Using qualitative observations for process tuning and control [IC manufacture]

Many qualitative properties of the product and the process are of interest during semiconductor manufacturing. One of the typical examples is the sidewall surface roughness of an etched polysilicon line. These properties are important since they affect directly the quality and performance of the integrated circuit (IC) devices being built. Traditionally, however, they are treated informally and subjectively as tacit knowledge in the processing arena. In this paper, we present a systematic approach to modeling and controlling such qualitative properties. This approach is based on treating qualitative process variables as categorical data that can be better understood with the help of formal statistical analysis known as logistic regression. This analysis reveals important relationships between the input process settings and the qualitative process output responses in a way that is similar to linear regression analysis for conventional numerical variables. Similarly, categorical process variables can be used for process control, which is driven by a probabilistic model of the categorical variables. We show how categorical models can be used to tune a process and, later, to control it via statistical process control (SPC) charts, model-based quality control techniques, and adaptive run-by-run controllers