Exploring wetting and optical properties of CuAg alloys via surface texture morphology analysis

Copper–silver (CuAg) alloys are increasingly explored for applications in high-performance electrical and electronic systems, owing to their unique combination of high electrical and thermal conductivity and enhanced mechanical strength. Nevertheless, a thorough understanding of how these alloys surface characteristics influence properties remains largely underdeveloped. Here, we explored the complex interplay between surface texture morphology, layer composition, wetting, and optical properties of Cu, Ag, and CuAg thin films deposited on textured silicon substrates via magnetron sputtering. Employing data mining and machine learning techniques, we identified correlations between contact angle and surface fractal dimension across all layer types promoting Cassie-Baxter surface state formation. Our analysis revealed a connection between layer thickness and surface topography entropy deficit, suggesting a dynamic evolution of surface order/disorder during metal film growth. Furthermore, we observed that contact angle sensitivity to layer thickness implied correlation with microstructure evolution. Through K-Means clustering, we successfully categorized the formed surface textures morphology. Finally, a Random Forest regression model was developed to predict water contact angles (Mean Absolute Error around 5 deg) using only texture and optical parameters. The model, along with Python code, is publicly available. Our findings establish pathway towards targeted surface texture morphology engineering for tailored material performance.