首页|Findings from Technische Universitat Wien Broaden Understanding of Machine Learn ing (Machine learning force field for thermal oxidation of silicon)
Findings from Technische Universitat Wien Broaden Understanding of Machine Learn ing (Machine learning force field for thermal oxidation of silicon)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting from the Technische Universitat Wien by NewsRx journalists, research stated, "Looking back at seven decades of highly extensive application in the semiconductor industry, silicon a nd its native oxide SiO2 are still at the heart of several technological develop ments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and fo r the realization of novel device technologies." Financial supporters for this research include European Research Council. Our news reporters obtained a quote from the research from Technische Universita t Wien: "With this comes a need for better understanding of the atomic configura tion at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine lea rning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations."
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