Machine learning-based data processing technique for time-domain thermoreflectance (TDTR) measurements

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.