Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

TL;DR

This paper introduces a novel, derivative-free min-max optimization method for accurately and rapidly identifying 3D dislocation lines in nanocrystals using Bragg coherent x-ray diffractive imaging, outperforming existing techniques in speed and accuracy.

Contribution

The authors develop and validate a new min-max optimization approach that improves accuracy and efficiency in detecting dislocation lines in BCDI images, surpassing derivative-based and manual methods.

Findings

Achieves 260x faster processing speed.

Provides higher accuracy than existing methods.

Enables better defect imaging in nanomaterials.

Abstract

Defects such as dislocations impact materials properties and their response during external stimuli. Defect engineering has emerged as a possible route to improving the performance of materials over a wide range of applications, including batteries, solar cells, and semiconductors. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. However, the advent of Bragg coherent x-ray diffractive imaging (BCDI) has made possible the 3D imaging of multiple dislocations in nanoparticles ranging in size from 100 nm to1000 nm. While the imaging process succeeds in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative- or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 260x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide the MATLAB code for use by other researchers.