Mr. Dale Legband Profile

Dale Legband

at KLA Texas

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Proceedings Article | 17 March 2008 Paper

32nm design rule evaluation through virtual patterning

Scott Jessen, James Blatchford, Steve Prins, Simon Chang, Yiming Gu, Mark Smith, Dale Legband, Chris Sallee

Proceedings Volume 6925, 692518 (2008) https://doi.org/10.1117/12.772685

KEYWORDS: Optical proximity correction, Semiconducting wafers, SRAF, Lithography, Photomasks, Optical lithography, Lithographic illumination, Image segmentation, Manufacturing, Fiber optic illuminators

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Lithography simulation has proven to be a technical enabler to shorten development cycle time and provide direction before next-generation exposure tools and processes are available. At the early stages of design rule definition for a new technology node, small critical areas of layout are of concern, and optical proximity correction (OPC) is required to allow full exploration of the 2D rule space. In this paper, we demonstrate the utility of fast, resist-model-based, OPC correction to explore process options and optimize 2D layout rules for advanced technologies. Unlike conventional OPC models that rely on extensive empirical CD-SEM measurements of real wafers, the resist-based OPC model for the correction is generated using measured bulk parameters of the photoresist such as dissolution rate. The model therefore provides extremely accurate analysis capability well in advance of access to advanced exposure tools. We apply this 'virtual patterning' approach to refine lithography tool settings and OPC strategies for a collection of 32-nm-node layout clips. Different OPC decorations including line biasing, serifs, and assist features, are investigated as a function of NA and illumination conditions using script-based optimization sequences. Best process conditions are identified based on optimal process window for a given set of random layouts. Simulation results, including resist profile and CD process window, are validated by comparison to wafer images generated on an older-generation exposure tool. The ability to quickly optimize OPC as a function of illumination setting in a single simulation package allows determination of optimum illumination source for random layouts faster and more accurately than what has been achievable in the past. This approach greatly accelerates design rule determination.

Proceedings Article | 14 June 1999 Paper

Data analysis for photolithography

Chris Mack, Sven Jug, Dale Legband

Proceedings Volume 3677, (1999) https://doi.org/10.1117/12.350829

KEYWORDS: Reflectivity, Lithography, Photoresist materials, Critical dimension metrology, Statistical analysis, Data analysis, Data centers, Optical lithography, Tolerancing, Matrices

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This paper will propose standard methodologies for analyzing common lithographic data in three areas: photoresist contrast curves, swing curves, and focus-exposure matrices. For most data types, physics-based algebraic equations will be proposed to fit the data. The coefficients of these equation will offer physical insight into the meaning and nature of the data. The equations will be fit to the data using standard non-linear least-squares fitting algorithms with standard statistical test for removing data flyers and options for weighting the data. Analysis of the resulting curve fits will provide important information about the data. For the case of contrast curve data, the curve fits will yield resist contrast and dose-to-clear results. For swing curves, the swing ratio, period and the positions of the minimums and maximums will be provided. For focus- exposure data, process windows will be generated based on resist profile specifications. These process windows will then be analyzed by fitting rectangles or ellipses inside the window and determining the resulting exposure latitude/depth of focus trade-of. By specifying the desired exposure latitude, for example, the depth of focus and the best focus and best exposure to yield this maximum depth of focus will be calculated. Multiple process window overlaps can also be analyzed.

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