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
Abstract
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.