A computationally efficient feature scale model and experimental study for copper electrodeposition in the presence of additives

摘要

Electrodeposition has been an important process in the manufacture of copper interconnects. A number of different additives are used to ensure desirable feature filling properties, such as superfilling behavior and good microstructure, characterized as a bright film by process engineers. Trial and error experimentation is typically used to identify conditions leading to desirable filling properties. The number of parameters involved in the plating process along with the different additives make this a time consuming, expensive process. Clearly, it would be useful to have a predictive simulation tool that could guide the process optimization. Previous modeling efforts of electrodeposition have assumed simple and lumped surface chemistry of the effect of additives, which while useful for understanding the process and correlating data cannot be used predictively. As a first step towards simulations with chemistry, we present an efficient feature scale simulation tool that allows representation of the underlying fundamental surface chemistry of additives.