Mr. Sebastian F. Munoz Profile

Sebastian F. Munoz

R&D Engineer, Sr I. at Synopsys Chile Ltd

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Proceedings Article | 23 August 2021 Paper

Model-driven rule-based mask process correction

Wai Yip Kwok, Johnny Yeap, Sebastian Munoz, Seurien Chou, Tokiharu Sekiya, Hari Konnanur

Proceedings Volume 11908, 1190806 (2021) https://doi.org/10.1117/12.2601035

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MPC has been a technology enabler since 32nm technology node, and the number of mask layers receiving MPC increases as technology node advances. Model-based Mask Process Correction (MB-MPC) has evolved from correction based on short-range Gaussian to full Machine Learning (ML) based model and correction. Model-based MPC has demonstrated efficacy in reducing mask error on advanced nodes, but often requires extensive computing resource to achieve the stringent mask fidelity and Critical Dimension (CD) requirements. On the other hand, rule-based Mask Process Correction (RB-MPC) has the advantage of fast turn-around time. This paper presents an approach to rule-based MPC that seeks to extract the maximum benefits of model-based MPC. The rules cover critical geometrical ‘building blocks’ such as lines, contacts, line-ends, notches. Derivation of the rules is guided by a mask process model. The goal of RB-MPC is to mitigate the long runtime of MB-MPC while minimizing loss in patterning fidelity. We will describe the methodology of rule derivation, implementation, and verification of RB-MPC. The RB-MPC approach meets accuracy requirements for 32-22nm technology nodes. For more advanced technology nodes, a hybrid RB-MB-MPC recipe is proposed to achieve both high accuracy and fast runtime.

Proceedings Article | 28 June 2013 Paper

Circle pattern detector & VSB shot count estimator

Sebastian Munoz, Raghava Kondepudy

Proceedings Volume 8701, 87010G (2013) https://doi.org/10.1117/12.2028495

KEYWORDS: Vestigial sideband modulation, Raster graphics, Sensors, Photomasks, Beam shaping, Statistical analysis, Data analysis, Optical proximity correction, Roads

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There are two main technologies commercially available to write photomasks: Raster Scanning and Variable Shape Beam (VSB). For masks with features sizes that can be written on either kind of machine, it is advantageous to estimate the write time on both kinds of machines. The machine that is expected to have a faster Turn Around Time could be chosen. It is trivial to estimate how much time a design would take to be written by using a Raster Scanning machine. Since this kind of machine scans the whole design area, its TAT depends mainly on the size of the design and the size of the pixel. The write time is therefore mostly independent of the number of figures composing the design data. Estimating how long the same design would take to be written by a VSB machine is more involved, since its TAT depends greatly on how data is organized and fractured. In other words, there is a direct relation between number of elementary data figures (rectangles and trapezoids) and writing time. In VSB machines, data with curvilinear geometries can produce a huge increase in the amount of shots needed to write a design, which in turn directly affects TAT. This paper presents a novel technique used to provide the user with relevant information to aid with deciding which technology is to be used for writing the mask. The technique yields two vital pieces of information: a) An estimation of the amount of VSB Shots needed by a VSB Machine to write the design data into a photomask, and b) A map of where curvilinear geometries are located throughout the design.

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