Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing.

Abstract

Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.