A Novel Analysis Framework for Microstructural Characterization of Ferroelectric Hafnia: Experimental Validation and Application

TL;DR

This paper introduces a new analysis framework for ferroelectric hafnia microstructure, combining ion beam treatment and deep neural networks, revealing new insights into grain size effects on device variability and reliability.

Contribution

The authors developed a novel microstructural analysis method that improves grain visibility and segmentation, challenging previous assumptions about grain size dependencies.

Findings

Grain size reduction decreases coercive field variability by ~68%.

Grain size reduction increases leakage current.

The new method uncovers discrepancies in prior grain size dependence results.

Abstract

Herein, we present a novel analysis framework for grain size profile of ferroelectric hafnia to tackle critical shortcomings inherent in the current microstructural analysis. We vastly enhanced visibility of grains with ion beam treatment and performed accurate grain segmentation using deep neural network (DNN). By leveraging our new method, we discovered unexpected discrepancies that contradict previous results, such as deposition temperature (Tdep) and post-metallization annealing (PMA) dependence of grain size statistics, prompting us to reassess earlier interpretations. Combining microstructural analysis with electrical tests, we found that grain size reduction had both positive and negative outcomes: it caused significant diminishing of die-to-die variation (~68 % decrease in standard deviation) in coercive field (Ec), while triggering an upsurge in leakage current. These uncovered results signify robustness of our method in characterization of ferroelectric hafnia for in-depth examination of both device variability and reliability.