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• W3197599733 abstract "• Present an efficient global sensitivity analysis method for nanoscale scatterometry. • Adapt a density-based, absolute, and iterative global sensitivity analysis approach. • Propose a computationally cost-effective neural network as a surrogate model. • A scatterometry configuration problem for typical grating nanostructures is studied. • Numerical case studies showcase the effectiveness of the proposed approach. Optical scatterometry has been widely used for measuring periodic thin-film structures in a fast and non-invasive way. However, shrinking structure dimensions, along with increasing structural complexity, give rise to challenging such nanoscale scatterometry. For in-line nano-fabrication process control, it is critical to enhance the measurability of small feature change. Accordingly, there is a strong need to evaluate the global sensitivity performance of different measurement strategies for identifying the best scheme that exhibits a remarkable change of optical responses under small dimension fluctuation. Such analysis requires not only an appropriate sensitivity indicator but also time-intensive computation. This paper presents an efficient approach to multi-parameter global sensitivity analysis (GSA) for scatterometry. A neural network (NN) assisted, moment-independent, and adaptive GSA method is proposed. A computationally cost-effective deep neural network is developed as a surrogate model for circumventing the simulation-based forward modeling. A scatterometry configuration problem for typical grating nanostructures is studied to demonstrate the effectiveness and the generalization potential of our approach. Our results show that the proposed approach is a powerful tool for not only examining sensitivity performance but also expediting the GSA optimization process for fast scatterometry." @default.
• W3197599733 created "2021-09-13" @default.
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• W3197599733 date "2021-12-01" @default.
• W3197599733 modified "2023-09-23" @default.
• W3197599733 title "Neural network assisted multi-parameter global sensitivity analysis for nanostructure scatterometry" @default.
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• W3197599733 doi "https://doi.org/10.1016/j.apsusc.2021.151219" @default.
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