Machine learning-based resist 3D model

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Accurate prediction of resist proFile has become more important as technology node shrinks. Non-ideal resist proFiles due to low image contrast and small depth of focus affect etch resistance and post-etch result. Therefore, accurate prediction of resist proFile is important in lithographic hotspot veriFication. Standard approaches based on a single- or multiple-2D image simulation are not accurate, and rigorous resist simulation is too time consuming to apply to full-chip.We propose a new approach of resist proFile modeling through machine learning (ML) technique. A position of interest are characterized by some geometric and optical parameters extracted from surroundings near the position. The parameters are then submitted to an artiFicial neural network (ANN) that outputs predicted value of resist height. The new resist 3D model is implemented in commercial OPC tool and demonstrated using 10nm technology metal layer.
Publisher
SPIE
Issue Date
2017-02
Language
English
Citation

Optical Microlithography XXX 2017

DOI
10.1117/12.2257904
URI
http://hdl.handle.net/10203/269629
Appears in Collection
EE-Conference Papers(학술회의논문)
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