Thermal Property Microscopy with Compressive Sensing Frequency-Domain Thermoreflectance

TL;DR

This paper presents CS-FDTR, a high-throughput thermal property imaging method that reconstructs detailed thermal maps from sparse data using compressive sensing, enabling faster material analysis without significant loss of accuracy.

Contribution

The introduction of CS-FDTR, combining frequency-domain thermoreflectance with compressive sensing, allows rapid thermal property imaging with reduced experimental measurements.

Findings

Accurately reconstructs thermal maps with less than half the data.

Achieves less than 15% deviation from ground truth.

Validates high-throughput imaging on various material interfaces.

Abstract

Spatial mapping of thermal properties is critical for unveiling the structure-property relation of materials, heterogeneous interfaces, and devices. These property images can also serve as datasets for training artificial intelligence models for material discoveries and optimization. Here we introduce a high-throughput thermal property imaging method called compressive sensing frequency domain thermoreflectance (CS-FDTR), which can robustly profile thermal property distributions with micrometer resolutions while requiring only a random subset of pixels being experimentally measured. The high-resolution thermal property image is reconstructed from the raw down-sampled data through L_1-regularized minimization. The high-throughput imaging capability of CS-FDTR is validated using the following cases: (a) the thermal conductance of a patterned heterogeneous interface, (b) thermal conductivity variations of an annealed pyrolytic graphite sample, and (c) the sharp change in thermal conductivity across a vertical aluminum/graphite interface. With less than half of the pixels being experimentally sampled, the thermal property images measured using CS-FDTR show nice agreements with the ground truth (point-by-point scanning), with a relative deviation below 15%. This work opens the possibility of high-throughput thermal property imaging without sacrificing the data quality, which is critical for materials discovery and screening.