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

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 ...

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 ...

The number of defects detected through inspection is exploding at each new process node. There are now millions of defects being identified on each wafer, but only a fraction of those can cause ...

Researchers from South Korean organisations Pohang University of Science and Technology (POSTECH), Korea Institute of Materials Science (KIMS), and the Hyundai Motor Group, and the Japanese University ...

A breakthrough contactless inspection system developed at the University of New South Wales (UNSW) Sydney could soon become the new global standard in solar cell testing – cutting waste, doubling ...

GenAI and ML workloads are causing a ramp up in silent data corruption. Multi-stage detection with on-chip, AI-based telemetry offers smarter fault prevention. As transistor geometries shrink and ...

Abstract: As electrical devices take on more life-critical roles, such as in autonomous driving, ensuring the quality of solder joints during production becomes increasingly important. Recently, there ...

What if manufacturing companies could pinpoint the exact cause of a defect the moment it occurs, preventing costly production delays and ensuring top-notch quality? Generative artificial intelligence ...