Chipmakers are using more and different traditional tool types than ever to find killer defects in advanced chips, but they are also turning to complementary solutions like advanced forms of machine ...

Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and ...

Researchers have developed a new method for detecting defects in additively manufactured components. Researchers at the University of Illinois Urbana-Champaign have developed a new method for ...

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 ...

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

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...

50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow with manufacturing leading all industries due to its heavy reliance on data according to ...

AI and Machine Learning can predict transformer faults by analyzing dissolved gas data, thermal patterns, and vibration trends to identify insulation degradation, detect anomalies, and prevent costly ...

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 ...