Neural Network Modeling for Advanced Process Control Using Production Data

The fabrication of integrated circuits involves many unit processes, some linear and some non-linear, and each with multiple inputs and outputs. These complexities suggest that benefits could be derived from the development and implementation of advanced process control tools and strategies. Empirical process models are one of these tools. In this research, sequential neural network models are developed to characterize critical steps in a fabrication process. The data used was collected from an industrial process. The data comes from experiments related to the processes under investigation, but not systematically designed to generate data for modeling. The models performed well, with an average prediction error of 3.3%, demonstrating the flexibility of the sequential neural network modeling process. Additionally, the models are used in a sensitivity analysis to study the output response to the various inputs. The methodologies presented are currently being ported to a similar manufacturing process with a larger database. Future work includes using the models for process optimization and as part of a model-based supervisory control system.