A transfer-learning-enhanced POD-FNN surrogate for rapid signal prediction and inverse fitting in thermoreflectance with patterned transducers

TL;DR

This paper introduces a transfer-learning-enhanced POD-FNN surrogate model that significantly accelerates thermoreflectance signal prediction and inverse fitting, enabling cost-effective and rapid analysis in patterned transducer applications.

Contribution

It develops a novel transfer learning framework combined with POD-FNN for fast thermoreflectance modeling and inverse fitting, reducing computational time and data requirements.

Findings

Achieved median RMSE of 0.17 degrees in phase prediction.

Reduced prediction time per signal from 5.39 s to 0.01 s (about 534x).

Lowered high-fidelity data generation time from 34179 s to 5885 s.

Abstract

Patterned-transducer thermoreflectance enhances sensitivity to low-thermal-conductivity materials by suppressing lateral heat spreading in the metal transducer, but its wider use is limited by the cost of repeated high-fidelity forward evaluations in iterative fitting. Here, we develop a transfer-learning-enhanced POD-FNN surrogate for rapid phase prediction in patterned-transducer thermoreflectance, using patterned FDTR as a representative case. A validated COMSOL model is first constructed, and proper orthogonal decomposition is applied directly to the phase signals to build a compact reduced-order representation. A feedforward neural network is then trained to predict the POD coefficients from thermophysical and geometric parameters. Within the original parameter domain, the surrogate achieves mean and median RMSE values of 0.19 and 0.17 degrees, with a maximum RMSE below 0.47 degrees, while reducing the average prediction time per signal from 5.39 s to 0.01 s (about 534x). In inverse analysis, the fitting time for a representative case is reduced from about 18950 s to about 65 s with comparable accuracy. The framework is further applied to measured Al/SiO2 samples, yielding stable silica thermal conductivities of 1.44 +/- 0.088, 1.43 +/- 0.093, and 1.50 +/- 0.079 W/(m K) for conventional FDTR and patterned FDTR with pattern radii of 5.3 and 3.25 um, respectively. Transfer learning further improves performance in expanded parameter domains, with the TL-FR strategy giving the best overall results. Reducing the additional target-domain dataset from 6000 to 1000 samples also lowers the high-fidelity data-generation time from about 34179 s to about 5885 s. The proposed framework provides an accurate and efficient route for repeated forward evaluation, rapid inverse fitting, and cost-effective model updating in patterned thermoreflectance workflows.