Exploring Dielectric Constant and Dissipation Factor of LTCC Using Machine Learning

Abstract

Low-temperature co-fired ceramics (LTCCs) have been attracting attention due to rapid advances in wireless telecommunications. Low-dielectric-constant (Dk) and low-dissipation-factor (Df) LTCCs enable a low propagation delay and high signal quality. However, the wide ranges of glass, ceramic filler compositions, and processing features in fabricating LTCC make property modulating difficult via experimental trial-and-error approaches. In this study, we explored Dk and Df values of LTCCs using a machine learning method with a Gaussian kernel ridge regression model. A principal component analysis and k-means methods were initially performed to visually analyze data clustering and to reduce the dimension complexity. Model assessments, by using a five-fold cross-validation, residual analysis, and randomized test, suggest that the proposed Dk and Df models had some predictive ability, that the model selection was appropriate, and that the fittings were not just numerical due to a rather small data set. A cross-plot analysis and property contour plot were performed for the purpose of exploring potential LTCCs for real applications with Dk and Df values less than 10 and 2 × 10−3, respectively, at an operating frequency of 1 GHz. The proposed machine learning models can potentially be utilized to accelerate the design of technology-related LTCC systems.

Acceleration of optimizing the dielectric properties of (Ca0.7Nd0.2)1−x(Li0.5Nd0.5)xTi1−y(Mg1/3Nb2/3)yO3 with the aid of machine learning

“…[13][14][15] Utilizing machine learning to establish the mapping relationship between material influencing factors (such as composition and processing) and target variables (such as performance, microstructure, and phase composition) can enable the prediction of material composition, structure, processing, and performance. [16][17][18][19] Machine learning can also be used to optimize the design of materials and accelerate the discovery of new materials with desirable properties. [20][21][22] For example, Yuan et al 23 used machine learning to accelerate the discovery of novel lead-free BaTiO 3 -based piezoelectrics with large electrical strain, obtaining a maximum electrical strain of 0.23%.…”

“…Recently, the field of materials science has shown increased interest in machine learning due to its impressive capability to extract hidden insights from data and make precise predictions without requiring explicit utilization of formulas or equations 13–15 . Utilizing machine learning to establish the mapping relationship between material influencing factors (such as composition and processing) and target variables (such as performance, microstructure, and phase composition) can enable the prediction of material composition, structure, processing, and performance 16–19 . Machine learning can also be used to optimize the design of materials and accelerate the discovery of new materials with desirable properties 20–22 .…”

A machine learning model for flank wear prediction in face milling of Inconel 718

“…Recently, deep learning (DL) and machine learning (ML) model are considered as powerful methods to decipher and explore the complex underlying physics of the materials science and engineering [18], including quality prediction in manufacturing [19], effective charge in electromigration effect [20], dielectric constant and dissipation factor in low temperature co-red ceramics [21], irradiation embrittlement in steel [22] etc. More relevantly, direct tool wear detection of physical vapor deposition (PVD)-coated carbide inserts by using a convolution neural network (CNN) model using image features is one of the approach to characterize failure occurrence [23].…”