Exploiting structure in fast aerial image computation for integrated circuit patterns

Pati, Y.C.; Ghazanfarian, Amir Aalam; Pease, R. F. W.

doi:10.1109/66.554485

Cited by 48 publications

(31 citation statements)

References 4 publications

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“…Section 2 will examine the Hopkins' model for periodic objects in more detail. Clearly, such a scheme is also the basis of the "fast" schemes currently in use in OPC design tools, and the interested reader is referred to the several excellent publications on the subject 4,5 . Section 3 contains the thrust of the new results presented in this work.…”

Section: Introductionmentioning

confidence: 99%

<title>Exact computation of scalar 2D aerial imagery</title>

Gordon

2002

SPIE Proceedings

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An exact formulation of the problem of imaging a 2D object through a Köhler illumination system is presented; the accurate simulation of a real layout is then not time-limited, but memory-limited. The main idea behind the algorithm is that the boundary of the region that comprise a typical TCC is made up of circular arcs, and therefore the area -which determines the value of the TCC -should be exactly computable in terms of elementary analytical functions. A change to integration around the boundary leads to an expression with minimal dependence on expensive functions such as arctangents and square roots. Numerical comparisons are made for a simple 2D structure.

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Section: Introductionmentioning

confidence: 99%

<title>Exact computation of scalar 2D aerial imagery</title>

Gordon

2002

SPIE Proceedings

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“…The numeric aperture of the lens (NA), the illumination wavelength ( ), and the coherence of the imaging system ( ) are the key input parameters which shape the Hopkins integral. Using these parameters and several assumptions of the image/projection system [16] allow the Hopkins integral to be expressed as a two-dimensional convolution integral which allows the expression to be evaluated using numerically efficient fast Fourier transforms. By taking equi-illumination contours at the target, an estimate of the optical proximity component of lithography variance is enabled.…”

Section: Simulation Methodologymentioning

confidence: 99%

“…Optical proximity components are estimated using aerial imaging [16], [17]. Aerial imaging has been in practice for approximately ten years and most simulators use the Hopkins model approach [17].…”

Section: Simulation Methodologymentioning

confidence: 99%

Simulating the impact of pattern-dependent poly-CD variation on circuit performance

Stine

Boning

Chung

et al. 1998

IEEE Trans. Semicond. Manufact.

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“…A widely used optical model is based on coherent decomposition of partially coherent imaging system that has the following form [1] ∑ ∫∫ ∑ ∫∫…”

Section: Optical Proximity Model and Model Based Data Processingmentioning

confidence: 99%

Simulation-based data processing using repeated pattern identification

Zhang¹

2003

SPIE Proceedings

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In typical integrated circuits (IC) designs, the final layout generally contains a lot of repeated patterns. Many of these repetitions are captured by the layout hierarchy. That is, the layout contains many cells that are each repeatedly placed in many locations with different transformation. Effective use of such repetition information in the computation intensive operations such as model-based optical proximity correction (OPC), verification, or contour generation, can lead to significant performance improvement. However, in many other occasions, such repetition information is not directly available. For example, if the layout is flattened, then all the hierarchy that captures the repetition information is lost. Even in hierarchical layout, a cell can contain repeated geometries or patterns. In order for the application to take advantage of such property, a mechanism to efficiently capture such repetition information is necessary.In this paper, we consider the model-based applications that have a unique property, which allows us to find different geometrical patterns that are equivalent in principle for simulation purpose. We introduce a proximity-based pattern identification method which aims at recognizing the maximum amount of repetition in the layout. This method not only captures repeated or symmetric geometries that are present from either the flattening of the hierarchy or within a cell itself, but also finds symmetries within the geometries themselves. The method also finds partial repetitions of geometries that are not completely identical or symmetric. Ideally, these "equivalent" patterns will eventually carry the same processing results with miniscule variations small enough to be ignored for the application. For this reason, it is sufficient to run the computationally expensive model-based operations for one of the pattern and carry the result to the rest of the patterns of the same family. Doing so reduces the problem size as well as the amount of data that requires processing. The total processing time therefore can be dramatically reduced.We demonstrate the method by using OPC as a test example. We show the level of problem size reduction and job run time reduction due to the specific nature of different layouts.

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Exploiting structure in fast aerial image computation for integrated circuit patterns

Cited by 48 publications

References 4 publications

<title>Exact computation of scalar 2D aerial imagery</title>

<title>Exact computation of scalar 2D aerial imagery</title>

Simulating the impact of pattern-dependent poly-CD variation on circuit performance

Simulation-based data processing using repeated pattern identification

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