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 ...

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 ...

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).

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 ...

Detecting sub-5nm defects creates huge challenges for chipmakers, challenges that have a direct impact on yield, reliability, and profitability. In addition to being smaller and harder to detect, ...

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 ...

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 ...