With the development and production of integrated circuits at the 22nm node, optical lithography faces increasing challenges to keep up with the specifications on its performance along various metrics, such as pattern fidelity and process window. The past few years saw the emergence of source mask optimization (SMO) as an important technique in computational lithography, which allows lithographers to rise to the challenges by exploiting a larger design space on both mask and illumination configuration, and integrates with methods such as inverse imaging. Yet, many methods that are used to tackle SMO problem arising in the inverse imaging involve heavy computation and slow convergence, making the technique unappealing for full-chip simulations or large circuits. Therefore, the purpose of this research is to take advantage of computational imaging technologies to solve source and mask design problems, extending the lifetime of optical lithography. The computational burden results in part from identical optimization over the whole mask pattern, consequently, we propose a weighted SMO scheme which applies different degrees of correction in the corresponding regions so that the optimal solutions are reached with fewer iterations. Additionally, undesirably long time is also attributed to the algorithm adopted to solve SMO problem. A fast algorithm based on augmented Lagrangian methods is therefore developed, which use the quasi-Newton method to accelerate convergence, thereby shortening the overall execution time. However, as semiconductor lithography is pushed to even smaller dimensions, mask topography effects have to be taken into account for a more accurate solution of SMO. At this stage, intensive computation is spent mainly in rigorous 3D mask modeling and simulations. To address this issue, we devise an optimization framework incorporating pupil aberrations into SMO procedure, which is performed based on the thin mask model so as to ensure a faster speed. We apply the above approaches to various mask geometries with different critical dimensions. Compared to conventional SMO, simulation results show that the proposed methods lead to better pattern fidelity and larger process window, especially in rigorous calculations. This demonstrates that the source and mask design generated through our algorithms are more practical. More importantly, the improved performance is not at the cost of speed. Instead, our methods take the least time to achieve it. This allows the advantages of computational imaging technologies to be worth exploring for further applications in optical lithography. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

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In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently.

M.Sc. (Computer Science) / Please refer to full text to view abstract

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published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy