Mr. Aram Kazarian Profile

Aram Kazarian

at Synopsys Inc

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Publications (7)

Proceedings Article | 28 September 2017 Paper

ILT optimization of EUV masks for sub-7nm lithography

Kevin Hooker, Bernd Kuechler, Aram Kazarian, Guangming Xiao, Kevin Lucas

Proceedings Volume 10446, 1044604 (2017) https://doi.org/10.1117/12.2279912

KEYWORDS: Extreme ultraviolet, Lithography, Extreme ultraviolet lithography, Photomasks, Optical lithography, Optical proximity correction, Resolution enhancement technologies

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Proceedings Article | 24 March 2017 Presentation + Paper

New methodologies for lower-K1 EUV OPC and RET optimization

Kevin Hooker, Aram Kazarian, Xibin Zhou, Josh Tuttle, Guangming Xiao, Yunqiang Zhang, Kevin Lucas

Proceedings Volume 10143, 101431C (2017) https://doi.org/10.1117/12.2258186

KEYWORDS: Extreme ultraviolet, Optical lithography, Extreme ultraviolet lithography, Photomasks, Optical proximity correction, Resolution enhancement technologies, Reticles, SRAF, Semiconducting wafers, Lithography, Critical dimension metrology, Manufacturing, Atrial fibrillation

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Proceedings Article | 13 March 2012 Paper

Resist loss in 3D compact modeling

Xin Zheng, Jensheng Huang, Fook Chin, Aram Kazarian, Chun-Chieh Kuo

Proceedings Volume 8326, 83261C (2012) https://doi.org/10.1117/12.916582

KEYWORDS: 3D modeling, Optical proximity correction, Calibration, Data modeling, Scanning electron microscopy, Mathematical modeling, Photoresist materials, Optical lithography, Photomasks, Lithography

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Proceedings Article | 4 March 2010 Paper

A new etch-aware after development inspection (ADI) technique for OPC modeling

Jing Xue, Jason Huang, Aram Kazarian, Brad Falch

Proceedings Volume 7640, 76402Q (2010) https://doi.org/10.1117/12.846683

KEYWORDS: Etching, Data modeling, Lithography, Optical proximity correction, Calibration, Inspection, Performance modeling, Optical lithography, Semiconducting wafers, Process modeling

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Proceedings Article | 16 March 2009 Paper

Chemically amplified resist modeling in OPC

Xin Zheng, Jason Huang, Fred Kuo, Aram Kazarian, Fook Chin, Yongfa Fan

Proceedings Volume 7274, 727435 (2009) https://doi.org/10.1117/12.814354

KEYWORDS: Optical proximity correction, Chemically amplified resists, Calibration, Data modeling, Diffusion, Semiconducting wafers, Image enhancement, Optical resolution, Optical lithography, Image analysis

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Proceedings Article | 24 March 2008 Paper

Empirical data validation for model building

Aram Kazarian

Proceedings Volume 6922, 69221I (2008) https://doi.org/10.1117/12.773414

KEYWORDS: Data modeling, Optical proximity correction, Process modeling, Photoresist processing, Statistical modeling, Critical dimension metrology, Calibration, Metrology, Mathematical modeling, Data processing

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Optical Proximity Correction (OPC) has become an integral and critical part of process development for advanced technologies with challenging k₁ requirements. OPC solutions in turn require stable, predictive models to be built that can project the behavior of all structures. These structures must comprehend all geometries that can occur in the layout in order to define the optimal corrections by feature, and thus enable a manufacturing process with acceptable margin. The model is built upon two main component blocks. First, is knowledge of the process conditions which includes the optical parameters (e.g. illumination source, wavelength, lens characteristics, etc) as well as mask definition, resist parameters and process film stack information. Second, is the empirical critical dimension (CD) data collected using this process on specific test features the results of which are used to fit and validate the model and to project resist contours for all allowable feature layouts. The quality of the model therefore is highly dependent on the integrity of the process data collected for this purpose. Since the test pattern suite generally extends to below the resolution limit that the process can support with adequate latitude, the CD measurements collected can often be quite noisy with marginal signal-to-noise ratios. In order for the model to be reliable and a best representation of the process behavior, it is necessary to scrutinize empirical data to ensure that it is not dominated by measurement noise or flyer/outlier points. The primary approach for generating a clean, smooth and dependable empirical data set should be a replicated measurement sampling that can help to statistically reduce measurement noise by averaging. However, it can often be impractical to collect the amount of data needed to ensure a clean data set by this method. An alternate approach is studied in this paper to further smooth the measured data by means of curve fitting to identify remaining questionable measurement points for engineering scrutiny since they may run the risk of incorrectly skewing the model. In addition to purely statistical data curve fitting, another concept also merits investigation, that of using first principle, simulation-based characteristic coherence curves to fit the measured data.

Proceedings Article | 12 March 2008 Paper

Resist bias measured in Iso-focal structure

Jianliang Li, Chunqing Wang, Aram Kazarian, Qiliang Yan, Lawrence Melvin

Proceedings Volume 6924, 692449 (2008) https://doi.org/10.1117/12.772912

KEYWORDS: Optical proximity correction, Cadmium, Critical dimension metrology, Wafer-level optics, Process modeling, Image processing, Optical lithography, Computer simulations, Optical simulations, Semiconducting wafers

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Showing 5 of 7 publications

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