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 ...
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AI-based model measures atomic defects in materials
In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during ...
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 ...
Scientists at The University of Manchester have uncovered how subtle changes in temperature during a promising metal 3D ...
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 ...
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 ...
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 ...
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 ...
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