A Numerical Fitting Routine for Frequency-domain Thermoreflectance Measurements of Nanoscale Material Systems having Arbitrary Geometries
Ronald J. Warzoha, Adam A. Wilson, Brian F. Donovan, Andrew N. Smith,, Nicholas T. Vu, Trent Perry, Longnan Li, Nenad Miljkovic, Elizabeth Getto

TL;DR
This paper introduces a numerical fitting routine based on finite element analysis for frequency-domain thermoreflectance measurements, enabling thermal property extraction of materials with arbitrary geometries beyond traditional semi-infinite models.
Contribution
It develops and validates a finite element-based numerical fitting routine for FDTR, allowing accurate thermal measurements of complex geometries where analytical solutions fail.
Findings
The routine accurately extracts thermal conductivity for semi-infinite substrates.
It successfully measures thermal properties of Si micropillars with non-standard geometries.
Analytical solutions are inadequate for complex geometries, highlighting the routine's importance.
Abstract
In this work, we develop a numerical fitting routine to extract multiple thermal parameters using frequency-domain thermoreflectance (FDTR) for materials having non-standard, non-semi-infinite geometries. The numerical fitting routine is predicated on either a 2-D or 3-D finite element analysis that permits the inclusion of non semi-infinite boundary conditions, which can not be considered in the analytical solution to the heat diffusion equation in the frequency domain. We validate the fitting routine by comparing it to the analytical solution to the heat diffusion equation used within the wider literature for FDTR and known values of thermal conductivity for semi-infinite substrates (SiO2, Al2O3 and Si). We then demonstrate its capacity to extract the thermal properties of Si when etched into micropillars that have radii on the order of the pump beam. Experimental measurements of Si…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
