Computational Lithography
Published on 10. November 2010
Software
Other digital
256 pages
978-0-470-61894-3 (ISBN)
Description
This is the first book to address the optimization of resolution enhancement techniques in optical lithography. It provides an in-depth discussion of RET tools that use model-based mathematical optimization approaches. The book starts with an introduction of optical lithography systems, electric magnetic field principles, and fundamentals of optimization; it goes on to describe algorithms for the development of optimal optical proximity correction, phaseshifting mask, offaxis illumination approaches, and their combinations. The accompanying mathematical derivations and MATLAB(r) software files make it easy for researchers, scientists, engineers, and graduate students and faculty to apply any of the optimization algorithms.
Reviews / Votes
"Computational lithography draws from the rich theory of inverse problems, optics, optimization, and computational imaging; as such, the book is also directed to researchers and practitioners in these fields. " (Consumer Electronics Net, 15 March 2011)More details
Language
English
Place of publication
Hoboken
United Kingdom
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Dimensions
Height: 250 mm
Width: 150 mm
Thickness: 15 mm
Weight
666 gr
ISBN-13
978-0-470-61894-3 (9780470618943)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Persons
Dr. Xu Ma received a PhD in electrical and computer engineering from the University of Delaware. He is now with the Electrical Engineering and Computer Science Department at the University of California at Berkeley. Dr. Ma's research interests include computational imaging, signal processing, and computational lithography. Dr. Gonzalo R. Arce received a PhD degree in electrical engineering from Purdue University. He is the Charles Black Evans Distinguished Professor of Electrical and Computer Engineering at the University of Delaware and holds the Fulbright-Nokia Distinguished Chair in Information and Communications Technologies. Dr. Arce's fields of interest include nonlinear and statistical signal processing, digital printing, and computational imaging. He is a Fellow of the IEEE for his contributions to the theory and applications of nonlinear signal processing.
Content
Preface. Acknowledgments. Acronyms. 1 Introduction. 1.1 Optical Lithography. 1.2 Rayleigh's Resolution. 1.3 Resist Processes and Characteristics. 1.4 Techniques in Computational Lithography. 1.5 Outline. 2 Optical Lithography Systems. 2.1 Partially Coherent Imaging Systems. 2.2 Approximation Models. 2.3 Summary. 3 Rule-Based Resolution Enhancement Techniques. 3.1 RET Types. 3.2 Rule-Based OPC. 3.3 Rule-Based PSM. 3.4 Rule-Based OAI. 3.5 Summary. 4 Fundamentals of Optimization. 4.1 Definition and Classification. 4.2 Unconstrained Optimization. 4.3 Summary. 5 Computational Lithography with Coherent Illumination. 5.1 Problem Formulation. 5.2 OPC Optimization. 5.3 Two-Phase PSM Optimization. 5.4 Generalized PSM Optimization. 5.5 Resist Modeling Effects. 5.6 Summary. 6 Regularization Framework. 6.1 Discretization Penalty. 6.2 Complexity Penalty. 6.3 Summary. 7 Computational Lithography with Partially Coherent Illumination. 7.1 OPC Optimization. 7.2 PSM Optimization. 7.3 Summary. 8 Other RET Optimization Techniques. 8.1 Double-Patterning Method. 8.2 Post-Processing Based on 2D DCT. 8.3 Photoresist Tone Reversing Method. 8.4 Summary. 9 Source and Mask Optimization. 9.1 Lithography Preliminaries. 9.2 Topological Constraint. 9.3 Source-Mask Optimization Algorithm. 9.4 Simulations. 9.5 Summary. 10 Coherent Thick-Mask Optimization. 10.1 Kirchhoff Boundary Conditions. 10.2 Boundary Layer Model. 10.3 Lithography Preliminaries. 10.4 OPC Optimization. 10.5 PSM Optimization. 10.6 Summary. 11 Conclusions and New Directions of Computational Lithography. 11.1 Conclusion. 11.2 New Directions of Computational Lithography. Appendix A: Formula Derivation in Chapter 5. Appendix B: Manhattan Geometry. Appendix C: Formula Derivation in Chapter 6. Appendix D: Formula Derivation in Chapter 7. Appendix E: Formula Derivation in Chapter 8. Appendix F: Formula Derivation in Chapter 9. Appendix G: Formula Derivation in Chapter 10. Appendix H: Software Guide. References. Index.