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 ...

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 ...

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 ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

Researchers have designed a robust image-based anomaly detection (AD) framework with illumination enhancement and noise suppression features that can enhance the detection of subtle defects in ...