Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devices

In this paper, we show various memory characteristics of the Ag/AlN/TiN devices for neuromorphic systems. We verified the thickness and the components of the device stack by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). We investigated the long-term memory (LTM) characteristics, and short-term memory (STM) characteristics can be determined by compliance current (CC). It shows LTM characteristics when CC is high and STM characteristics when CC is low. I-V curves for each characteristic were investigated, and potentiation and depression for LTM characteristics. The switching and conduction mechanisms of Ni/Ag/AlN/TiN devices are studied using the schematic drawing of the conducting filament and the energy band diagram, including the work function, electron affinity, and bandgap energy of each layer. The linearity of potentiation and depression was compared for an identical pulse and an incremental pulse. Finally, we investigated Modified National Institute of Standards and Technology (MNIST) pattern accuracy depending on the linearity of potentiation and depression.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the memory characteristics of Ag/AlN/TiN devices for neuromorphic systems were investigated. The thickness and components of the device stack were verified using TEM and EDS. The long-term memory (LTM) and short-term memory (STM) characteristics were determined by compliance current (CC), with LTM characteristics observed at high CC and STM characteristics observed at low CC. The I-V curves and potentiation and depression for LTM characteristics were studied. The switching and conduction mechanisms of Ni/Ag/AlN/TiN devices were analyzed using schematic drawings and energy band diagrams. The linearity of potentiation and depression was compared, and the accuracy of Modified National Institute of Standards and Technology (MNIST) pattern was evaluated based on the linearity.