Using X-ray beams and machine learning for detecting structural defects, such as pore formation, can help prevent failure of metal 3D-printed parts. Systematic computer-based material design uses ...

Add Yahoo as a preferred source to see more of our stories on Google. Scientists from the federally funded Argonne National Laboratory in Illinois and the University of Virginia have developed a new ...

Researchers have designed a robust image-based anomaly detection (AD) framework with illumination enhancement and noise suppression features that can enhance the detection of subtle defects in ...

Researchers from Northwestern University, University of Virginia, Carnegie Mellon University, and Argonne National Laboratory have made a significant advancement in defect detection and process ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

As device sizes continue to increase on devices at 2x nm design rule and beyond and high wafer stress is worsening due to multi-film stacking in the vertical memory process, we observe an increasing ...

In manufacturing metal parts, the quality of the finished products hinges on the precision of every step, especially surface cleaning. Before metal parts undergo treatment such as coating, painting, ...