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 ...

US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...

In industry, the detection of anomalies such as scratches, dents, and discolorations is crucial to ensure product quality and safety. However, conventional methods rely on heavy computational ...

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 ...

Defect detection requirements on the order of 10 defective parts per million (DPPM) are driving improvements in inspection tools’ resolution and throughput at foundries and OSATs. However, defects ...