One well-known example of such material defect that has been studied extensively over the past century, Upmanyu says, is a dislocation. It occurs when an entire atomic plane is missing from a ...

A study published in Molecules and led by researchers from the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) of the Chinese Academy of Sciences demonstrated how deep learning can ...

The rapid advancement of 2D materials (2DMs), such as graphene, transition metal dichalcogenides (TMDs), and hexagonal boron nitride (hBN), has revolutionized the field of nanotechnology and ...

Materials scientists at Rice University have developed a new workflow methodology for measuring microscopic defects in diamond and other advanced semiconductor materials. By making it easier to spot ...

A deep learning model identifies atomic-scale defects in MoS2 with 95% accuracy, offering a faster route to quality control and quantum material research. Defects in 2D materials play a decisive role ...

SEMVision™ H20 enables better and faster analysis of nanoscale defects in leading-edge chips Second-generation “cold field emission” technology provides high-resolution imaging AI image recognition ...

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 ...

Detecting macro-defects early in the wafer processing flow is vital for yield and process improvement, and it is driving innovations in both inspection techniques and wafer test map analysis. At the ...

The solutes attach to dislocation threads like a swarm of bees, making it harder for dislocations to move. By engineering these defects and behaviors of solutes in alloys, Upmanyu says, humans can ...