Uniform resistive switching and highly stable synaptic characteristics of HfOx sandwiched TaOx-based memristor for neuromorphic system

Abstract

Emerging nanoscale devices, including memristors, have been extensively studied to implement biological synaptic functions such as learning and plasticity, which are the fundamental building blocks of brain-inspired neuromorphic computing. The memristor with analog switching ability exhibits linear tuning of weight during neural network training is a desirable synaptic device behavior. The importance of inserting a HfOx sandwiched layer in a TaOx/HfOx/TaOx memristor is to achieve analog set/reset operation along with improved spatial/temporal switching uniformity. The optimal resistive switching (RS) behavior can be attributed to asymmetric oxygen vacancy distribution in the stacked structure leading to the formation of an hourglass-shaped conductive filament. Furthermore, confining filament formation/rapture in the narrow fixed region displays superior endurance characteristics (dc cycles >2000 and ac cycles > 106) and uniform resistive switching with the set (reset) voltage variation constrained to 1.8 (2.9) %. Paired-pulse facilitation (PPF), a form of short-term synaptic plasticity is stimulated to replicate bio-synapse behavior. The stable long-term potentiation (LTP) and depression (LTD) behavior for more than 1000 epochs (>105 pulses) with excellent symmetry and linearity is achieved with 50 ns voltage pulse stimulation. The pattern recognition accuracy of 93% was achieved for an image of size 10 × 10 pixels after 13 epochs by deploying 100 synapses in Hopfield Neural Network (HNN) simulation. This comprehensive study demonstrates that the HfOx-inserted TaOx memristor has tremendous potential for application in future neuromorphic computing.