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Dielectric Engineered Two-Dimensional Neuromorphic Transistors.
Xiang, Du; Liu, Tao; Zhang, Xumeng; Zhou, Peng; Chen, Wei.
Afiliação
  • Xiang D; Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China.
  • Liu T; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China.
  • Zhang X; Department of Chemistry, National University of Singapore, Singapore 117543, Singapore.
  • Zhou P; Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China.
  • Chen W; Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China.
Nano Lett ; 21(8): 3557-3565, 2021 04 28.
Article em En | MEDLINE | ID: mdl-33835807
Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable synaptic arrays with complementary metal-oxide-semiconductor compatibility remains challenging. Here, we report a 2D transition metal dichalcogenide-based synaptic array fabricated on commercial silicon-rich silicon nitride (sr-SiNx) substrate. The array demonstrates uniform performance with sufficiently high analogue on/off ratio and linear conductance update, and low cycle-to-cycle variability (1.5%) and device-to-device variability (5.3%), which are essential for neuromorphic hardware implementation. On the basis of the experimental data, we further prove that the artificial synapses can achieve a recognition accuracy of 91% on the MNIST handwritten data set. Our findings offer a simple approach to achieve 2D synaptic arrays by using an industry-compatible sr-SiNx dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sinapses / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Nano Lett Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sinapses / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Nano Lett Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China