The OSHIMA EagleAi® is an AI fabric inspection machine that uses visual recognition to detect, map, and classify more than 20 categories of fabric defects in woven, knit, and elastic textiles, and ...

The Department of Science and Technology (DOST) has unveiled new equipment capable of analyzing materials and detecting ...

A new wafer inspection platform combines AI analytics, sub-micron imaging, SWIR sensing, and precision metrology to help ...

Modern advanced packaging processes and shrinking semiconductor device sizes mean that it is vital to consistently eliminate sub-20 nm defects and surface contaminants. To do this effectively, the ...

Researchers have evaluated how Vision Transformers and convolutional neural networks can support faster and more accurate ...

BMW researchers have demonstrated that camera-based inspection systems can catch manufacturing flaws in battery electrodes ...

The system, developed by Panevo, a Canadian clear technology and manufacturing analytics company, reportedly achieved approximately 97% detection reliability with minimal false positives of Muskoka’s ...

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 ...

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 ...

Within the context of semiconductor inspection and failure analysis, latent defects present a significant challenge because they make it difficult to determine whether a fault originated during ...