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ICCAD-2013 CAD contest in mask optimization and benchmark suite

Published: 18 November 2013 Publication History

Abstract

Optical microlithography is the technique of printing a set of shapes on a wafer using light transmitted through a template called a mask. Repeatedly printing and stacking such shapes on top of each other to build electrical circuits allows us to manufacture chips in high volume. However this technique has now reached its fundamental physical limits of resolution. Current 193nm wavelength light is no longer sufficient to reliably transfer patterns which are now in the sub-100nm dimensional range. This has led to increased research in optimizing lithographic masks to pre-compensate for distortions introduced by the lithographic process. This is called mask optimization. In this contest, students are provided with a sample lithographic model which simulates the transfer of a mask pattern on to wafer. The mask is assumed to be a pixelated template, where every pixel can be turned on or off, to indicate where light passes through, or is blocked. Contestants are also provided with models to predict the robustness of their pattern i.e. how much variability is in the transferred pattern. Given these tools, the objective is to minimize the variability in the wafer image, as measured by process variability (PV) bands. This is subject to the constraints of runtime and satisfying pattern fidelity i.e. the transferred pattern should resemble the target pattern. Benchmarks are provided in the form of collections of geometric shapes, each of which provides a challenge in printing at sub-wavelength.

References

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Cited By

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  • (2024)CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement LearningProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656254(1-6)Online publication date: 23-Jun-2024
  • (2024)FuILT: Full Chip ILT System With Boundary HealingProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633315(13-20)Online publication date: 12-Mar-2024
  • (2023)Enabling Scalable AI Computational Lithography with Physics-Inspired ModelsProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568361(715-720)Online publication date: 16-Jan-2023
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Published In

cover image ACM Conferences
ICCAD '13: Proceedings of the International Conference on Computer-Aided Design
November 2013
871 pages
ISBN:9781479910694
  • General Chair:
  • Jörg Henkel

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IEEE Press

Publication History

Published: 18 November 2013

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  • Research-article

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ICCAD'13
Sponsor:
ICCAD'13: The International Conference on Computer-Aided Design
November 18 - 21, 2013
California, San Jose

Acceptance Rates

ICCAD '13 Paper Acceptance Rate 92 of 354 submissions, 26%;
Overall Acceptance Rate 457 of 1,762 submissions, 26%

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View all
  • (2024)CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement LearningProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656254(1-6)Online publication date: 23-Jun-2024
  • (2024)FuILT: Full Chip ILT System With Boundary HealingProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633315(13-20)Online publication date: 12-Mar-2024
  • (2023)Enabling Scalable AI Computational Lithography with Physics-Inspired ModelsProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568361(715-720)Online publication date: 16-Jan-2023
  • (2022)Generic lithography modeling with dual-band optics-inspired neural networksProceedings of the 59th ACM/IEEE Design Automation Conference10.1145/3489517.3530580(973-978)Online publication date: 10-Jul-2022
  • (2020)VLSI Mask Optimization: From Shallow To Deep LearningProceedings of the 25th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC47756.2020.9045241(434-439)Online publication date: 17-Jan-2020
  • (2019)Efficient Layout Hotspot Detection via Binarized Residual Neural NetworkProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317811(1-6)Online publication date: 2-Jun-2019
  • (2019)Deep learning-based framework for comprehensive mask optimizationProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3288749(311-316)Online publication date: 21-Jan-2019
  • (2019)SRAF insertion via supervised dictionary learningProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3287684(406-411)Online publication date: 21-Jan-2019
  • (2019)A fast machine learning-based mask printability predictor for OPC accelerationProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3287682(412-419)Online publication date: 21-Jan-2019
  • (2018)GAN-OPCProceedings of the 55th Annual Design Automation Conference10.1145/3195970.3196056(1-6)Online publication date: 24-Jun-2018
  • Show More Cited By

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