AI-Assisted Microstructure Optimization of BaTiO₃ Ceramics for Ultrathin MLCC Applications

Nidra Shenoy; Alister Leary

Authors

Nidra Shenoy Department of Computer Science, George Mason University, Fairfax, VA, USA. Author
Alister Leary Department of Computer Science, University of Central Florida, Orlando, FL, USA. Author

Keywords:

AI-assisted materials design; BaTiO3 ceramics; ultrathin MLCC; microstructure optimization; reliability; sustainable electronics; materials informatics; manufacturing governance

Abstract

Multilayer ceramic capacitors have become foundational components in modern electronics, and their continued miniaturization depends on the precise control of barium titanate ceramic microstructures at thicknesses approaching the limits of conventional processing. This paper examines how artificial intelligence can assist the optimization of BaTiO3 microstructures for ultrathin multilayer ceramic capacitor applications, with attention to system-level architecture, process integration, reliability governance, sustainability, and policy consequences. The discussion treats AI-assisted microstructure optimization not merely as a computational materials problem but as a socio-technical infrastructure that connects synthesis, tape casting, sintering, electrode integration, metrology, factory data systems, and regulatory expectations. The paper argues that the most consequential gains arise when machine learning models are embedded within closed-loop manufacturing and characterization environments rather than used as isolated predictive tools. It reviews microstructural constraints such as grain size, core-shell architecture, porosity, oxygen vacancy distribution, and dielectric breakdown, and it explains how surrogate models, Bayesian optimization, generative design, and physics-informed learning can support trade-off navigation across permittivity, loss, reliability, and yield. The paper also analyzes data infrastructure requirements, uncertainty management, distributional shift, model governance, and the risk of overfitting to narrow process windows. It considers sustainability and fairness implications, including energy intensity, critical materials, supply chain concentration, and equitable access to advanced manufacturing capabilities. Finally, it proposes a staged deployment roadmap that couples pilot-line validation, digital twins, human oversight, and standards development. The conclusion is that AI-assisted microstructure optimization can accelerate ultrathin MLCC development, but only if it is governed as an integrated materials and manufacturing system rather than as a narrow algorithmic intervention.

References

1. Kishi, H., Mizuno, Y., & Chazono, H. (2003). Base-metal electrode-multilayer ceramic capacitors: Past, present and future perspectives. Japanese Journal of Applied Physics, 42(1), 1–15. https://doi.org/10.1143/JJAP.42.1

2. Pan, M. J., & Randall, C. A. (2010). A brief introduction to ceramic capacitors. IEEE Electrical Insulation Magazine, 26(3), 44–50. https://doi.org/10.1109/MEI.2010.5482787

3. Rodel, J., Jo, W., Seifert, K. T. P., Anton, E. M., Granzow, T., & Damjanovic, D. (2009). Perspective on the future of piezoelectric ceramics. Journal of the American Ceramic Society, 92(6), 1153–1177. https://doi.org/10.1111/j.1551-2916.2009.03061.x

4. Hennings, D., Klee, M., & Waser, R. (1991). Advanced dielectrics: Bulk ceramics and thin films. Advanced Materials, 3(7-8), 334–340. https://doi.org/10.1002/adma.19910030703

5. Randall, C. A., Ogihara, H., & Baker, A. (2006). The role of oxygen vacancies in the degradation of perovskite dielectrics. Journal of Electroceramics, 17, 477–482. https://doi.org/10.1007/s10832-006-9888-8

6. Buscaglia, M. T., Viviani, M., Buscaglia, V., Mitoseriu, L., Testino, A., Nanni, P., Righetti, M. C., & Siri, A. S. (2006). Nanostructured barium titanate ceramics for MLCC. Journal of the European Ceramic Society, 26(14), 3065–3069. https://doi.org/10.1016/j.jeurceramsoc.2006.02.001

7. Damjanovic, D. (1998). Ferroelectric, dielectric and piezoelectric properties of ferroelectric thin films and ceramics. Reports on Progress in Physics, 61(9), 1267–1324. https://doi.org/10.1088/0034-4885/61/9/002

8. Arlt, G., Hennings, D., & de With, G. (1985). Dielectric properties of fine-grained barium titanate ceramics. Journal of Applied Physics, 58(4), 1619–1625. https://doi.org/10.1063/1.336051

9. Luan, Saiwei, et al. "Fabrication of High Performance BaTiO3 Films via Aqueous Tape Casting for ultrathin MLCC applications." Ceramics International (2025).

10. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. npj Computational Materials, 3, 54. https://doi.org/10.1038/s41524-017-0056-5

11. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2

12. Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5, 83. https://doi.org/10.1038/s41524-019-0221-0

13. Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019). Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Computational Materials, 5, 21. https://doi.org/10.1038/s41524-019-0153-8

14. Xue, D., Balachandran, P. V., Hogden, J., Theiler, J., Xue, D., & Lookman, T. (2016). Accelerated search for materials with targeted properties by adaptive design. Nature Communications, 7, 11241. https://doi.org/10.1038/ncomms11241

15. Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361(6400), 360–365. https://doi.org/10.1126/science.aat2663

16. Teichert, G. H., Natarajan, A. R., Van der Ven, A., & Garikipati, K. (2019). Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions. Computer Methods in Applied Mechanics and Engineering, 353, 201–216. https://doi.org/10.1016/j.cma.2019.05.019

17. Moosavi, S. M., Jablonka, K. M., & Smit, B. (2020). The role of machine learning in the understanding and design of materials. Journal of the American Chemical Society, 142(48), 20273–20287. https://doi.org/10.1021/jacs.0c06466

18. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

19. European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. Publications Office of the European Union. https://op.europa.eu/en/publication-detail/-/publication/9a1c9b3e-5c1c-11ea-b735-01aa75ed71a1

20. National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1

AI-Assisted Microstructure Optimization of BaTiO₃ Ceramics for Ultrathin MLCC Applications

Authors

Keywords:

Abstract

References

Downloads

Published

Issue

Section

License