journal article·2025-04-23Open access

Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics Using High-Speed Reflection High-Energy Electron Diffraction

Yichen Guo, Peter Meisenheimer, Shuyu Qin, Xinqiao Zhang, Julian Goddy, Ramamoorthy Ramesh, Lane W. Martin, Joshua Agar

ACS Applied Materials & Interfaces· Volume 17· Issue 16

Abstract

High Resolution Image Download MS PowerPoint Slide Pulsed-laser deposition (PLD) is a powerful technique for growing complex oxides with controlled stoichiometry. To understand growth dynamics therein, it is common to leverage in situ spectroscopies, such as reflection high-energy electron diffraction (RHEED), to monitor surface crystallinity. Most commercial systems rely on video-rate cameras operating at 60–120 Hz that lack sufficient temporal resolution to capture growth dynamics at practical deposition frequencies. Here, a high-speed platform to record in situ dynamics via RHEED at >500 Hz is implemented. An open-source analysis package is designed to fit diffraction spots to 2D Gaussians, allowing single-pulse surface reconstruction kinetics extraction. Using homoepitaxially deposited (001)-oriented SrTiO 3 as a model system, we demonstrate how high-speed RHEED can provide real-time insight into growth processes obscured by slower acquisition systems. By fitting the single-pulse intensity to a set of exponential functions, we observe changes in the characteristic decay time and mechanism correlated to the substrate step width and surface termination. We observe distinct surface effects, with diffraction intensity decaying on lower-energy TiO 2 -terminated surfaces and stabilizing on SrO- or mixed-terminated surfaces. Similarly, using an exponential model, the extracted characteristic time of adatom deposition decreases with increased density of bonding sites associated with mixed termination and narrower step widths. Ultimately, this work shows how increasing RHEED temporal resolution can uncover new insights into growth processes, with practical implications for the design and control of PLD processes. This experimental platform provides new capabilities to enable data-driven machine learning analysis and autonomous control systems to enhance the complexity and fecundity of PLD.
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Research context

OpenAlex topics: Electronic and Structural Properties of Oxides · Magnetic and transport properties of perovskites and related materials · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Materials Science and Engineering

Lehigh University

Department of Mechanical Engineering and Mechanics

Drexel University

University of California

Department of Computer Science and Engineering

Department of Materials Science and Nanoengineering

Rice University

Department of Physics and Astronomy

Materials Sciences Division

Lawrence Berkeley National Laboratory

Funding

Advanced Scientific Computing Research · Army Research Office · Office of Advanced Cyberinfrastructure · Division of Advanced Cyberinfrastructure · Division of Materials Research

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