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 ...

As the semiconductor world excitingly explores the potential of new advanced package solutions for their intricate and novel designs, challenges arise from undetected defects caused by the complexity ...

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 ...

Hidden semiconductor defects often pass inspection but fail later in operation. Learn how latent defects form, evade ...

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.

Scientists have found a promising new way to manufacture one of industry’s toughest materials—tungsten carbide–cobalt—using ...