Accurate prediction of EUV lithographic images and 3D mask effects using generative networks - Awad, Abdalaziz; Brendel, Philipp; Evanschitzky, Peter; Woldeamanual, Dereje S.; Rosskopf, Andreas; Erdmann, Andreas (Journal of Micro-Nanolithography MEMS and MOEMS, 2021)
Background: As extreme ultraviolet lithography (EUV) lithography has progressed toward feature dimensions smaller than the wavelength, electromagnetic field (EMF) solvers have become indispensable for EUV simulations. Although numerous approximations such as the Kirchhoff method and compact mask models exist, computationally heavy EMF simulations have been largely the sole viable method of accurately representing the process variations dictated by mask topography effects in EUV lithography. Aim: Accurately modeling EUV lithographic imaging using deep learning while taking into account 3D mask effects and EUV process variations, to surpass the computational bottleneck posed by EMF simulations. Approach: Train an efficient generative network model on 2D and 3D model aerial images of a variety of mask layouts in a manner that highlights the discrepancies and non-linearities caused by the mask topography. Results: The trained model is capable of predicting 3D mask model aerial images from a given 2D model aerial image for varied mask layout patterns. Moreover, the model accurately predicts the EUV process variations as dictated by the mask topography effects. Conclusions: The utilization of such deep learning frameworks to supplement or ultimately substitute rigorous EMF simulations unlocks possibilities of more efficient process optimizations and advancements in EUV lithography. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JMM.20.4.043201]
CRIS-Team WoS Importer:2022-01-21

Integrated Mask Process Modeling for Better Yield Predictions - Awad, Abdalaziz; Behroozi, C.; Erdmann, Andreas (Proceedings of SPIE - The International Society for Optical Engineering, 2024)
Conventional lithography simulation often treats mask manufacturing as an ideal process that introduces no distortions, which leads to inaccurate patterning and yield predictions since the mask making process introduces distortions. Approaches to model the distortions from the mask process and their corrections often take a black-box form,1,2 which limits the capabilities of end-to-end modeling, disallowing integrated lithography simulations and mask-aware process corrections. The aim of this work is to develop efficient and differentiable”white-box” models for the mask process that help yield prediction and improve design for manufacturability through enabling end-to-end optimization. Our approach involves pre-processing SEM images of the manufactured mask and converting them into a raster format to allow differentiable pixel loss formulations. We then fit a mask process from mask input design to manufactured SEM using accurate machine learning models. We illustrate that such mask process model can be integrated in end-to-end lithography simulations to improve the accuracy of wafer pattern predictions with minimal overhead and can be used for efficient sensitivity analysis of the mask. Moreover, the utilization of differentiable modeling for all process steps including the mask process is instrumental in enabling effective end-to-end lithography modeling and optimization.
CRIS-Team Scopus Importer:2025-02-04
Keywords: Computational lithography; deep learning; defect prediction; generative networks; mask modeling