Reliable 4H-SiC for high-power electronics and quantum photonics requires a quantitative understanding of how contact loading drives microstructure evolution and load-bearing/fracture response in epitaxial layers. Here, we integrate instrumented indentation, confocal micro-Raman residual-stress metrology, atomistic molecular dynamics (MD), and high-resolution TEM (HRTEM) to establish processing–microstructure–mechanical property linkages in chemical vapor deposition (CVD) 4H-SiC epilayers. At peak depths of 600–1050 nm, indentation promotes Palmqvist-type radial cracks and the apparent indentation toughness K_IC increases from 0.87 ± 0.08 to 1.20 ± 0.05 MPa m^1/2 with depth, consistent with plastic-zone growth and dislocation shielding. E₂(TO) Raman mapping quantifies an increase in residual stress from ∼302 ± 60 to ∼665 ± 72 MPa. It also shows that the incremental broadening of the FWHM becomes less pronounced beyond ∼750 nm, suggesting that the near-surface disorder indicator within the Raman probe volume approaches a quasi-steady level. MD captures a 4H → 3C phase transformation, amorphization beneath indenter ridges, and dislocation nucleation/growth, which HRTEM directly corroborates. The combined measurement–model–validation closed loop yields a depth-dependent relationship between residual-stress accumulation and apparent toughness, converting them into an actionable processing window: constraining penetration depth below ∼0.75 μm limits residual stress and near-surface disorder. These results provide physics-based guidance for machining and packaging of 4H-SiC epilayers and illustrate a transferable framework for brittle, anisotropic ceramics.

Ion implantation and subsequent annealing reshape the defect landscape and stress state of compound semiconductors, yet the temperature-dependent mechanisms in SiC remain incompletely understood. Here, we utilize molecular dynamics (MD) simulations and confocal micro-Raman measurements to resolve how implantation temperature and post-annealing regulate lattice disorder, amorphization kinetics, and residual-stress evolution in chemical vapor deposited (CVD) 4H-SiC. MD reveals surface-nucleated amorphization that propagates inward, whereas elevated implantation temperatures activate defect recombination pathways that suppress amorphous-layer formation. Raman signatures of optical-phonon shifts, linewidth broadening, and amorphization bands track the coupled evolution of lattice disorder and stress. Experimentally, increasing implantation temperature smooths the surface (S_a 0.133 → 0.101 nm) and reduces the amorphous-layer thickness (from ∼700 nm at 25°C to undetectable at 500°C), while driving more compressive residual stress (−57 → −132 MPa). Post-annealing largely restores phonon lifetimes and eliminates amorphization signatures, consistent with the recovery trends predicted by MD. These results delineate a thermal-treatment window that controls amorphization and residual stress in 4H-SiC, providing a transferable Raman-based methodology for nondestructive assessment of implantation-induced damage in compound semiconductors.

4H-SiC is widely employed in power electronic devices operating under high frequencies, voltages, and temperatures due to its exceptional physical properties. However, its inherent high hardness and elastic modulus induce inevitable residual stress during device fabrication. Raman spectroscopy, which leverages lattice dynamics, offers an effective, non-destructive, rapid, and contactless method for measuring these stresses. Nevertheless, its accuracy critically depends on precisely determining the Raman phonon deformation potential constant. This work investigates mechanically induced Raman shifts in 4H-SiC via first-principles calculations and in-situ Raman spectroscopy under hydrostatic and non-hydrostatic stress conditions. The E2(TO) and A1(LO) phonon modes exhibit sensitivity to hydrostatic stress, whereas A1(LO) remains largely unaffected under shear, reflecting directional vibrational differences. Theoretical predictions and experimental measurements agree well within 16% error, highlighting the effectiveness of Raman-based stress detection for 4H-SiC. This integrated theoretical–experimental approach provides a robust framework for stress and strain analysis, facilitating the design and fabrication of next-generation 4H-SiC electronic devices.

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.