Predicting the surface roughness of an electrodeposited copper film using a machine learning technique

Electrodeposition-based metal coating techniques are used to manufacture various industrial products and rely on the quantitative control of the physical properties of the coating layers, such as electrical conductivity, surface roughness, and hardness. To clarify the experimental conditions required to realize the desired physical properties of metal coating layers and shed light on the complex mechanism of the involved reactions, we prepared a custom-built experimental dataset (60 conditions) on the surface roughness of electrodeposited thin copper films and submitted it to an open-access data repository. Data-driven analysis revealed that surface roughness is strongly affected by the deposition temperature, current, and interelectrode distance. To understand the relationship between the experimental conditions and surface roughness of electrodeposited copper thin films, the custom-built dataset for electrodeposition was prepared and data-driven approach was applied.