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A Simple Strategy to Realize Super Stable Ferroelectric Capacitor via Interface Engineering

Hyo‐Bae Kim, Kyun Seong Dae, Youkyoung Oh, Seung‐Won Lee, Yoseop Lee, Seung‐Eon Ahn, Jae Hyuck Jang, Ji‐Hoon Ahn

2022Advanced Materials Interfaces28 citationsDOI

Abstract

Abstract Fluorite‐structure ferroelectric thin films have been extensively studied as promising candidates for next‐generation non‐volatile memory. However, these ferroelectric thin films have fatal issues such as the irregular formation of the ferroelectric phase, low cycling endurance, and wake‐up and fatigue during endurance cycling tests. These problems are reportedly caused by oxygen vacancies, which form due to the interface reaction between the thin films and bottom electrodes during deposition and the post‐annealing process. Therefore, in this work, the enhanced ferroelectric characteristics of Hf 1‐x Zr x O 2 thin films that control the oxygen vacancies in thin films through interfacial pretreatment are investigated. Interfacial treatment using an oxygen source can reduce oxygen vacancies and improve crystallinity through intentional oxidation of the bottom electrode. As a result, the remanent polarization value is increased by ≈1.6 times by applying the optimized pretreatment condition, and the measured 2 P r is a very high value of 73 µC cm −2 . Furthermore, it exhibits very stable ferroelectric properties without a wake‐up effect or significant fatigue, up to 10 8 cycles even under a severe electric field of 3.5 MV cm −1 . This simple strategy provides a new avenue to effectively improve the performance and cycling endurance of devices with ferroelectric thin films.

Topics & Concepts

FerroelectricityMaterials scienceThin filmCrystallinityAnnealing (glass)Polarization (electrochemistry)ElectrodeCapacitorOxygenOptoelectronicsComposite materialNanotechnologyVoltageDielectricElectrical engineeringOrganic chemistryChemistryEngineeringPhysical chemistryFerroelectric and Negative Capacitance DevicesFerroelectric and Piezoelectric MaterialsAdvanced Memory and Neural Computing