The Experts below are selected from a list of 969 Experts worldwide ranked by ideXlab platform
Zihao Chen - One of the best experts on this subject based on the ideXlab platform.
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SUALD: Spacing uniformity-aware layout decomposition in triple patterning lithography
International Symposium on Quality Electronic Design (ISQED), 2013Co-Authors: Zihao ChenAbstract:In triple patterning lithography (TPL), balanced Feature Density on each layout mask helps facilitate the following OPC process. This paper presents the first spacing uniformity-aware layout decomposition method, called SUALD, which formulates the Density optimization problem in TPL based on the spacings between locally adjacent Features on each colored layout mask, and hence enhances the patterning quality. Based on the new Density formulation, a spacing uniformity graph is built using the Voronoi diagram. An effective heuristic triple partitioning algorithm is also proposed for TPL layout decomposition. Experimental results are very promising and show that SUALD obtains 69% and 40% improvements in average in the presented Density metrics over an integer linear programming method without Density control.
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ISQED - SUALD: Spacing uniformity-aware layout decomposition in triple patterning lithography
International Symposium on Quality Electronic Design (ISQED), 2013Co-Authors: Zihao ChenAbstract:In triple patterning lithography (TPL), balanced Feature Density on each layout mask helps facilitate the following OPC process. This paper presents the first spacing uniformity-aware layout decomposition method, called SUALD, which formulates the Density optimization problem in TPL based on the spacings between locally adjacent Features on each colored layout mask, and hence enhances the patterning quality. Based on the new Density formulation, a spacing uniformity graph is built using the Voronoi diagram. An effective heuristic triple partitioning algorithm is also proposed for TPL layout decomposition. Experimental results are very promising and show that SUALD obtains 69% and 40% improvements in average in the presented Density metrics over an integer linear programming method without Density control.
Pierre Boulanger - One of the best experts on this subject based on the ideXlab platform.
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Using Scale-space and Visual Perception Analysis
2020Co-Authors: Irene Cheng, Pierre BoulangerAbstract:Efficient online visualization of 3D mesh, mapped with photo realistic texture, is essential for a variety of applications such as museum exhibits and medical images. In these applications synthetic texture or color per vertex loses authenticity and resolution. An image-based view dependent approach requires too much overhead to generate a 360° display for online applications. We propose using a mesh simplification algorithm based on scale-space analysis of the Feature point distribution, combined with an associated visual perception analysis of the surface texture, to address the needs of adaptive online transmission of high quality 3D objects. The premise of the proposed textured mesh (TexMesh) simplification, taking into account of the human visual system, is the following: given limited bandwidth, texture quality in low Feature Density surfaces can be reduced, without significantly affecting human perception. The advantage of allocating higher bandwidth, and thus higher quality, to dense Feature Density surfaces, is to improve the overall visual fidelity. Statistics on Feature point distribution and their associated texture fragments are gathered during preprocessing. Online transmission is based on these statistics, which can be retrieved in constant time. Using an initial estimated bandwidth, a scaled mesh is first transmitted. Starting from a default texture quality, we apply an efficient Harmonic Time Compensation Algorithm based on the current bandwidth and a time limit, to adaptively adjust the texture quality of the next fragment to be transmitted. Properties of the algorithm are proved. Experimental results show the usefulness of our approach.
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Adaptive online transmission of 3-D TexMesh using scale-space and visual perception analysis
IEEE Transactions on Multimedia, 2006Co-Authors: Irene Cheng, Pierre BoulangerAbstract:Efficient online visualization of three-dimensional (3-D) mesh, mapped with photo realistic texture, is essential for a variety of applications such as museum exhibits and medical images. In these applications synthetic texture or color per vertex loses authenticity and resolution. An image-based view dependent approach requires too much overhead to generate a 360/spl deg/ display for online applications. We propose using a mesh simplification algorithm based on scale-space analysis of the Feature point distribution, combined with an associated visual perception analysis of the surface texture, to address the needs of adaptive online transmission of high quality 3-D objects. The premise of the proposed textured mesh (TexMesh)simplification, taking the human visual system into consideration, is the following: given limited bandwidth, texture quality in low Feature Density surfaces can be reduced, without significantly affecting human perception. The advantage of allocating higher bandwidth, and thus higher quality, to dense Feature Density surfaces, is to improve the overall visual fidelity. Statistics on Feature point distribution and their associated texture fragments are gathered during preprocessing. Online transmission is based on these statistics,which can be retrieved in constant time. Using an initial estimated bandwidth,a scaled mesh is first transmitted. Starting from a default texture quality,we apply an efficient Harmonic Time Compensation Algorithm based on the current bandwidth and a time limit, to adaptively adjust the texture quality of the next fragment to be transmitted. Properties of the algorithm are proved. Experimental results show the usefulness of our approach.
Irene Cheng - One of the best experts on this subject based on the ideXlab platform.
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Using Scale-space and Visual Perception Analysis
2020Co-Authors: Irene Cheng, Pierre BoulangerAbstract:Efficient online visualization of 3D mesh, mapped with photo realistic texture, is essential for a variety of applications such as museum exhibits and medical images. In these applications synthetic texture or color per vertex loses authenticity and resolution. An image-based view dependent approach requires too much overhead to generate a 360° display for online applications. We propose using a mesh simplification algorithm based on scale-space analysis of the Feature point distribution, combined with an associated visual perception analysis of the surface texture, to address the needs of adaptive online transmission of high quality 3D objects. The premise of the proposed textured mesh (TexMesh) simplification, taking into account of the human visual system, is the following: given limited bandwidth, texture quality in low Feature Density surfaces can be reduced, without significantly affecting human perception. The advantage of allocating higher bandwidth, and thus higher quality, to dense Feature Density surfaces, is to improve the overall visual fidelity. Statistics on Feature point distribution and their associated texture fragments are gathered during preprocessing. Online transmission is based on these statistics, which can be retrieved in constant time. Using an initial estimated bandwidth, a scaled mesh is first transmitted. Starting from a default texture quality, we apply an efficient Harmonic Time Compensation Algorithm based on the current bandwidth and a time limit, to adaptively adjust the texture quality of the next fragment to be transmitted. Properties of the algorithm are proved. Experimental results show the usefulness of our approach.
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Adaptive online transmission of 3-D TexMesh using scale-space and visual perception analysis
IEEE Transactions on Multimedia, 2006Co-Authors: Irene Cheng, Pierre BoulangerAbstract:Efficient online visualization of three-dimensional (3-D) mesh, mapped with photo realistic texture, is essential for a variety of applications such as museum exhibits and medical images. In these applications synthetic texture or color per vertex loses authenticity and resolution. An image-based view dependent approach requires too much overhead to generate a 360/spl deg/ display for online applications. We propose using a mesh simplification algorithm based on scale-space analysis of the Feature point distribution, combined with an associated visual perception analysis of the surface texture, to address the needs of adaptive online transmission of high quality 3-D objects. The premise of the proposed textured mesh (TexMesh)simplification, taking the human visual system into consideration, is the following: given limited bandwidth, texture quality in low Feature Density surfaces can be reduced, without significantly affecting human perception. The advantage of allocating higher bandwidth, and thus higher quality, to dense Feature Density surfaces, is to improve the overall visual fidelity. Statistics on Feature point distribution and their associated texture fragments are gathered during preprocessing. Online transmission is based on these statistics,which can be retrieved in constant time. Using an initial estimated bandwidth,a scaled mesh is first transmitted. Starting from a default texture quality,we apply an efficient Harmonic Time Compensation Algorithm based on the current bandwidth and a time limit, to adaptively adjust the texture quality of the next fragment to be transmitted. Properties of the algorithm are proved. Experimental results show the usefulness of our approach.
Paul Filitchkin - One of the best experts on this subject based on the ideXlab platform.
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Feature-based terrain classification for LittleDog
2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012Co-Authors: Paul FilitchkinAbstract:Recent work in terrain classification has relied largely on 3D sensing methods and color based classification. We present an approach that works with a single, compact camera and maintains high classification rates that are robust to changes in illumination. Terrain is classified using a bag of visual words (BOVW) created from speeded up robust Features (SURF) with a support vector machine (SVM) classifier. We present several novel techniques to augment this approach. A gradient descent inspired algorithm is used to adjust the SURF Hessian threshold to reach a nominal Feature Density. A sliding window technique is also used to classify mixed terrain images with high resolution. We demonstrate that our approach is suitable for small legged robots by performing real-time terrain classification on LittleDog. The classifier is used to select between predetermined gaits to traverse terrain of varying difficulty. Results indicate that real-time classification in-the-loop is faster than using a single all-terrain gait.
Qing Su - One of the best experts on this subject based on the ideXlab platform.
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A min-variance iterative method for fast smart dummy Feature Density assignment in chemical-mechanical polishing
Sixth international symposium on quality electronic design (isqed'05), 2005Co-Authors: Xin Wang, C.c. Chiang, J. Kawa, Qing SuAbstract:Dummy Feature filling is an efficient approach for reducing wafer-topography variation in chemical-mechanical polishing (CMP), which is the key planarization process in modern VLSI fabrication. In this paper, we present a new min-variance iterative method for fast smart dummy Feature Density assignment and post-CMP topography variation reduction. This method iteratively selects target areas using an efficient CMP low-pass filter model and a variance-minimizing heuristic, and assigns/removes dummy Features accordingly. Because of the efficient usage of the 2D fast Fourier transform (FFT), the computational cost of this new method is close to O(nlog(n)), making it much faster than the existing linear programming method that costs O(n/sup 3/). Numerical experiments show the computational cost of our new method is almost negligible when compared with the LP method and its solution is very close to the optimal solution.
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ISQED - A min-variance iterative method for fast smart dummy Feature Density assignment in chemical-mechanical polishing
Sixth International Symposium on Quality of Electronic Design (ISQED'05), 2005Co-Authors: Xin Wang, C.c. Chiang, J. Kawa, Qing SuAbstract:Dummy Feature filling is an efficient approach for reducing wafer-topography variation in chemical-mechanical polishing (CMP), which is the key planarization process in modern VLSI fabrication. In this paper, we present a new min-variance iterative method for fast smart dummy Feature Density assignment and post-CMP topography variation reduction. This method iteratively selects target areas using an efficient CMP low-pass filter model and a variance-minimizing heuristic, and assigns/removes dummy Features accordingly. Because of the efficient usage of the 2D fast Fourier transform (FFT), the computational cost of this new method is close to O(nlog(n)), making it much faster than the existing linear programming method that costs O(n/sup 3/). Numerical experiments show the computational cost of our new method is almost negligible when compared with the LP method and its solution is very close to the optimal solution.
