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 ...
System reliability and safety are paramount across industries such as semiconductors, energy, automotive, and steel, where even microscopic cracks or defects within structures can critically affect ...
Production delays and quality errors are a universal challenge in manufacturing. In Aerospace and defense (A&D), however, the stakes are especially high. An equipment failure or out-of-tolerance ...
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 ...
Lattice Semiconductor announced the latest release of the Lattice sensAI solution stack, delivering expanded model support, ...
Researchers built an AI system that adapts to process changes, maintaining defect detection accuracy and lowering retraining costs in smart factories. (Nanowerk News) Artificial intelligence is ...
Recently, defect detection systems using artificial intelligence (AI) sensor data have been installed in smart factory manufacturing sites. However, when the manufacturing process changes due to ...
LineWise, a US-based startup developing an AI-powered “virtual engineer” for manufacturers, said it has raised $1.1 million in pre-seed funding from A2D Ventures, Y Combinator and a group of global ...
Standard electrogalvanized coating thicknesses tend to range from 5 µm to 8 µm. Homogeneity is key to ensuring premium adhesive behavior, in terms of the upper layers’ adhesion to the coating and the ...
High-bandwidth memory stands at the forefront of multiple technology developments as a critical enabler of AI, but it is one of the most difficult modules to manufacture. Leading HBM device makers and ...
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