Pairing wafer leveling metrology from a lithographic apparatus with deep learning to enable cost effective dense wafer alignment metrology

For the past several years there has been a push in the industry to drive innovation by pairing different types of metrology to keep up with the challenging requirements of overlay, focus and CD in multi-patterning processes. Holistic metrology is an example of this where instead of using a single metrology method we pair various available metrology methods to enrich the overall information content. With advancements in deep learning algorithms we can better utilize existing infrastructure to extract information from metrology parings for a cost-effective solution that has traditionally gone unused. In computational alignment metrology we pair leveling data with alignment and wafer quality to generate a dense alignment vector map. In the first step wafer leveling metrology from the lithographic apparatus is deconvolved into individual contributors. Selecting the deconvolved signatures with greatest influence on alignment metrology we train our dense input metrology to our targeted alignment metrology using a deep feedforward network. With the trained weights and biases of the deep feedforward network and input from a new lot of wafers we can now compute a dense alignment vector map. With a 3rd order HOWA model fit to the original 32 marks and then again to the same 32 marks paired with leveling, the model fit to the dense estimation from the 32 marks paired with leveling out performs HOWA fit to the original 32 marks. Finally, by fitting an advanced alignment model which optimizes spatial frequency between our enhanced alignment and corresponding overlay metrology, we can realize additional performance improvements in wafer to wafer overlay.