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 ...

NVIDIA (NASDAQ:NVDA) revealed that Taiwan Semiconductor Manufacturing Co. (NYSE:TSM) is deploying a range of its artificial ...

TSMC has expanded its three-decade partnership with NVIDIA by integrating accelerated computing, CUDA-X libraries, and machine learning models directly into its semiconductor fabrication facilities to ...

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: Defects occurring in manufacturing processes can lead to customer dissatisfaction, reduced product quality, and increased operational costs. Accurately predicting product defects is critical ...

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 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 ...