Author

Lin-Kang Lu

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Conference Aug 2026

Predicting Structure-Dependent Dielectric Responses in High-k Oxides using Physics-Informed Graph Neural Networks

As semiconductor scaling extends into the deep nanometer regime, traditional silicon dioxide gate dielectrics encounter fundamental physical limitations imposed by due to severe quantum tunneling leakage currents. High-k gate dielectric materials have therefore become essential for modern nanoelectronics, enabling cont...

Liang Cao, Lin-Kang Lu, Jia-Yi Tang et al. · International Conference on... · 0 citations

#graph neural networks Sep 2026

A physics-informed graph-ensemble framework for predicting structure-dependent dielectric properties.

Predicting the structure-dependent dielectric responses of high-k oxides remains a fundamental bottleneck in the development of next-generation nanoelectronics, primarily due to the complex nature of ionic polarization. In this work, we propose a physics-informed hybrid framework designed for the performance prediction...

Lin-Kang Lu, Liang Cao, Guang-Hui Xu et al. · Physical Chemistry, Chemical... · 0 citations