| Literature DB >> 33835807 |
Du Xiang1,2, Tao Liu3, Xumeng Zhang1, Peng Zhou1,4, Wei Chen3,5,6,7.
Abstract
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.Entities:
Keywords: ANN simulation; TMDC-based synaptic array; analogue nonvolatile memory; low cycle-to-cycle and device-to-device variability; silicon-rich silicon nitride substrate
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Year: 2021 PMID: 33835807 DOI: 10.1021/acs.nanolett.1c00492
Source DB: PubMed Journal: Nano Lett ISSN: 1530-6984 Impact factor: 11.189