Exploring Wetting and Optical Properties of CuAg Alloys via Surface Texture Morphology Analysis

TL;DR

This study investigates how surface texture morphology influences wetting and optical properties of CuAg alloys, using machine learning to model and predict these properties based on surface features.

Contribution

It introduces a comprehensive analysis linking surface texture, composition, and properties of CuAg alloys, employing data mining and machine learning for property prediction.

Findings

Correlation between contact angle and surface fractal dimension.

Layer thickness affects surface topography entropy.

Random Forest model predicts contact angles with ~5° MAE.

Abstract

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 fundamentally 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 robust correlations between contact angle and surface fractal dimension across all layer types promoting Cassie-Baxter surface state formation. Our analysis revealed a significant 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 a 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 accurately predict water contact angles (Mean Absolute Error around 5 deg) using only texture and optical parameters. The model, along with accompanying Python code, is publicly available. Our findings establish a pathway towards targeted surface texture morphology engineering for tailored material performance.