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To advance thermal control technology, improve thermal reuse efficiency, and further enhance device performance, it is crucial to understand microscopic spatial and temporal heat transport in materials. In this study, we developed a high-throughput time-domain thermoreflectance (HT-TDTR) technique that accelerates the measurement speed of thermophysical properties. The fundamental concept involves decomposing supercontinuum light into a pump pulse (1064 nm) and multiple delayed probe pulses (900 nm–730 nm) with different delays, enabling simultaneous acquisition of thermoreflectance signals at multiple delay times. Quartz glass, SrTiO3 (100) single crystal, and c-plane sapphire were heated with picosecond pulsed light, and the temporal temperature decrease at six delay times was simultaneously measured. The thermal effusivities analyzed based on heat diffusion equation were consistent with the literature values. Furthermore, we applied machine learning-based analysis and demonstrated the ability to determine thermophysical properties from measurement data consisting of only a few delay points. With sufficient signal strength, machine learning can predict a reasonable thermal effusivity based on experimental data obtained in less than a second. HT-TDTR enables rapid and accurate measurement of samples based on information about thermal relaxation dynamics, facilitating more efficient characterization of thermophysical properties.

権利情報:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

キーワード: Time-domain thermoreflectance, multiple delay, thin film, thermal effusivity, interfacial thermal resistance, machine learning

刊行年月日: 2025-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 14686996) vol. 26 2523240

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5551

公開URL: https://doi.org/10.1080/14686996.2025.2523240

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更新時刻: 2025-07-16 16:14:58 +0900

MDRでの公開時刻: 2025-06-28 08:19:42 +0900