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.

Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This ...

BACKGROUND: Congenital heart disease (CHD), the most common birth defect and a leading cause of infant mortality, is ...

NVIDIA GTC Taipei — NVIDIA today announced that TSMC, the world’s leading semiconductor company, is using NVIDIA accelerated computing and AI to advance semiconductor design and manufacturing.

Nvidia and the world’s largest foundry TSMC are collaborating to speed up semiconductor design and manufacturing. Under the ...

This software is a research prototype, solely developed for and published as part of the publication MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot ...

Abstract: Automated detection of metallic surface defects has increasingly become essential for industrial quality control and safety. In this work, we evaluate the balance between deep learning model ...

Researchers report a machine learning approach to predict LPBF defects from up-skin and down-skin angles, suggesting there might be angle-aware process control for metal AM. Laser Powder Bed Fusion ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...

How might aerospace quality engineers progress from defect detection to making defects obsolete entirely? The key to doing so lies in the intersection of AI-based inspection technology, predictive ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine learning algorithm designed to identify physical anomalies in solar ...