Paper
13 March 2012 Fast source independent estimation of lithographic difficulty supporting large scale source optimization
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Abstract
Many chip design and manufacturing applications including design rules development, optical proximity correction tuning, and source optimization can benefit from rapid estimation of relative difficulty or printability. Simultaneous source optimization of thousands of clips has been demonstrated recently, but presents performance challenges. We describe a fast, source independent method to identify patterns which are likely to dominate the solution. In the context of source optimization the estimator may be used as a filter after clustering, or to influence the selection of representative cluster elements. A weighted heuristic formula identifies spectral signatures of several factors contributing to difficulty. Validation methods are described showing improved process window and reduced error counts on 22 nm layout compared with programmable illuminator sources derived from hand picked patterns, when the formula is used to influence training clip selection in source optimization. We also show good correlation with fail prediction on a source produced with hand picked training clips with some level of optical proximity correction tuning.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David DeMaris, Maria Gabrani, Sankha Subhra Sarkar, Nathalie Casati, Ronald Luijten, Kafai Lai, and Kehan Tian "Fast source independent estimation of lithographic difficulty supporting large scale source optimization", Proc. SPIE 8326, Optical Microlithography XXV, 832614 (13 March 2012); https://doi.org/10.1117/12.916433
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Cited by 1 scholarly publication and 2 patents.
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KEYWORDS
Optical proximity correction

Lithography

Metals

Diffraction

Source mask optimization

Image quality

Modulation transfer functions

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