Ferroelectric fatigue in layered perovskites from self-energy corrected density functional theory

Kan-Hao Xue,Leonardo R C Fonseca,Xiang-Shui Miao

doi:10.1039/c7ra01650f

Abstract

We employed GGA-1/2 to investigate the band alignment between platinum and various layered perovskite Aurivillius ferroelectrics. A model is proposed for ferroelectric fatigue in bismuth titanate based on our calculation.

Highlights

Ferroelectric random access memory[1] (FeRAM), as a novel nonvolatile semiconductor memory, has been successfully applied to smart IC cards and other embedded applications for decades
This work is organized as follows: in Section 2, we describe generalized gradient approximation (GGA)-1/2 and propose a new approach that is successful in the prediction of band gaps of several layered materials
Notwithstanding the band gap improvement brought by the GGA1/2 method in comparison to GGA for the calculation of Bicontaining layered compounds, its original implementation with 1/2 electron removal from all anions and a unique cutoff radius seems insufficient for the calculation of an accurate band gap comparable to experimental data

Summary

Introduction

The recent discovery of ferroelectricity in HfO2 (ref. 13) has greatly enhanced the application potential of FeRAM and nonvolatile ferroelectric eld effect transistors (FeFET) at the 21856 | RSC Adv., 2017, 7, 21856–21868. Given that Pt/SBT and Pt/BIT possess similar SBs, it is difficult to explain their great difference in leakage currents in terms of interface-limited mechanisms. To predict SBs numerically, it is necessary to perform accurate calculations of band gaps and band alignments for these complex Aurivilliusphase materials (typical examples are shown in Fig. 1) forming interfaces with metals, a task that o en involves models comprising over 200 atoms. Notwithstanding its many successes in accurately and efficiently predicting band gaps, we show that for layered materials, GGA-1/2 is improved, compared to GGA, but not enough in comparison to experimental data. This work is organized as follows: in Section 2, we describe GGA-1/2 and propose a new approach that is successful in the prediction of band gaps of several layered materials.

Computational method

Model of ferroelectric fatigue and dielectric breakdown in BIT

Findings

Conclusion