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Open AccessArticle

Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate

1
School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea
2
Inter-University Semiconductor Research Center (ISRC) and the Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea
3
Materials Simulation Research Laboratory (MSRL), Department of Physics, Bahauddin Zakariya University Multan Pakistan, Multan 60800, Pakistan
4
Department of Physics, Khalifa University, Abu Dhabi 127788, UAE
5
Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Korea
6
Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Nanomaterials 2020, 10(5), 994; https://doi.org/10.3390/nano10050994
Received: 22 March 2020 / Revised: 16 May 2020 / Accepted: 16 May 2020 / Published: 22 May 2020
(This article belongs to the Section Nanophotonics: Characterization, Modelling, and Nanodevices)
Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n++-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure. View Full-Text
Keywords: memristor; silicon nitride; boron nitride; neuromorphic computing; resistive switching memristor; silicon nitride; boron nitride; neuromorphic computing; resistive switching
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MDPI and ACS Style

Rahmani, M.K.; Kim, M.-H.; Hussain, F.; Abbas, Y.; Ismail, M.; Hong, K.; Mahata, C.; Choi, C.; Park, B.-G.; Kim, S. Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate. Nanomaterials 2020, 10, 994.

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