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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. · 0 citations
Conference Aug 2026

Multi-Objective Prediction and Discovery of High-Performance Polymer Dielectrics for Advanced Packaging

As integrated circuits advance toward heterogeneous integration, higher operating frequency, and greater power density, polymer dielectrics used in advanced packaging must simultaneously provide efficient heat dissipation and low relative dielectric constant. However, the joint optimization of thermal conductivity and...

Liang Cao, Rui-Yang Wu, Meng-Han Li et al. · 0 citations
Conference Aug 2026

Discovery of High-Performance Low-k Interconnect Materials via Multi-Target Machine Learning Model

With the continuous development of the semiconductor industry in terms of miniaturization and performance improvement, the demand for high-frequency and energy-efficient semiconductor devices, traditional dielectric materials, represented by dense amorphous silicon dioxide (SiO2) grown via thermal oxidation or CVD, whi...

Liang Cao, Meng-Han Li, Dan Xu et al. · 0 citations

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. · 0 citations

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