Nature

Unsupervised defect clustering in atomic-resolution microscopy using a convolutional variational autoencoder

Atomic-scale defects govern many functional properties of materials, yet their systematic identification and quantification remain challenging because supervised learning approaches require extensive ...
Nature

Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion

Imaging techniques have considerably improved corrosion-induced metal loss defect detection and severity estimation in recent decades. Even though the detection of defects using imaging techniques in ...
pv magazine International

Ensemble deep learning for PV cell defect detection

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 ...
Semiconductor Engineering

AI Models Transform Defect Inspection And Review, But Can Fail To Scale

AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.
Semiconductor Engineering

Can Your ATPG Do This? Cut Defects Escaping Detection With ML

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 ...