Revealing interface roughness and chemical composition distribution in III-V semiconductor quantum wells at the atomic scale

Ternary III-V semiconductor quantum wells (QW) have shown to be interesting platforms to explore topological superconductivity. Their performance is greatly affected by the interface morphology, which in turn can be affected by growth parameters. Optimization of growth conditions and physical properties might be possible by using characterization techniques delivering structure and compositional information with atomic precision. Here, we applied a combination of high-resolution High-Angle Annular Dark Field imaging in the Aberration-Corrected Scanning Transmission Electron Microscope, together with advanced image processing and mathematical modeling using Python libraries to understand and quantify roughness and chemical composition distribution across these heterostructures interfaces. Our findings deliver metrology parameters to assess the quality of semiconductor heterostructure and highlight the relationship between such parameters and growth conditions.