Longitudinal (top) and axial (middle) images of X-Ray CT data of parts with 6 internal defects: a spherical clog, a stellated shaped clog, a cone shaped void, a blob shaped void, an elliptical warp of ...

Machine learning (ML) has emerged as a powerful tool for studying the properties of condensed matter. To date, most research has focused on the bulk properties of solids, however, defects are ...

Ceramic materials are renowned for their hardness and high-temperature resistance, making them indispensable in fields such as aerospace, electronics, and biomedical devices. However, these properties ...

Assembly houses are fine-tuning their methodologies and processes for automotive ICs, optimizing everything from inspection and metrology to data management in order to prevent escapes and reduce the ...

This new technical paper titled “End-to-end deep learning framework for printed circuit board manufacturing defect classification” is from researchers at École de technologie supérieure (ÉTS) in ...

ATPG targets faults at IC-gate boundaries, but 50% of defects are located within cells. Learn how cell-aware ATPG and user-defined fault models help to ferret out these hard-to-squash bugs.