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.

A systematic review newly published in the Journal of Pipeline Science and Engineering maps machine learning (ML) advances for pipelines across the full lifecycle: reliability-based design, structural ...

A comparison of eight AI-powered requirements management platforms for 2026, from NLP-based quality analysis and automated test generation to live traceability scoring, covering Jama Connect, IBM ...

Abstract: This paper presents a novel approach to automating aircraft surface inspection by leveraging deep learning techniques for the classification of thirteen different types of defects across the ...

Abstract: Automated optical inspection (AOI) is widely used by manufacturers for the detection of defects in printed circuit boards (PCBs). Recent works have proposed to apply deep learning for defect ...

To showcase the launch of its physical AI automation platform at Automate 2026, Universal Robots demonstrated its UR7e robots ...

Main difference between Edge AI and traditional cloud-based AI is how ML models are processed and deployed in both models ...

Technology Business Lead, Semiconductor and Electronics Industries in the Philippines Inc. THE global semiconductor and electronics industry is undergoing a major transformation driven by ...

Deeply's 'Listen AI Industrial' has already proven to have 99.87% accuracy in global automaker (Company H) production lines in Korea and Mexico.Foundation model trained on 2.1 million hours of factory ...

A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

From UN Women highlighting AI’s growing gender and racial bias, to JustAI’s $17 million Series A raise, YourStory brings you today’s headlines with the latest developments across sectors As global ...