Computational Lithography

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.

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.