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Application of Scatterometry-Based Machine Learning to Control Multiple Electron Beam Lithography

Authored by: Nivea Figueiro, Francisco Sanchez, Roy Koret, Michael Shifrin, Yoav Etzioni, Shay Wolfling, Matthew Sendelbach, Yoann Blancquaert, Thibault Labbaye, Guido Rademaker, Jonathan Pradelles, Lucie Mourier, Stephane Rey, Laurent Pain | ASMC 2018, May 1, 2018

Abstract 

The evaluation of scatterometry and machine learning for the monitoring of intended critical dimension (CD) variations within scatterometry targets is presented. Such variations mimic non-uniformities potentially caused by massively parallel e-beam Maskless Lithography (ML2). Although previous results [1] demonstrate that traditional model-based scatter-ometry can properly quantify these within-target variations, the current work shows that the application of scatterometry-based machine learning complements the model-based scatterometry results. While model-based scatterometry can provide information about structure profile, which can be used to detect parameter shifts even in the absence of a reference, machine learning provides superb correlation to a defined reference.
Keywords—machine learning, scatterometry, alternative lithography, e-beam lithography, multibeam, multiple e-beam, dose variation, TMU, TMU analysis