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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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STM study of single phosphorus incorporation into silicon by heating PBr3 on Si(100)

Applied Surface Science. 2026. Vol. 736. P. 166813–166813.
Pavlova T., V.M. Shevlyuga (Шевлюга В. М.

The objective of miniaturizing doped areas in silicon, with the ultimate goal of achieving atomic-precision doping, requires a fundamental understanding of the dopant incorporation process at the atomic level. We present a combined scanning tunneling microscopy (STM) and density functional theory (DFT) investigation of single phosphorus atom incorporation into the Si(100) surface. Phosphorus was supplied via PBr molecules, which completely dissociate on Si(100) at room temperature. By performing in situ annealing within the STM, we directly tracked the same phosphorus atom before and after heating. Upon annealing, the P atom undergoes an exchange with a nearby Si atom, forming a stable P–Si–Br complex with a Br atom located atop the Si atom of the heterodimer. The activation barrier calculated using DFT is consistent with our observation of doping starting at temperatures as low as 175 °C. These results provide detailed atomic-scale insight into the phosphorus incorporation pathway and offer a foundation for improving methods of precise, single-atom doping in silicon.

Research target: Physics
Language: English
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Keywords: КремнийSTMСТМHalogensгалогеныФОСФОРТФП расчетыDensity functional theory (DFT)phosphorusPrecise dopingSilicon surface
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