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.

Thermal transport properties for the isotropic and anisotropic characterization of nanolayers have been a significant gap in the research over the last decade. Multiple studies have been close to determining the thermal conductivity, diffusivity, and boundary resistance between the layers. The methods detailed in this work involve non-contact frequency domain pump-probe thermoreflectance (FDTR) and photothermal radiometry (PTR) methods for the ultraprecise determination of in-plane and cross-plane thermal transport properties. The motivation of one of the works is the advantage of the use of amplitude (TR signal) as one of the input parameters along with the phase for the determination of thermal parameters. In this article, we present a unique strategy for measuring the thermal transport parameters of thin films, including cross-plane thermal diffusivity, in-plane thermal conductivity, and thermal boundary resistance as a comprehensively reviewed article. The results obtained for organic and inorganic thin films are presented. Precise ranges for the thermal conductivity can be across confidence intervals for material measurements between 0.5 and 60 W/m-K for multiple nanolayers. The presented strategy is based on frequency-resolved methods, which, in contrast to time-resolved methods, make it possible to measure volumetric-specific heat. It is worth adding that the presented strategy allows for accurate (the signal in both methods depends on cross-plane thermal conductivity and thermal boundary resistance) and precise measurement.

The directional nature of heat conduction in semiconductor superlattices—marked by significant differences between in-plane and cross-plane pathways—poses substantial challenges for precise thermal property assessment. Conventional frequency-domain thermoreflectance (FDTR) techniques, while proficient at evaluating cross-plane thermal conductivity, suffer from restricted capability in resolving in-plane transport due to inherent phase-delay constraints and inadequate lateral resolution. In this investigation, we overcome these limitations through a hybrid analytical framework that integrates a non-contact Beam-offset Frequency-Domain Thermoreflectance (BO-FDTR) setup with a neural network initialization, a Gaussian Process Regression, and Bayesian Optimization (GPR-BO) technique. The enabling innovation is our inversion engine: neural networks provide instantaneous initial parameter estimates that seed a GPR-BO scheme, performing rigorous probabilistic inference to deliver final thermal conductivity values with 95 % credible intervals. This hybrid framework preserves the statistical rigor of Bayesian inversion while exploiting the computational efficiency of neural networks for accelerated experimental guidance. This method reduces estimation inaccuracies, attaining measurement uncertainties under 1 % to 2 % at 95 % confidence intervals. The technique demonstrates improved performance, furnishing a structured protocol for AlAs/GaAs superlattice (period thickness 52 nm) that delivers thermal conductivity values of 14.7 (cross-plane) and 37.4 W m−1 K−1 (in-plane). Our findings confirm that the neural-network-initialized GPR-BO protocol, applied to frequency sweeps with varied beam-offset locations, delivers superior measurement precision that exceeds conventional single-variable methods—validating the robustness of our thermal assessment across both geometric arrangements.