Beam-Offset Thermoreflectance with Bayesian Optimization to Measure the Anisotropic Thermal Properties of Semiconductor Superlattices
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 establish a non-contact beam-offset FDTR (BO-FDTR) approach that concurrently determines both directional thermal conductivities within layered semiconductor architectures. Our methodology implements spatial separation between excitation and detection beams while utilizing coupled normalized amplitude and phase responses as analytical inputs, thereby improving discrimination between anisotropic thermal parameters. We combine this experimental configuration with a Bayesian optimization scheme incorporating Gaussian Process Regression (BO-GPR) to reduce estimation inaccuracies, attaining measurement uncertainties under 1% to 2% at 95% confidence intervals. This technique demonstrates particular efficacy for intricate multilayer nanostructures, furnishing a structured protocol for superlattice thermal evaluation. Experimental characterization of an AlAs/GaAs superlattice (period thickness 52 nm) delivers thermal conductivity values of 14.7 W m-1 K-1 (cross-plane) and 37.4 W m-1 K-1 (in-plane). Our findings indicate that integrating frequency sweeps with varied beam offset locations yields superior measurement precision, exceeding conventional single-variable methods and confirming thermal assessment validity across both geometric arrangements.