In today's semiconductor industry downscaling of the IC design puts a stringent requirement on pattern overlay control. Tighter overlay requirements lead to exceedingly higher rework rates, meaning additional costs to manufacturing. Better alignment control became a target of engineering efforts to decrease rework rate for high-end technologies. Overlay performance is influenced by known parameters such as "Shift, Scaling, Rotation, etc", and unknown parameters defined as "Process Induced Variation", which are difficult to control by means of a process automation system. In reality, this process-induced variation leads to a strong wafer to wafer, or lot to lot variation, which are not easy to detect in the mass-production environment which uses sampling overlay measurements for only several wafers in a lot. An engineering task of finding and correcting a root cause for Process Induced Variations of overlay performance will be greatly simplified if the unknown parameters could be tracked for each wafer. This paper introduces an alignment performance monitoring method based on analysis of automatically generated "AWE" files for ASML scanner systems. Because "AWE" files include alignment results for each aligned wafer, it is possible to use them for monitoring, controlling and correcting the causes of "process induced" overlay performance without requiring extra measurement time. Since "AWE" files include alignment information for different alignment marks, it is also possible to select and optimize the best alignment recipe for each alignment strategy. Several case studies provided in our paper will demonstrate how AWE file analysis can be used to assist engineer in interpreting pattern alignment data. Since implementing our alignment data monitoring method, we were able to achieve significant improvement of alignment and overlay performance without additional overlay measurement time. We also noticed that the rework rate coming from alignment went down and stabilized at quite satisfactory level.
Optical & process model are used in conjunction with Mentors Calibre OPC tool to predict the behavior of a lithography process. Resist models rely exclusively on empirical measurement data, while optical models are calibrated based on the users knowledge of tool settings, but also fitting unknown parameters to empirical measurements. The final OPC model is a combination of optical & process behaviors prediction which includes resist & other process influence to meet the ever increasing demand of advanced lithography technology nodes like 90nm & below on model accuracy. Reliance of optical model creation on empirical measurement data is undoubtedly raising suspicion of how well the derived diffraction model is able to provide an accurate description of how light energy is distributed inside the resist. Various work & effort had been conducted in the past to cover defocus phenomenal on final model outcome & methodology introduced on better prediction from defocus to achieve better simulation quality, investigation has been carried out to study in further detail of existing strategy of resist & optical decoupling methodology in this work.