Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing

To overcome the coming big-data era, significant efforts are needed to implement neuromorphic computing that mimics the functional and structural features of the human brain using electronic devices. Repeated updates mimicking biological synaptic plasticity degrade the endurance of synaptic devices during in situ training. This study demonstrates a two-dimensional (2D) ferroelectric α-In₂Se₃-based synaptic device with enhanced durability via self-curing performed from current annealing (CA) in synaptic fatigue occurring during repetitive learning. The conduction, degradation, and self-curing mechanisms of the 2D ferroelectric-based synaptic device are quantitatively elucidated by low-frequency noise (LFN) spectroscopy. The classification accuracy of the Canadian Institute for Advanced Research (CIFAR)-10 dataset with self-cured conductance is superior to that of the device with synaptic fatigue and recovers to the initial accuracy level. The simulation results of removing defect cells through self-curing in the 2D ferroelectric synaptic array can help reduce energy consumption in the long term. The experimental results emphasize adopting 2D ferroelectric materials for future neuromorphic computing.