Dimensionality-guided sparse sampling for time-domain thermoreflectance: high-throughput thermal mapping of multilayer devices
Year: 2026
Type: article
Abstract: Thermal characterization of multilayer microelectronic devices requires efficient measurement strategies that balance accuracy against investigation resources. Time-domain thermoreflectance (TDTR) offers non-contact thermal property measurement, yet conventional approaches rely on densely sampled data and prior knowledge of material properties. We address these limitations through a data-driven framework combining dimensionality analysis with measurement optimization. Using an autoencoder, we reveal that across the broad range of bilayer systems in the diffusive regime, TDTR signals exhibit a low dimensionality: three degrees of freedom for single-frequency measurements, four for multifrequency measurements. Guided by this limit, Bayesian optimization identifies a nine-point sparse sampling configuration that captures information equivalent to 192-point scans. A neural network trained on this configuration enables simultaneous extraction of film thickness, thermal conductivity, and interfacial conductance where sensitivity permits. We validate the framework on SiO 2 /Si specimens and demonstrate its utility by mapping multilayer ceramic capacitors. Our approach achieves thermal property mapping without prior knowledge of the sample structure, transforming TDTR into a high-throughput image modality.
Authors: Yoonseong Jung, Jaywan Chung, Hyejin Jang
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,590
OpenAlex ID: W7225794902