10 December 2009 Source-mask co-optimization: optimize design for imaging and impact of source complexity on lithography performance
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Proceedings Volume 7520, Lithography Asia 2009; 75200D (2009); doi: 10.1117/12.838701
Event: SPIE Lithography Asia, 2009, Taipei, Taiwan
Abstract
The co-optimization of the source and mask patterns [1, 2] is vital to future advanced ArF technology node development. This paper extends work previously reported on this topic [3, 4]. We will systematically study the impact of source on designs with different k1 values using SMO. Previous work compared the co-optimized versus iterative source-mask optimization methods [3]. We showed that the co-optimization method clearly improved lithography performance. This paper's approach consists of: 1) Co-optimize a pixelated freeform source and a continuous transmission gray tone mask based on a user specified cost function; 2) ASML-certified scanner-specific models and constraints are applied to the optimized source; 3) Assist feature (AF) "seeds" are identified from the optimized continuous transmission mask (CTM). Both the AF seed and the main feature are subsequently converted into a polygon mask; 4) The extracted AF seeds and main features are co-optimized with the source to achieve the best lithographic performance. Using this approach, we first use a DRAM brick wall design to demonstrate that using the same cost function metric by adjusting the optimization conditions creates an image log slope only optimization that can easily be applied. An optimize design for imaging methodology is introduced and shown to be important for low k1 imaging. A typical 2x node SRAM design is used to illustrate an integrated SMO design rule optimization flow. We use the same SRAM layout that used design rule optimization to study the source complexity impact with a range of k1 values that varies from 0.42 to 0.35. For the source type, we use freeform and traditional finite pole shape DOEs, all subject to ASML's scanner-specific models and constraints. We report the process window, MEF and process variation band (PV band) with different source types to find which source type give the best lithography performance.
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Stephen Hsu, Zhipan Li, Luoqi Chen, Keith Gronlund, Hua-yu Liu, Robert Socha, "Source-mask co-optimization: optimize design for imaging and impact of source complexity on lithography performance", Proc. SPIE 7520, Lithography Asia 2009, 75200D (10 December 2009); doi: 10.1117/12.838701; https://doi.org/10.1117/12.838701
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KEYWORDS
Source mask optimization

Lithography

Photomasks

Diffractive optical elements

Photovoltaics

Atrial fibrillation

Optimization (mathematics)

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