
Rice doctoral alumna Tia Gray holding a sample of selectively grown diamond microstructure in the shape of an owl.
Photos Credit: Brandon Martin/Rice University
Scientific Frontline: Extended "At a Glance" Summary: Automated Defect Detection in Advanced Semiconductors
The Core Concept: Materials scientists have developed a custom, Python-based software workflow to rapidly analyze high-resolution X-ray diffraction data, successfully measuring microscopic defects in diamond and other wide-bandgap semiconductors.
Key Distinction/Mechanism: Rather than relying on time-consuming and labor-intensive manual analysis, this approach utilizes automated software to process X-ray diffraction patterns. It rapidly identifies structural irregularities and calculates the precise density of atomic lattice dislocations across diverse crystal structures.
Major Frameworks/Components:
- High-resolution X-ray diffraction (HRXRD) analysis.
- Custom Python-based automation and data processing software.
- Lattice dislocation density calculation modeling.
- Wide-bandgap semiconductor evaluation protocols (specifically focusing on synthetic single-crystal diamond and gallium nitride).







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