What if manufacturing companies could pinpoint the exact cause of a defect the moment it occurs, preventing costly production delays and ensuring top-notch quality? Generative artificial intelligence ...

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

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 ...

Detecting sub-5nm defects creates huge challenges for chipmakers, challenges that have a direct impact on yield, reliability, and profitability. In addition to being smaller and harder to detect, ...

Chipmakers worldwide consider Automatic Test Pattern Generation (ATPG) their go-to method for achieving high test coverage in production. ATPG generates test patterns designed to detect faults in the ...