All wafers moving through a microchip nanofabrication process pass through a lithographic apparatus for most, if not all, layers. With a lithographic apparatus providing a massive amount of data per wafer, this paper will outline how physicsbased models can be used to refine UVLS (ultraviolet level sensor) metrology into four unique inputs for use in a deep learning network. Due to the multi-dimensional cross correlation of our deep learning network, we then show that training to a sparse overlay layout with dense inputs results in a hyper dense overly signature. On a testing dataset blind to the training we show that the accuracy of the predictive computational overlay metrology can capture R2 up to 0.81 of the signature in overlay Y. As a real-world application, we outline how our predictive computational overlay metrology can then be used to designate which wafer combinations, coming from the TWINSCAN system, should have overlay measured with a YieldStar system for possible use with APC (advanced process control).
Chan Hwang, Seung Yoon Lee, SeungHwa Oh, Emil Schmitt-Weaver, Jeonghyun Park, Daniel Park, Mohamed El Kodadi, and Kaustuve Bhattacharyya, "Smart overlay metrology pairing adaptive deep learning with the physics-based models used by a lithographic apparatus," Proc. SPIE 10587, Optical Microlithography XXXI, 105870B (Presented at SPIE Advanced Lithography: February 27, 2018; Published: 20 March 2018); https://doi.org/10.1117/12.2297513.
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