Automated X-ray workflow speeds up defect mapping in diamond semiconductors
The method could accelerate development of more reliable electronic and quantum devices by making flaw spotting easier.
A research team at Rice University, USA, has developed a custom Python-based software tool to rapidly analyse data from high-resolution X-ray diffraction, which probes a material’s internal crystal structure.
The software extracts dislocation densities from the data, providing rapid feedback on crystal quality.
These lattice defects affect charge transport, thermal conductivity and device reliability, making their accurate measurement critical for power electronics and quantum technologies. Previously, interpreting data from diamond has proved particularly challenging because of its crystal structure and defect behaviour.
The team has tested the workflow on four grades of single-crystal diamond, distinguishing material quality by dislocation density. Electronic-grade material shows the lowest defect levels, while heteroepitaxial diamond displays the highest.
The approach was also demonstrated on gallium nitride, indicating that it could be applied to a broader range of wide-bandgap semiconductor materials.
