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Multi-objective genetic algorithms for reducing mark read-out effort in lithographic tests

Authors:

Pierluigi Frisco,

Timo Bootsma,

Thomas BäckAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 365 - 366

https://doi.org/10.1145/3319619.3321931

Published: 13 July 2019 Publication History

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Abstract

This paper describes an application of multi-objective genetic algorithms (MOGA) to an optimization problem in the context of lithographic testing. The overall aim is to find a general procedure for reducing mark read-out effort in lithographic tests with limited degradation of related performance indicators. In these tests, silicon wafers are exposed with marks which are then read-out to determine lithographic performance expressed, for instance, with the 99.7 percentile of the read-out marks.

The problem was solved by applying MOGA in two stages. The first stage aims at determining a reduced layout in which for each field the same marks are read-out. In the second stage, the aim is to determine a reduced layout in which different fields have different marks read-out. In both stages the conflicting objectives are two: the number of read-out marks and the chosen performance indicator.

The recombination and mutation operators applied in MOGA are different in the two stages and are derived from a statistical analysis of the input data.

This approach, when applied to overlay test data, leads to a 50% reduction in read-out marks with 10% degradation range in performance indicators when compared to the full layout.

References

[1]

C. Mack, 2012. Fundamental Principles of Optical Lithography: The Science of Microfabrication. Wiley, NY, 2012.

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[2]

K. Deb, 2001. Multi-objective optimization using evolutionary algorithms. Wiley, NY, 2001.

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Index Terms

Multi-objective genetic algorithms for reducing mark read-out effort in lithographic tests
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Lithographic sonication patterning of large area GaN nanopillar forests grown on a Si substrate

Optimization of lithographic masks using genetic algorithms

A lithographic polymer process sequence for chemical sensing arrays

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