Stress and temperature analysis in semiconductor devices using multi-mode Raman thermography

Abstract

The accurate characterization of stress and temperature distributions in semiconductor devices is critical for ensuring their reliability, performance, and longevity, especially as device miniaturization and power density increase. This study investigates the application of multi-mode Raman thermography for simultaneous stress and temperature analysis in semiconductor materials, including silicon wafers, gallium nitride (GaN) thin films, and silicon carbide (SiC) substrates. The objective was to evaluate the accuracy and limitations of multi-mode Raman thermography by comparing experimental results with Finite Element Analysis (FEA) predictions. Experimental measurements were conducted using a high-resolution confocal Raman microscope equipped with 532 nm and 785 nm laser sources, and data were analyzed using spectral deconvolution techniques and computational modeling. Temperature and stress distributions were mapped across selected spatial points under controlled thermal and mechanical loads. The results showed moderate deviations in temperature measurements, with a Mean Absolute Error (MAE) of 15.28°C and a weak correlation coefficient (r = -0.11) when compared to FEA simulations. Stress measurements exhibited a lower MAE of 2.99 MPa but displayed an even weaker correlation coefficient (r = -0.017). These findings highlight the strengths of Raman thermography in identifying localized anomalies but also reveal limitations arising from spatial averaging effects, noise interference, and spectral overlap. Practical recommendations include enhanced calibration protocols, integration with complementary diagnostic tools such as infrared thermography and photoluminescence spectroscopy, and the adoption of advanced signal processing techniques for improved accuracy. Despite these limitations, multi-mode Raman thermography remains a powerful diagnostic tool for semiconductor analysis, capable of offering valuable insights into device behavior under operational conditions. Further research focusing on hybrid diagnostic frameworks and refined predictive models is essential to maximize the technique's potential in advancing semiconductor technology.