Genetic Algorithms to Improve Mask and Illumination Geometries in Lithographic Imaging Systems

Fühner, Tim; Erdmann, Andreas; Farkas, Richárd; Tollkuhn, Bernd; Kókai, Gabriella

doi:10.1007/978-3-540-24653-4_22

Cited by 13 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept has already been successfully applied to the optimization of crystal growth and lithography simulation processes (for discussion on these applications see [9,10]). …”

Section: Discussionmentioning

confidence: 99%

A novel framework for distributing computations DisPyTE – distributing Python tasks environment

2006

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel framework for the distribution of concurrent tasks. DisPyTE (Distributing Python Tasks Environment) is written in the portable, interpreted programming language Python. It makes use of an eventdriven, asynchronous network communication interface, which renders it especially well suited for application in heterogeneous network environments. After a short discussion on existing parallelization techniques, this paper illustrates the key principles of DisPyTE, including the main components and the call scheme. In a first example, it is demonstrated how DisPyTE can be used to distribute objective function evaluations in the scope of a heuristic optimization routine (genetic algorithm).

show abstract

“…The concept has already been successfully applied to the optimization of crystal growth and lithography simulation processes (for discussion on these applications see [9,10]). …”

Section: Discussionmentioning

confidence: 99%

A novel framework for distributing computations DisPyTE – distributing Python tasks environment

2006

Self Cite

View full text Add to dashboard Cite

show abstract

“…The corresponding LWR value for the simulated profile is computed from these computed CD values and the selected sampling along the line and versus height, respectively. Pythmea, 34 a python multi-objective evolutionary algorithm from Dr. LiTHO, is used for the calibration of the model to the experimental data. The model parameters are calibrated for CD and the LWR simultaneously using a multi-objective optimizer.…”

Section: Simulation Parameters and Process Conditionsmentioning

confidence: 99%

Stochastic simulation and calibration of organometallic photoresists for extreme ultraviolet lithography

Belete

Bisschop

Welling

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

Self Cite

View full text Add to dashboard Cite

Organometallic photoresists are being pursued as an alternative photoresist material to push the current extreme ultraviolet lithography (EUVL) to the next generation of high-NA EUVL. In order to improve the photoresist performance, an understanding of the photoresist's response to different process conditions is required. In this endeavor, a stochastic development model is implemented, integrated into full photoresist process steps, and applied for photoresist performance investigations. The model is applied to Inpria-YA photoresist, which works mainly by the process of aggregation. Previously published modeling approaches for metal-organic photoresists assume that the development characteristics of these materials depend only on the size of the created oxo-clusters. In contrast to that, we propose a modeling approach that provides a more detailed description of the interaction among the developer, ligands, and oxo-bonds. Further, the calibration procedures conducted to extract the model parameters to match experimental data are discussed. The model approximated the experimental data with CD RMSE and LWR RMSE of 0.60 and 0.40 nm, respectively. We also investigated the impact of photoresist parameters on the process metrics, line width roughness (LWR), critical dimension (CD), doseto-size (DtS), and exposure latitude (EL) with the calibrated model. The investigation shows that details of the interaction of photoresist and developer, especially, the so-called development critical value, have a significant impact on the LWR and DtS. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…Finally, the optimization scheme is applied to 2D features (contact holes), yielding first promising results. A detailed discussions on the procedure and on results can be found elsewhere [5].…”

Section: Application To Mask Optimizationmentioning

confidence: 99%

Genetic Algorithm for Optimization and Calibration in Process Simulation

Fühner

Erdmann

Ortiz

et al. 2004

Simulation of Semiconductor Processes and Devices 2004

View full text Add to dashboard Cite

This paper proposes the use of genetic algorithms for process optimization and calibration of model parameters. The main principles of these evolution inspired optimizers are briefly explained. Afterwards, their application to two process simulation tasks is presented: (1) a layout problem in lithography processes and (2) the extraction of physical parameters of a diffusion model. The obtained results are shortly discussed.

show abstract

Genetic Algorithms to Improve Mask and Illumination Geometries in Lithographic Imaging Systems

Cited by 13 publications

References 6 publications

A novel framework for distributing computations DisPyTE – distributing Python tasks environment

A novel framework for distributing computations DisPyTE – distributing Python tasks environment

Stochastic simulation and calibration of organometallic photoresists for extreme ultraviolet lithography

Genetic Algorithm for Optimization and Calibration in Process Simulation

Contact Info

Product

Resources

About