In fact, early forms of machine learning have been used in metrology and inspection in fabs since the 1990s to pinpoint defects in chips and even predict problems using pattern-matching techniques.

Then, the software classified the defects into pre-defined categories, which were learned from training samples, according to IBM. The system, however, wasn’t fast enough, and it did not have enough ...

The contemporary fast-moving high-tech environment brings a strong urgency to efficient management of defects within software in relation to maintaining software quality and integrity. With every ...

In this article, let’s explore how machine learning is revolutionizing software testing and breaking new ground for QA teams and enterprises alike, as well as how to successfully implement it. 1.

Researchers use machine learning to detect defects in additive manufacturing. ScienceDaily. Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2024 / 06 / 240604132239.htm.

Machine learning, which has disrupted and improved so many industries, is just starting to make its way into software testing. Heads are turning, and for good reason: the industry is never going ...

Machine learning (ML) has emerged as a powerful tool for studying the properties of condensed matter. To date, most research has focused on the bulk properties of solids, however, defects are ...

Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography Article Publication Date 24-May-2024 ...