This Letter presents a combined analytical and experimental method to effectively decouple the radial and tangential residual stress fields induced by Berkovich nanoindentation in single-crystalline 4H-SiC using micro-Raman spectroscopy. By integrating the Raman stress characterization model with Yoffe’s expanding cavity model, precise extraction of individual residual stress components around the indentation region is realized. Through the vertical backscattering micro-Raman mapping of the E2 phonon mode, we systematically investigate the residual stress distribution near the indentation. The results highlight significant anisotropy in nanoindentation-induced stress fields, strongly dependent on the crystal orientation of 4H-SiC, predominantly featuring radial tensile stress gradients. This comprehensive theoretical–experimental approach offers a robust optical framework for residual stress characterization in 4H-SiC and provides foundational insights for extending Raman spectroscopy-based stress characterization to other crystalline materials and related device structures.

High-energy Al ion implantation is an indispensable technique for achieving precise doping in fabricating 4H‑SiC devices. However, it inevitably introduces interfacial damage and residual stress that can compromise subsequent manufacturing processes and device reliability. Conventional destructive characterization techniques cannot provide real-time, in‑situ, nondestructive monitoring under process conditions, creating a major bottleneck in quality control. Here, we establish a predictive modeling framework that integrates multiscale simulations with advanced, non‑destructive micro‑Raman spectroscopy to systematically investigate the evolution of high-energy Al ion implantation–induced interface defects and residual stress in 4H-SiC. Simulation results reveal a linear relationship between the implantation dose and the formation of vacancies and interstitial defects, while the stress accumulation tends to saturate at higher doses due to a dynamic equilibrium among defect interactions. Complementary micro‐Raman spectroscopy corroborates the simulations, showing that the damaged interface layer deepens from approximately 300 nm at a dose of 1014 ions cm−2 to nearly 500 nm at 1016 ions cm−2, consistent with Monte Carlo predictions. Furthermore, the molecular dynamics simulations capture a trend of the implantation stress evolution with strong concurrence with the Raman-measured residual stress. This combined computational–experimental approach elucidates the fundamental mechanisms governing defect formation and residual stress in ion‑implanted 4H‑SiC, establishes implantation dose as the pivotal role of 4H-SiC in defect density and residual stress, and underscores the utility of optical‑based characterization in real‑time, non‑invasive quality control for advanced manufacturing.

4H-SiC is widely used in power electronics owing to its superior physical properties. However, temperature-induced stresses compromise the reliability of 4H-SiC power devices in high-temperature applications, warranting precise, and nondestructive stress characterization responsive to temperature variations. Herein, a temperature-dependent predictive model is proposed for analyzing the Raman shift–stress in 4H-SiC. The 4H-SiC epitaxial samples prepared via chemical vapor deposition are characterized using in situ variable-temperature Raman spectroscopy, resulting in a temperature correction factor of approximately −0.021 cm−1 K−1, which is integrated into the conventional Raman shift–stress relationship to assess stress variations induced by temperature variations. The elastic modulus tensor of 4H-SiC at various temperatures determined using molecular dynamics simulations indicates a linear reduction in modulus with increasing temperature. This variable temperature modulus is incorporated into the Raman shift–stress relationship. Furthermore, a finite element method is used for model simplification to perform stress calculations in three axial directions. The experimental results confirm the consistency between calculated and experimental values with a 10% error range under the uniaxial stress condition. The study findings provide valuable insights into assessing stress evolution in 4H-SiC under temperature variations based on Raman spectroscopy, thereby advancing the application of spectroscopic techniques in material stress detection.

This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer.