Handbook of Algorithms for Physical Design Automation part 26 provides a detailed overview of VLSI physical design automation, emphasizing state-of-the-art techniques, trends and improvements that have emerged during the previous decade. After a brief introduction to the modern physical design problem, basic algorithmic techniques, and partitioning, the book discusses significant advances in floorplanning representations and describes recent formulations of the floorplanning problem. The text also addresses issues of placement, net layout and optimization, routing multiple signal nets, manufacturability, physical synthesis, special nets, and designing for specialized technologies. It includes a personal perspective from Ralph Otten as he looks back on. | 232 Handbook of Algorithms for Physical Design Automation FIGURE 3D placement. The corresponding 3D-subTCG is given in the Figure . From Yuh . Yang . Chang . and Chen . Proceedings of Asia and South Pacific Design Automation Conference 2004. With permission. nb 1 ne nd na C v nc 2 2 O nf FIGURE Corresponding 3D-subTCG of Figure . From Yuh . Yang . Chang . and Chen . Proceedings of Asia and South Pacific Design Automation Conference 2004. With permission. On the basis of previous works 22 23 the T-tree and 3D-subTCG outperform sequence triplet. Further the T-tree outperforms the 3D-subTCG in terms of packing efficiency and volume optimization due to its relatively simpler tree representation and good neighborhood structure. Nevertheless the 3D-subTCG has the following three advantages over the T-tree 3D-subTCG is a fully topological representation that can represent the general topological modeling of tasks and thus contains a complete solution structure for searching the optimal floorplan placement solution. In contrast T-tree is a partially topological representation and can only represent part of the compacted 3D floorplans where each task must be compacted to the origin. Because the relation between each pair of tasks is defined in the representation the geometric relation of each pair of tasks is transparent to both the 3D-subTCG representation and its induced operations. Thus we can perform the feasibility detection before perturbation to guarantee the satisfaction of precedence constraints. In contrast T-tree is a partially topological representation where some geometric relations among tasks cannot be obtained directly from representation. Thus it is harder to detect the violations of the precedence constraints before packing and a postprocessing is required to guarantee the feasibility of the solutions after packing. Because the geometric relations among tasks can be directly obtained from the .
