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 ...

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 ...

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 ...

Variation is becoming a bigger problem in multi-die assemblies with TSVs and hybrid bonding. Multi-modal approaches are required to test these devices. AI plays a role in improving defect capture rate ...

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 ...

Korean researchers have found that the defects limiting silicon heterojunction solar cells (SHJ), the ...