Parameter identification for model-based advanced process control of diffusion furnaces

The main practice of the current trend in Advanced Process Control (APC) concentrates on run-to-run controls. Even with the few exceptions on wafer-based applications, these practices mostly rely on statistical associations for changing the controlled variables in relation to some direct or indirect measurements of process parameters. This paper reports another approach to the current mainstream APC practice by adopting real-time control techniques. First step of this approach requires a thorough understanding of the underlying physics of the process dynamics. Utilizing fundamental equations governing temperature variations for diffusion furnaces, mathematical models are obtained in state-space formulation. Parameter estimation of these models is performed from online production data for individual tool-sets. Practical issues during this implementation process are discussed in detail. As an in-house development, it integrated understanding of the governing process dynamics and digital data processing techniques without enlisting support from equipment manufacturer or software vendors.