Abstract

Thermoreflectance techniques, including time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), and the square-pulsed source (SPS) method, are powerful tools for characterizing the thermal properties of bulk and thin-film materials. However, accurately interpreting their signals remains challenging due to intricate interdependencies among experimental variables. This study introduces a systematic framework based on singular value decomposition (SVD) to decouple these interdependent parameters and enhance the reliability of thermal property extraction. By applying SVD to the sensitivity matrix, the authors identify key parameter combinations and establish essential dimensionless numbers governing thermoreflectance signals. The framework is applied to a GaN/Si heterostructure, where the performance of TDTR, FDTR, and SPS is evaluated and compared. The results demonstrate strong consistency across all three techniques and suggest the potential to simultaneously and accurately extract multiple thermal properties of the multilayer system.

Abstract

Thermoreflectance techniques, including time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), and the square-pulsed source (SPS) method, are powerful tools for characterizing the thermal properties of bulk and thin-film materials. However, accurately interpreting their signals remains challenging due to intricate interdependencies among experimental variables. This study introduces a systematic framework based on singular value decomposition (SVD) to decouple these interdependent parameters and enhance the reliability of thermal property extraction. By applying SVD to the sensitivity matrix, the authors identify key parameter combinations and establish essential dimensionless numbers governing thermoreflectance signals. The framework is applied to a GaN/Si heterostructure, where the performance of TDTR, FDTR, and SPS is evaluated and compared. The results demonstrate strong consistency across all three techniques and suggest the potential to simultaneously and accurately extract multiple thermal properties of the multilayer system.

Abstract

Thermoreflectance techniques, including time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), and the square-pulsed source (SPS) method, are powerful tools for characterizing the thermal properties of bulk and thin-film materials. However, accurately interpreting their signals remains challenging due to intricate interdependencies among experimental variables. This study introduces a systematic framework based on singular value decomposition (SVD) to decouple these interdependent parameters and enhance the reliability of thermal property extraction. By applying SVD to the sensitivity matrix, the authors identify key parameter combinations and establish essential dimensionless numbers governing thermoreflectance signals. The framework is applied to a GaN/Si heterostructure, where the performance of TDTR, FDTR, and SPS is evaluated and compared. The results demonstrate strong consistency across all three techniques and suggest the potential to simultaneously and accurately extract multiple thermal properties of the multilayer system.

Abstract

Thermoreflectance techniques, including time-domain thermoreflectance (TDTR), frequency-domain thermoreflectance (FDTR), and the square-pulsed source (SPS) method, are powerful tools for characterizing the thermal properties of bulk and thin-film materials. However, accurately interpreting their signals remains challenging due to intricate interdependencies among experimental variables. This study introduces a systematic framework based on singular value decomposition (SVD) to decouple these interdependent parameters and enhance the reliability of thermal property extraction. By applying SVD to the sensitivity matrix, the authors identify key parameter combinations and establish essential dimensionless numbers governing thermoreflectance signals. The framework is applied to a GaN/Si heterostructure, where the performance of TDTR, FDTR, and SPS is evaluated and compared. The results demonstrate strong consistency across all three techniques and suggest the potential to simultaneously and accurately extract multiple thermal properties of the multilayer system.