Tu Jing, Haque Md Azimul, Baran Derya, Ong Wee-Liat
ZJU-UIUC Institute, College of Energy Engineering, Zhejiang University, Jiaxing 314400, China.
KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Fundam Res. 2023 Feb 23;5(1):288-295. doi: 10.1016/j.fmre.2023.01.010. eCollection 2025 Jan.
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.
我们通过数学推导得出了一种基于灵敏度的方法，该方法可识别适用于频域热反射（FDTR）实验中多变量非线性拟合的热输运物理特性和参数。现代电子设备通常由具有多种未知热输运特性的异质纳米层组成。然而，如果这些未知参数相关，那么在单个实验中对它们进行同时拟合将产生不可靠的结果。当前用于识别此类相关性的方法并不可靠。这种不可靠性阻碍了表征此类多层堆叠未知热特性的准确性和速度。我们提出的对数灵敏度比（LSR）分析可以评估拟合一对未知参数的可行性，并阐明主导的热输运物理特性。使用蒙特卡罗模拟和实际的FDTR实验对该分析的有效性和便利性进行了研究，以拟合多达三个未知参数。该方法背后的原理可以扩展到需要多变量拟合的其他技术。
