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 ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...

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

Whether the discussion is about smart manufacturing or digital transformation, one of the biggest conversations in the semiconductor industry today centers on the tremendous amount of data fabs ...

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 ...