Memristive and synaptic characteristics of nitride-based heterostructures on si substrate

Mehr Khalid Rahmani, Min Hwi Kim, Fayyaz Hussain, Yawar Abbas, Muhammad Ismail, Kyungho Hong, Chandreswar Mahata, Changhwan Choi, Byung Gook Park, Sungjun Kim

    Research output: Contribution to journalArticlepeer-review

    19 Scopus citations


    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.

    Original languageBritish English
    Article number994
    Issue number5
    StatePublished - May 2020


    • Boron nitride
    • Memristor
    • Neuromorphic computing
    • Resistive switching
    • Silicon nitride


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