Visualizing electrothermal dynamics in ovonic threshold switches via in-situ thermoreflectance for enhanced reliability
This study investigates the thermal behavior of Te-rich GeTe (GT)- and Se-doped GeTe (SGT)-based ovonic threshold switch (OTS) devices, focusing on understanding electrothermal dynamics and enhancing device reliability. Thermoreflectance-based thermal imaging (TTI) enables direct visualization of localized conductive channels, offering insights into heat localization during switching. Time-domain thermoreflectance (TDTR) further provides accurate thermal properties of the amorphous films, enabling electrothermal (ET) simulations. These simulations reveal that internal temperatures in GT-based devices may approach crystallization thresholds during operation, emphasizing the need for optimized thermal design. In contrast, SGT-based devices exhibit improved thermal stability, attributed to stronger bonding and reduced Joule heating, thereby maintaining their structural integrity during switching. The combination of experimental thermal mapping and predictive modeling offers a powerful framework for identifying potential failure points and guiding material design. By comparing GT and SGT systems, the study highlights how targeted doping strategies and current optimization can significantly improve thermal resilience and device durability. These findings provide a comprehensive understanding of thermal dynamics in OTS devices and demonstrate a path forward for the development of more robust and reliable selectors.
Authors
- Hyun Kyu Seo
- M.S. Shakouri (ORCID: https://orcid.org/0009-0001-5737-3655)
- Byung Joon Choi (ORCID: https://orcid.org/0000-0003-1920-8162)
- Dustin Kendig (ORCID: https://orcid.org/0009-0000-4335-6865)
- Min Kyu Yang (ORCID: https://orcid.org/0000-0001-7219-7816)
- Hyun Wook Kim (ORCID: https://orcid.org/0000-0001-9101-1028)
- Jae Hyuck Jang
- Ju Hwan Park
- Gun Hwan Kim (ORCID: https://orcid.org/0000-0002-2911-4525)
Institutions
- Seoul National University of Science and Technology (KR)
- Micron (United States) (US)
- Yonsei University (KR)
- Sahmyook University (KR)
- University Health System (US)
- Korea Basic Science Institute (KR)
- Yonsei University Health System (KR)
Publication Details
- Journal
- Materials Today Advances
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1016/j.mtadv.2025.100602
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00