Toward a systematic methodology for modeling vapor deposition processes

Vapor deposition is a complex process that includes gas-phase, surface, and solid-phase phenomena. Because of the complexity of the chemical and physical processes occurring in the vapor deposition process, it is difficult to form a comprehensive understanding and to control such systems to obtain the desirable structures, properties, and performance. To overcome these difficulties, a systematic methodology is necessary. In this work, we propose a systems approach to distinguish essential processes from other non-essential chemical and physical processes. We illustrate the systems approach using preferred orientation as an example. We also report our recent efforts to extract existing knowledge efficiently from the huge amounts of literature, using natural language processing (NLP). Our tools can support the literature-survey process that has traditionally been time consuming. Causal knowledge extraction from the literature is demonstrated with NLP.