Authors: Jiayi Tang | Liang Cao | Guanghui Xu | Miao Dong | Zhiyuan Jiang | Yan Zeng | Aiming Xiong | Liang Wu | Ming Li
Source:  (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Predicting the dielectric properties of dielectric films in chip by machine learning methods With the continuous development of the semiconductor industry in terms of miniaturization and performance improvement, the drawbacks of traditional dielectric materials such as silicon dioxide (SiO₂) become increasingly evident. In high-frequency scenarios, the demand for low-dielectric constant (low-k) materials that can effectively reduce signal delay and power consumption becomes of vital importance. This study focuses on designing a machine learning framework to identify low-k materials that can replace silicon dioxide in the dielectric films of chips, aiming to meet the needs of innovation in semiconductor technology.Our research methodology integrates data extracted from the Materials Project database, which is a comprehensive repository of material properties. We systematically collect data on dielectric materials, focusing on parameters such as dielectric constant, density, volume, band gap, and elemental composition. The significance of these parameters for the dielectric constant is determined, which will be helpful for future research. Additionally, we use material data generated by density functional theory (DFT) and high-throughput screening to further enhance the database.We employ advanced regression methods, including Gradient Boosting, Random Forest, and Support Vector Regression, to analyze the relationship between material features and dielectric properties. To ensure the reliability of the model, we carry out a large amount of data preprocessing work, such as handling missing values, detecting outliers through z-scores, and standardizing features using the RobustScaler. Subsequently, we divide the data into training and testing sets to evaluate the performance of the model. We rigorously tune the hyperparameters using Grid Search Cross-Validation and Bayesian optimization to improve the accuracy of the algorithm.The results show that the materials we discovered exhibit promising low-k properties. Their dielectric constants are significantly lower than that of silicon dioxide, and they also possess excellent mechanical stability and heat resistance. Metrics such as the Mean Squared Error (MSE) and the Coefficient of Determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) verify these findings, highlighting the effectiveness of the machine learning model in predicting material performance.In conclusion, this study demonstrates the potential of machine learning in accelerating the discovery of new dielectric materials. This work sets a new example for using data-driven approaches to solve complex engineering problems. Future research will focus on refining the prediction model and exploring more materials that can contribute to the development of semiconductor technology. Dielectric ; Reliability (semiconductor) ; Hyperparameter ; Support vector machine ; Miniaturization ; Hyperparameter optimization ; Bayesian optimization ; Semiconductor High voltage insulation and dielectric phenomena ; Dielectric materials and actuators