Cell Decomposition Approach Research Articles

Triple Patterning Lithography Aware Optimization and Detailed Placement Algorithms for Standard Cell-Based Designs

Triple patterning lithography (TPL) is regarded as a promising technique to handle the manufacturing challenges in the 14nm technology node and beyond. It is necessary to consider TPL in early design stages to make the layout more TPL friendly and reduce the manufacturing cost. In this paper, we propose a flow to co-optimize cell layout decomposition and detailed placement. Our cell decomposition approach can enumerate all coloring solutions with the minimum number of stitches. The experimental results show that our approach can outperform the existing work in all aspects of stitch number, half-perimeter wirelength (HPWL), and running time. We further extend our placer to consider the displacement of cells as a constraint and as an objective, respectively, which can help to preserve the quality of the input placement. Effectiveness of the extensions is verified by the experiments.

A cell decomposition approach to visibility-based pursuit evasion among obstacles

In this paper, we address the problem of surveillance in an environment with obstacles. We consider the problem in which a mobile observer attempts to maintain visual contact with a target as it moves through an environment containing obstacles. This surveillance problem is a variation of traditional pursuit–evasion games, with the additional condition that the pursuer immediately loses the game if at any time it loses sight of the evader. We analyze this tracking problem as a game of kind. We use the method of explicit policy to compute guaranteed strategies for surveillance for the observer in an environment containing a single corner. These strategies depend on the initial positions of the observer and the target in the workspace. Based on these strategies a partition of the visibility polygon of the players is constructed. The partitions have been constructed for varying speeds of the observer and the target. Using these partitions we provide a sufficient condition for escape of a target in a general environment containing polygonal obstacles. Moreover, for a given initial target position, we provide a polynomial-time algorithm that constructs a convex polygonal region that provides an upper-bound for the set of initial observer positions from which it does not lose the game. We extend our results to the case of arbitrary convex obstacles with differentiable boundaries. We also present a sufficient condition for tracking and provide a lower-bound on the region around the initial position of the target from which the observer can track the target. Finally, we provide an upper bound on the area of the region in which the outcome of the game is unknown.

A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network

A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane. The sensing-pursuit problem considered in this paper is analogous to the Marco Polo game, in which a pursuer Marco must capture multiple mobile targets that are sensed intermittently, and with very limited information. The competing objectives exhibited by this problem arise in a number of surveillance and monitoring applications. In this paper, the mobile sensor network consists of a set of robotic sensors that must track and capture mobile targets based on the information obtained through cooperative detections. When these detections form a satisfactory target track, a mobile sensor is switched to pursuit mode and deployed to capture the target in minimum time. Since the sensors are installed on robotic platforms and have limited range, the geometry of the platforms and of the sensors' fields-of-view play a key role in obstacle avoidance and target detection. A new cell-decomposition approach is presented to determine the probability of detection and the cost of operating the sensors from the geometric properties of the network and its workspace. The correctness and complexity of the algorithm are analyzed, proving that the termination time is a function of the network parameters and of the number of required detections.

Improved neighbor list algorithm in molecular simulations using cell decomposition and data sorting method

An improved neighbor list algorithm is proposed to reduce unnecessary interatomic distance calculations in molecular simulations. It combines the advantages of Verlet table and cell linked list algorithms by using cell decomposition approach to accelerate the neighbor list construction speed, and data sorting method to lower the CPU data cache miss rate, as well as partial updating method to minimize the unnecessary reconstruction of the neighbor list. Both serial and parallel performance of molecular dynamics simulation are evaluated using the proposed algorithm and compared with those using conventional Verlet table and cell linked list algorithms. Results show that the new algorithm outperforms the conventional algorithms by a factor of 2∼3 in cases of both small and large number of atoms.

Motion Planning in Environments with Low Obstacle Density

We present a simple and efficient paradigm for computing the exact solution of the motion planning problem in environments with a low obstacle density. Such environments frequently occur in practical instances of the motion planning problem. The complexity of the free space for such environments is known to be linear in the number of obstacles. Our paradigm is a new cell decomposition approach to motion planning and exploits properties that follow from the low density of the obstacles in the robot's workspace. These properties allow us to decompose the workspace, subject to some constraints, rather than to decompose the higher-dimensional free configuration space directly. A sequence of uniform steps transforms the workspace decomposition into a free space decomposition of asymptotically the same size. The approach leads to nearly optimal O(n \log n) motion planning algorithms for free-flying robots with any fixed number of degrees of freedom in workspaces with low obstacle density.

Determining Optimal Paths, Based on Time and Distance Metric Combinations, in a Dynamic 2-D Environment

In this paper, the problem of determining optimal paths in a dynamic 2-D environment with multiple, moving obstacles and multiple, moving goals is studied. Presented is a novel cell decomposition approach which generates optimal paths based on a number of different optimality criteria (time, distance, and minimum cost metric combinations). The approach uses a computational geometry technique for sweeping a 3-D space with a plane, called time-sweeping, which allows the calculation of these optimal paths much more efficiently than could be done with traditional path planning approaches in this area.

A framed-quadtree approach for determining Euclidean shortest paths in a 2-D environment

In this paper we investigate the problem of finding a Euclidean (L/sub 2/) shortest path between two distinct locations in a planar environment. We propose a novel cell decomposition approach which calculates an L/sub 2/ distance transform through the use of a circular path-planning wave. The proposed method is based on a new data structure, called the framed-quadtree, which combines together the accuracy of high resolution grid-based path planning techniques with the efficiency of quadtree-based techniques, hence having the advantages of both. The heart of this method is a linear time algorithm for computing certain special dynamic Voronoi diagrams. The proposed method does not place any unrealistic constraints on obstacles or on the environment and represents an improvement in accuracy and efficiency over traditional path planning approaches in this area.