Machine Learning-Guided Optimization of Thin-Film Processing Parameters for Advanced Functional Ceramics
Keywords:
machine learning; thin-film processing; functional ceramics; process optimization; data infrastructure; governance; sustainabilityAbstract
The reproducible fabrication of advanced functional ceramic thin films depends on precise control of a multidimensional processing space that includes substrate conditioning, precursor chemistry, deposition energetics, thermal treatment, atmosphere, and post-deposition annealing. Traditional optimization routes remain costly and narrowly scoped because they treat individual process variables in isolation and rely heavily on domain heuristics. Machine learning-guided approaches introduce a systemic alternative by building predictive models that connect processing parameters to functional performance and by closing the loop between experimental and computational workflows. This paper examines the system-level architecture of machine learning-guided optimization for thin-film processing of functional ceramics, emphasizing data infrastructure, model selection, uncertainty quantification, experimental validation, governance, and deployment in production-like environments. The discussion addresses structural trade-offs between model complexity and interpretability, the role of active learning in reducing experimental burden, and the need for robust data representations capable of capturing heterogeneity across deposition technologies. Cross-domain comparisons with semiconductor manufacturing and energy materials illustrate how workflow design choices affect scalability and reliability. The paper further considers fairness, reproducibility, sustainability, and policy implications of data-driven materials platforms. It argues that durable progress requires not only algorithmic innovation but also harmonized metadata standards, open benchmarking protocols, and institutional arrangements that align incentives across academic laboratories, national facilities, and industrial pilot lines. Such an integrated view is essential for translating machine learning prototypes into trustworthy tools for ceramic thin-film manufacturing.
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