Accuracy of diffused aerial image model for full-chip-level optical proximity correction

2000 ◽  
Author(s):  
Jee-Suk Hong ◽  
Hee-Bom Kim ◽  
Hyoung-Soon Yune ◽  
Chang-Nam Ahn ◽  
Young-Mo Koo ◽  
...  
Keyword(s):  
Aerial Image ◽  
Image Model ◽  
Optical Proximity Correction

Aerial image model and application to aberration measurement

2010 ◽  
Author(s):  
Anatoly Y. Bourov ◽  
Liang Li ◽  
Zhiyong Yang ◽  
Fan Wang ◽  
Lifeng Duan
Keyword(s):  
Aerial Image ◽  
Image Model

Aerial image of 3D phase-shifted reticle: 3D fast aerial image model

1994 ◽  
Author(s):  
Eytan Barouch ◽  
Uwe Hollerbach ◽  
Steven A. Orszag
Keyword(s):  
Aerial Image ◽  
Image Model

Fast and accurate optical proximity correction based on aerial image simulation

1996 ◽  
Author(s):  
Tetsuro Hanawa ◽  
Kazuya Kamon ◽  
Akihiro Nakae ◽  
Shuji Nakao ◽  
Koichi Moriizumi
Keyword(s):  
Aerial Image ◽  
Image Simulation ◽  
Optical Proximity Correction

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Explanation of Color Lines Based on a Simple Color Image Model

2018 ◽  
Vol 2018 (16) ◽  
pp. 296-1-296-5
Author(s):  
Megan M. Fuller ◽  
Jae S. Lim
Keyword(s):  
Color Image ◽  
Image Model

Localization Techniques on a 0.15um CMOS Device: Charge Pump Failure Analysis

2002 ◽  
Author(s):  
Hui Pan ◽  
Thomas Gibson
Keyword(s):  
Failure Analysis ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Electrical Characterization ◽  
Charge Pump ◽  
Front Side ◽  
Pump Failure ◽  
Electrical Failure ◽  
Optical Proximity Correction ◽  
A Charge

Abstract In recent years, there have been many advances in the equipment and techniques used to isolate faults. There are many options available to the failure analyst. The available techniques fall into the categories of electrical, photonic, thermal and electron/ion beam [1]. Each technique has its advantages and its limitations. In this paper, we introduce a case of successful failure analysis using a combination of several fault localization techniques on a 0.15um CMOS device with seven layers of metal. It includes electrical failure mode characterization, front side photoemission, backside photoemission, Focused Ion Beam (FIB), Scanning Electron Microscope (SEM) and liquid crystal. Electrical characterization along with backside photoemission proved most useful in this case as a poly short problem was found to be causing a charge pump failure. A specific type of layout, often referred to as a hammerhead layout, and the use of Optical Proximity Correction (OPC) contributed to the poly level shorts.

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