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Time-domain thermoreflectance (TDTR) is a powerful pump–probe technique for measuring thermal properties of materials and interface thermal conductance. However, a diffusive thermal transport model is often used for data analysis, leading to underestimated thermal conductivities for high thermal conductivity materials, for example, single-crystalline semiconductors like Si at low temperatures. In this work, we have developed a theoretical model based on phonon hydrodynamics, an approximation of the phonon Boltzmann transport equation, for TDTR data analysis. We apply this model to process the TDTR signals of Si measured between 80 and 300 K. The extracted thermal conductivities using the phonon hydrodynamic model agree remarkably well with the bulk values measured by the steady-state technique, providing a more appropriate way of TDTR data analysis. The effectiveness of the phonon hydrodynamic model is further verified by analyzing TDTR signals of Ge at room temperature.

Machine learning (ML) has emerged as an increasingly important research tool and has shown great potential for efficient and high-throughput experimental data processing. Meanwhile, ultrafast laser-based time-domain thermoreflectance (TDTR) has been developed into a powerful thermal characterization technique and has been widely applied to measure thermal properties of both bulk and thin-film materials. In this work, artificial neural network-based ML models have been trained for data processing in TDTR experiments. One generally applicable ML model could be trained to process the experimental data of different samples measured using different modulation frequencies and laser spot sizes. Our results suggest that ML is not only fast and efficient in data processing but also accurate and powerful, capable of detecting minute features in the experimental signals and thus enabling extraction of multiple (three or more) parameters simultaneously from the experimental data. The ML model also enables high-speed estimation of the uncertainties of multiple parameters using the Monte Carlo method.