Deep learning-based data processing method for transient thermoreflectance measurements
At a Glance
Section titled āAt a Glanceā| Metadata | Details |
|---|---|
| Publication Date | 2024-03-01 |
| Journal | Journal of Applied Physics |
| Authors | Yali Mao, Shaojie Zhou, Weiyun Tang, Mei Wu, Haochen Zhang |
| Institutions | University of Science and Technology of China, Wuhan University |
| Citations | 10 |
Abstract
Section titled āAbstractāPump-probe thermoreflectance has been commonly applied for characterizing the thermal properties of materials. Generally, a reliable and efficient non-linear fitting process is often implemented to extract unknown thermal parameters during the pump-probe thermoreflectance characterizations. However, when it comes to processing large amounts of data acquired from similar structural samples, non-linear fitting process appears to be very time-consuming and labor-intensive to search for the best fitting for every testing curve. Herein, we propose to apply deep learning (DL) approach to nanosecond transient thermoreflectance technique for high-throughput experimental data processing. We first investigated the effect of training set parameters (density and bounds) on the predictive performance of the DL model, providing a guidance to optimize the DL model. Then, the DL model is further verified in the measurement of the bulk sapphire, SiC, diamond samples, and GaN-based multilayer structures, demonstrating its capability of analyzing the results with high accuracy. Compared to the conventional non-linear fitting method (such as Global Optimization), the computation time of the new model is 1000 times lower. Such a data-driven DL model enables the faster inference and stronger fitting capabilities and is particularly efficient and effective in processing data acquired from wafer-level measurements with similar material structures.
Tech Support
Section titled āTech SupportāOriginal Source
Section titled āOriginal SourceāReferences
Section titled āReferencesā- 2004 - Analysis of heat flow in layered structures for time-domain thermoreflectance [Crossref]
- 2010 - Ultrafast thermoreflectance techniques for measuring thermal conductivity and interface thermal conductance of thin films [Crossref]
- 2016 - Thermal boundary resistance in GaN films measured by time domain thermoreflectance with robust Monte Carlo uncertainty estimation [Crossref]
- 2009 - A frequency-domain thermoreflectance method for the characterization of thermal properties [Crossref]
- 2010 - Characterization of thin metal films via frequency-domain thermoreflectance [Crossref]
- 2023 - Quantitative study on thermoreflectance linear relation [Crossref]
- 2019 - Nanosecond transient thermoreflectance method for characterizing anisotropic thermal conductivity [Crossref]
- 2005 - High room-temperature figure of merit of thin layers prepared by laser ablation from Bi2Te3 target [Crossref]
- 2015 - Assessment of thermal properties via nanosecond thermoreflectance method [Crossref]
- 2019 - Picosecond transient thermoreflectance for thermal conductivity characterization [Crossref]