Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session B53: Autonomous Control
11:30 AM–2:18 PM,
Monday, March 6, 2023
Room: Room 307
Sponsoring
Units:
GDS DQI
Chair: Maria Longobardi, University of Basel, Switzerland
Abstract: B53.00009 : Automated and autonomous scanning probe experiments for manipulating and measuring domain wall properties in ferroelectric thin films*
1:30 PM–1:42 PM
Presenter:
Rama K Vasudevan
(Oak Ridge National Lab)
Authors:
Sai M Valleti
(UT Bredesen Center for Interdisciplinary Research, Knoxville)
Yongtao Liu
(Oak Ridge National Laboratory)
Bharat Pant
(University of Texas, Arlington)
Shivaranjan Raghuraman
(Oak Ridge National Laboratory)
Maxim Ziatdinov
(Oak Ridge National Lab)
Jan-Chi Yang
(National Cheng Kung University)
Ye Cao
(University of Texas, Arlington)
Stephen Jesse
(OAK RIDGE NATIONAL LABORATORY)
Sergei V Kalinin
(University of Tennessee, Knoxville)
Rama K Vasudevan
(Oak Ridge National Lab)
Traditionally, manipulation of ferroelectric domains at the nanoscale has been achieved largely through piezoresponse force microscopy (PFM), where the electric field is concentrated below the PFM tip and allows domains and domain walls to be both perturbed and measured. However, this manipulation has traditionally been slow and done by human operators, or in automated regimes where e.g., the full hysteresis loop is captured across a grid of points, which does not allow for feedback.2
Here, we introduce a new class of automated experiments which use image-based feedback to find domain wall depinning fields. Initially, a domain is written in (110)-oriented PbTiO3 thin films, and then scanned to locate domain wall, and then a bias pulse is applied at the center of the wall. If the wall is moved as a result of this pulse, the next section of the wall is scanned; else the bias pulse magnitude is increased until such time that movement is detected. In this way, the local depinning field can be mapped and correlated with the local domain structure. Extensions to finding the bias pulse amplitude-pulse width ‘phase diagram’ for wall depinning, via structured Gaussian processes3, or reinforcement learning, will be discussed.
References
- 1. Meier, Dennis, and Sverre M. Selbach. Nature Reviews Materials 7.3 (2022): 157-173.
2. Jesse, Stephen, Arthur P. Baddorf, and Sergei V. Kalinin. Applied physics letters 88.6 (2006): 062908.
3. Ziatdinov, Maxim A., Ayana Ghosh, and Sergei V. Kalinin. Machine Learning: Science and Technology 3.1 (2022): 015022.
*The microscopy experiments were supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering. This machine learning and automation work was supported by Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.
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