Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments
We mathematically derived a sensitivity-based method that identifies the thermal transport physics and parameters suitable for multivariate nonlinear fits in a frequency-domain thermoreflectance (FDTR) experiment. Modern electronic devices often consist of heterogeneous nanolayers with multiple unkn...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Fundamental Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667325823000341 |
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| author | Jing Tu Md Azimul Haque Derya Baran Wee-Liat Ong |
| author_facet | Jing Tu Md Azimul Haque Derya Baran Wee-Liat Ong |
| author_sort | Jing Tu |
| collection | DOAJ |
| description | We mathematically derived a sensitivity-based method that identifies the thermal transport physics and parameters suitable for multivariate nonlinear fits in a frequency-domain thermoreflectance (FDTR) experiment. Modern electronic devices often consist of heterogeneous nanolayers with multiple unknown thermal transport properties. However, simultaneous fitting in a single experiment for these unknown parameters will produce unreliable results if they are correlated. Current methods to identify such correlations are unreliable. This unreliability has impeded the accuracy and speed of characterizing the unknown thermal properties of such multilayer stacks. Our proposed logarithmic sensitivity ratio (LSR) analysis can evaluate the feasibility of fitting a pair of unknown parameters and clarify the governing thermal transport physics. The effectiveness and convenience of this analysis were studied using Monte Carlo simulations and actual FDTR experiments for fitting up to three unknown parameters. The principle behind this method can be extended to other techniques where multivariate fits are needed. |
| format | Article |
| id | doaj-art-f55e9b153b6846c4bd7e36b6ea7aee95 |
| institution | Kabale University |
| issn | 2667-3258 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Fundamental Research |
| spelling | doaj-art-f55e9b153b6846c4bd7e36b6ea7aee952025-01-29T05:02:31ZengKeAi Communications Co. Ltd.Fundamental Research2667-32582025-01-0151288295Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experimentsJing Tu0Md Azimul Haque1Derya Baran2Wee-Liat Ong3ZJU-UIUC Institute, College of Energy Engineering, Zhejiang University, Jiaxing 314400, ChinaKAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaKAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaZJU-UIUC Institute, College of Energy Engineering, Zhejiang University, Jiaxing 314400, China; State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China; Corresponding author.We mathematically derived a sensitivity-based method that identifies the thermal transport physics and parameters suitable for multivariate nonlinear fits in a frequency-domain thermoreflectance (FDTR) experiment. Modern electronic devices often consist of heterogeneous nanolayers with multiple unknown thermal transport properties. However, simultaneous fitting in a single experiment for these unknown parameters will produce unreliable results if they are correlated. Current methods to identify such correlations are unreliable. This unreliability has impeded the accuracy and speed of characterizing the unknown thermal properties of such multilayer stacks. Our proposed logarithmic sensitivity ratio (LSR) analysis can evaluate the feasibility of fitting a pair of unknown parameters and clarify the governing thermal transport physics. The effectiveness and convenience of this analysis were studied using Monte Carlo simulations and actual FDTR experiments for fitting up to three unknown parameters. The principle behind this method can be extended to other techniques where multivariate fits are needed.http://www.sciencedirect.com/science/article/pii/S2667325823000341MultivariablesMonte CarloCorrelationThermal propertiesThermoreflectanceThermal conductivity |
| spellingShingle | Jing Tu Md Azimul Haque Derya Baran Wee-Liat Ong Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments Fundamental Research Multivariables Monte Carlo Correlation Thermal properties Thermoreflectance Thermal conductivity |
| title | Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| title_full | Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| title_fullStr | Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| title_full_unstemmed | Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| title_short | Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| title_sort | logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments |
| topic | Multivariables Monte Carlo Correlation Thermal properties Thermoreflectance Thermal conductivity |
| url | http://www.sciencedirect.com/science/article/pii/S2667325823000341 |
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