Interface engineering modulation of ferroelectric synapses for high-precision neuromorphic computing

Abstract

Nb:SrTiO3 (NSTO) are commonly employed as substrate and electrode for BaTiO3-based ferroelectric memristors. These substrates are available in two types. The first one is with the hybrid-terminated interface, which consists of alternating SrO and TiO2 planes, while the second one is a TiO2-terminated interface. The interaction between surfaces and interfaces plays a crucial role in determining the overall performance of synapses. This paper reports a ferroelectric synapse whose neuromorphic performance can be regulated by the terminated interface of NSTO substrate. Compared with hybrid-terminated devices, the TiO2-terminated devices exhibit a 0.38 eV increase in barrier height, a 38.2% reduction in dislocation density, an approximately 10-fold enhancement in the on/off ratio, a 47.1% improvement in the linearity of long-term potentiation, and a 57.1% improvement in the linearity of long-term depression. This is due to their lower surface state density and atomically flat surface topography. In addition, the TiO2-terminated devices accurately emulate the characteristics of artificial synapses, and the neural network developed based on the weight update characteristics of the memristor achieves an image recognition accuracy of 96.1% on the National Institute of Standards and Technology handwritten digit dataset.