A technical paper titled “Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy” was published by researchers at ...

This study is led by Prof. Shuangyin Wang (College of Chemistry and Chemical Engineering, Hunan University) and Prof. Chen Chen (College of Chemistry and Chemical Engineering, Hunan University).

A recent review article published in Advanced Materials explored the potential of artificial intelligence (AI) and machine learning (ML) in transforming thermoelectric (TE) materials design. The ...

Hidden semiconductor defects often pass inspection but fail later in operation. Learn how latent defects form, evade detection, and drive long-term reliability failures.

An international research team led by NYU Tandon School of Engineering and KAIST (Korea Advanced Institute of Science and Technology) has pioneered a new technique to identify and characterize ...

Yield loss is increasingly driven by molecular variability in thin films, interfaces, and contamination rather than visible defects. Reliability issues often appear first as parametric drift or margin ...

An international research team has pioneered a new technique to identify and characterize atomic-scale defects in hexagonal boron nitride (hBN), a two-dimensional (2D) material often dubbed 'white ...

Atomic-scale defects in crystals can make excellent quantum memories that can be written and read out using lasers, and could form the basis of future quantum communications and computing systems.

Today's systematic and more subtle random defects are not only decreasing yields, but are also increasing the number of test escapes, or defective parts per million (DPPM) shipped out. One of the ...