor-based artificial synapses.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: ACS Appl Mater Interfaces Sujet du journal: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Année: 2022 Type de document: Article Pays d'affiliation: Chine Pays de publication: États-Unis d'Amérique

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: ACS Appl Mater Interfaces Sujet du journal: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Année: 2022 Type de document: Article Pays d'affiliation: Chine Pays de publication: États-Unis d'Amérique