As mask pattern feature sizes shrink the need for tighter control of factors affecting critical dimensions (CD) increases
at all steps in the mask manufacturing process. To support this requirement Intel Mask Operation is expanding its
process and equipment monitoring capability. We intend to better understand the factors affecting the process and
enhance our ability to predict reticle health and critical dimension performance.
This paper describes a methodology by which one can predict the contribution of the dry etch process equipment to
overall CD performance. We describe the architecture used to collect critical process related information from various
sources both internal and external to the process equipment and environment. In addition we discuss the method used to
assess the significance of each parameter and to construct the statistical model used to generate the predictions. We
further discuss the methodology used to turn this model into a functioning real time prediction of critical dimension
performance. Further, these predictions will be used to modify the manufacturing decision support system to provide
early detection for process excursion.