Understanding Inherent Structural Defects and Chemical Distribution at Topological Superconductor-Semiconductor Interfaces and Heterostructures using Advanced Electron Microscopy

In the zoo of quantum systems that might disrupt the storage and manipulation of information, topological superconductors have received significant interest for their potential application in quantum computing, mainly due to their capacity to harbor non-Abelian states and provide fault-tolerant computation. One of the platforms proposed to host topological superconductivity is the interface between a superconducting metal and a semiconductor (SS)¹ with strong spin-orbit scattering[1] – where topological qubits are manifested. However, epitaxially interfacing two dissimilar materials, such a superconductor metal and a semiconductor heterostructure, inherent challenges emerge in the form of grain boundaries (black arrow in Fig. 1), grain misorientations (grains G1/G2 in Fig. 1), atomic distortions across the SS interface (white circle in Fig. 1), which might be detrimental to the device performance. Additionally, precise control of the semiconductor heterostructure interfaces and their chemical distribution across the interface is also needed to improve electron mobility and device performance. Thus, identifying and understanding these inherent structural defects at the multiple interfaces of this hybrid devices, and connecting our findings with growth process, might contribute to the performance improvement of topological qubits. Here we explore a combination of electron and ion microscopy together with advance image processing and mathematical modeling using Python libraries to understand structural properties at the SS interface, and quantify roughness and chemical distribution across the heterostructure interfaces. Our findings deliver metrology parameters to assess the quality of semiconductor heterostructure and highlight the relationship between such parameters and growth conditions.