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 ...
For the first time, researchers at the Lawrence Berkeley National Laboratory (Berkeley Lab) have built and trained machine learning algorithms to predict defect behavior in certain intermetallic ...
Semiconductor wafer defect pattern recognition and classification is a crucial area of research that underpins yield enhancement and quality assurance in microelectronics manufacturing. The discipline ...
Researchers have developed a new method for detecting defects in additively manufactured components. Researchers at the University of Illinois Urbana-Champaign have developed a new method for ...
Automated optical inspection (AOI) is a cornerstone in semiconductor manufacturing, assembly and testing facilities, and as such, it plays a crucial role in yield management and process control.
Machine learning models are highly influenced by the data they are trained on in terms of their performance, ...
SEMVision™ H20 enables better and faster analysis of nanoscale defects in leading-edge chips Second-generation “cold field emission” technology provides high-resolution imaging AI image recognition ...
Tokyo, Japan – Scientists from Tokyo Metropolitan University have used machine learning to automate the identification of defects in sister chromatid cohesion. They trained a convolutional neural ...
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