Machine learning-based 3D resist model

Seongbo Shim; Suhyeong Choi; Youngsoo Shin

doi:10.1117/12.2257904

30 March 2017 Machine learning-based 3D resist model

Seongbo Shim, Suhyeong Choi, Youngsoo Shin

Proceedings Volume 10147, Optical Microlithography XXX; 101471D (2017) https://doi.org/10.1117/12.2257904
Event: SPIE Advanced Lithography, 2017, San Jose, California, United States

Abstract

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.

Citation Download Citation

Seongbo Shim, Suhyeong Choi, and Youngsoo Shin "Machine learning-based 3D resist model", Proc. SPIE 10147, Optical Microlithography XXX, 101471D (30 March 2017); https://doi.org/10.1117/12.2257904

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