التفاصيل البيبلوغرافية
| العنوان: |
Effect of amplitude measurements on the precision of thermal parameters' determination in GaAs using frequency-resolved thermoreflectance. |
| المؤلفون: |
Chatterjee, Ankur, Dziczek, Dariusz, Song, Peng, Liu, J., Wieck, Andreas. D., Pawlak, Michal |
| المصدر: |
Journal of Applied Physics; 6/14/2024, Vol. 135 Issue 22, p1-11, 11p |
| مصطلحات موضوعية: |
THERMAL conductivity, INTERFACIAL resistance, MONTE Carlo method, DEEP learning, SEMICONDUCTOR materials, AUDITING standards, THERMAL diffusivity |
| مستخلص: |
Non-contact photothermal pump-probe methodologies such as Frequency-Domain Thermo-Reflectance (FDTR) systems facilitate the examination of thermal characteristics spanning semiconductor materials and their associated interfaces. We underscore the significance of meticulous measurements and precise error estimation attained through the analysis of both amplitude and phase data in Thermo-Reflectance (TR). The precision of the analytical estimation hinges greatly on the assumptions made before implementing the method and notably showcases a decrease in errors when both the amplitude and phase are incorporated as input parameters. We demonstrate that frequency-domain calculations can attain high precision in measurements, with error estimations in thermal conductivity (k), thermal boundary resistance (Rth), and thermal diffusivity (α) as low as approximately 2.4%, 2.5%, and 3.0%, respectively. At the outset, we evaluate the uncertainty arising from the existence of local minima when analyzing data acquired via FDTR, wherein both the phase and amplitude are concurrently utilized for the assessment of cross-plane thermal transport properties. Expanding upon data analysis techniques, particularly through advanced deep learning approaches, can significantly enhance the accuracy and precision of predictions when analyzing TR data across a spectrum of modulation frequencies. Deep learning models enhance the quality of fitting and improve the accuracy and precision of uncertainty estimation compared to traditional Monte Carlo simulations. This is achieved by providing suitable initial guesses for data fitting, thereby enhancing the overall performance of the analysis process. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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