Simultaneous source-mask optimization: a numerical combining method

Thomas Mülders; Vitaliy Domnenko; Bernd Küchler; Thomas Klimpel; Hans-Jürgen Stock; Amyn A. Poonawala; Kunal N. Taravade; William A. Stanton

doi:10.1117/12.865965

24 September 2010 Simultaneous source-mask optimization: a numerical combining method

Thomas Mülders, Vitaliy Domnenko, Bernd Küchler, Thomas Klimpel, Hans-Jürgen Stock, Amyn A. Poonawala, Kunal N. Taravade, William A. Stanton

Author Affiliations +

Proceedings Volume 7823, Photomask Technology 2010; 78233X (2010) https://doi.org/10.1117/12.865965
Event: SPIE Photomask Technology, 2010, Monterey, California, United States

Abstract

A new method for simultaneous Source-Mask Optimization (SMO) is presented. In order to produce optimum imaging fidelity with respect to exposure lattitude, depth of focus (DoF) and mask error enhancement factor (MEEF) the presented method aims to leverage both, the available degrees of freedom of a pixelated source and those available for the mask layout. The approach described in this paper is designed as to work with dissected mask polygons. The dissection of the mask patterns is to be performed in advance (before SMO) with the Synopsys Proteus OPC engine, providing the available degrees of freedom for mask pattern optimization. This is similar to mask optimization done for optical proximity correction (OPC). Additionally, however, the illumination source will be simultaneously optimized. The SMO approach borrows many of the performance enhancement methods of OPC software for mask correction, but is especially designed as to simultaneously optimize a pixelated source shape as nowadays available in production environments. Designed as a numerical optimization approach the method is able to assess in acceptable times several hundreds of thousands source-mask combinations for small, critical layout snippets. This allows a global optimization scheme to be applied to the SMO problem which is expected to better explore the optimization space and thus to yield an improved solution quality compared to local optimizations methods. The method is applied to an example system for investigating the impact of source constraints on the SMO results. Also, it is investigated how well possibly conflicting goals of low MEEF and large DoF can be balanced.

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Thomas Mülders, Vitaliy Domnenko, Bernd Küchler, Thomas Klimpel, Hans-Jürgen Stock, Amyn A. Poonawala, Kunal N. Taravade, and William A. Stanton "Simultaneous source-mask optimization: a numerical combining method", Proc. SPIE 7823, Photomask Technology 2010, 78233X (24 September 2010); https://doi.org/10.1117/12.865965

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