Physics Guided Exponential Model Design of High Ge Content SiGe Selective Epitaxy for Gate All Around Source/Drain Applications

High germanium content silicon germanium (SiGe) epitaxy is critical for strain engineering in advanced gate all around (GAA) transistors. This paper demonstrates a physics guided exponential function model that quantitatively links selective epitaxial growth (SEG) parameters to Ge incorporation kinetics in nanoscale trenches. By coupling surface diffusion limited transport, gradient strain, and competitive adsorption dynamics, the model predicts optimal conditions for bottom-up filling with maximal Ge content. For trenches with widths of approximately 60 nm, the optimized process achieved a maximum Ge content of 57.93% and demonstrated 100% selectivity against silicon nitride (SiN) and silicon dioxide (SiO). Cross sectional TEM and EDS analyses reveal a graded Ge profile that minimizes interfacial defects and strain energy. Our results show that the established process physics correlation will significantly facilitate the development of GAA devices with 5nm CMOS technology nodes and beyond.

💡 Research Summary

This paper presents a physics‑guided exponential‑function model that quantitatively links selective epitaxial growth (SEG) parameters to germanium (Ge) incorporation kinetics in nanoscale trenches, targeting high‑Ge‑content SiGe source/drain regions for advanced gate‑all‑around (GAA) transistors. The authors first identify the limitations of conventional empirical relationships, which typically relate Ge content only to the gas‑phase composition ratio (P_GeH4/P_SiH2Cl2). Because SEG involves additional complexities—surface diffusion, strain gradients, and competitive adsorption/etching—the paper replaces the simple linear model with an Arrhenius‑based exponential formulation (Equation 2): y = y₀ · exp