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Real time monitoring of reticle etch process tool to investigate and predict critical dimension performance

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.