Machine vision for defect detection and recognition has evolved from classical image‐processing workflows—such as thresholding, edge detection and template matching—to sophisticated deep learning ...

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

TDK SensEI’s edgeRX Vision system, powered by advanced AI, accurately detects defects in components as small as 1.0×0.5 mm in real time. Operating at speeds up to 2000 parts per minute, it reduces ...

Materials scientists at Rice University have developed a new workflow methodology for measuring microscopic defects in ...

Front and center at Automate 2026, machine vision solution suppliers showed how vision systems are foundational to industrial automation. Explore some of the products ...

Applied Materials has launched the SEMVision™ H20, a new defect review system designed to enhance the analysis of nanoscale defects in advanced semiconductor chips. This system utilizes cutting-edge ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

Defect detection requirements on the order of 10 defective parts per million (DPPM) are driving improvements in inspection tools’ resolution and throughput at foundries and OSATs. However, defects ...