Improving model prediction accuracy for ILT with aggressive SRAFs

Jung, Sunggon; Sim, Woo Seog; Jeong, Mi‐Ae; Ser, Jung-Hoon; Lee, Sung‐Woo; Choi, Sung Won; Zhou, Xin; Luan, Lan; Cecil, Thomas; Son, Donghwan; Gleason, Robert E.; Kim, David

doi:10.1117/12.864528

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Approaches to checking for SRAF printing often use the same OPC model (both optical and resist models) as used for estimating the main feature line-width dimensions, occasionally increasing the complexity of the models to account for more advanced effects in the resist chemistry or the mask topography [8][9][10]. While this approach can be adequate for checking printing in the form of resist fragments leftover on the bottom surface after development, it usually requires applying large amounts of overdose in order to check for printing in the form of a shallow depressions on the resist surface.…”

Section: Setting Optical and Resist Modelsmentioning

confidence: 99%

Process optimization through model based SRAF printing prediction

2012

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Sub-Resolution Assist Features (SRAFs) are used in optical lithography to improve the manufacturing process window (PW). They are added to the mask shapes to create a denser environment that improves the printability of the target design shapes on wafer. As the critical dimensions (CDs) that need to be patterned shrink with every technology generation, SRAFs have become a critical and key component in enabling processes with manufacturable process windows. The size and placement of the SRAFs must be carefully optimized to provide the maximum benefit to the main feature while avoiding any printing on resist that could affect subsequent etching processes. The un-intended printing of assist features on wafer is a critical yield detractor and is especially pervasive in newer technology nodes, where more aggressive and more complex SRAF patterns and placement are becoming commonplace. The need for the accurate prediction of SRAF printing is therefore very important to achieve the maximum main feature process window benefit without any assist feature printing.Traditionally, the optimization of SRAF sizing and placement consisted of a set of rules obtained through the extensive analysis of wafer printability on a variety of assisted mask patterns while using Scanning Electron Microscope images of the resist surface to monitor unwanted SRAF printing. Recent advances in model-based assist feature optimization methods allow for the automated adjustment of both main feature and assist feature size and placement through simulation of the aerial image, but critically rely on the accuracy of the lithography process model to ensure non-printing of the SRAF. Lithography or Optical Proximity Correction (OPC) models usually comprising an optical and a resist model are calibrated to measurements of the resist bottom CD. These models are naturally better at predicting the printing of SRAFS that are lines in resist. When the SRAFs are holes in resist, for eg. assist features supporting main features on a dark field mask, or SRAFs supporting inverse tone features on a bright field mask, these models do not have the required accuracy in predicting SRAF printing. SRAF printability prediction has thus far been tackled by large dose adjustments to the OPC model, to match simulation to wafer results. The drawbacks of this method have been two-fold -simple dose adjustments do not accurately predict printing across various SRAF configurations and the main feature printability is compromised We present in this paper a method to calibrate and predict printing of assist features that appear as a dimpling in the resist surface, by carefully selecting the calibration data and separately tuning the model parameters for the main feature and of the SRAF printing models. With this method, we obtain a model that accurately predicts the printing of various configurations of SRAFs on wafer while still maintaining the accuracy on the main features. An analysis of the implementation of such a model in the OPC flow and the corresponding supporting results ...

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Section: Setting Optical and Resist Modelsmentioning

confidence: 99%

Process optimization through model based SRAF printing prediction

2012

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“…in out F tr = + ICC ICC J - (10) where the sizes of ICC in and ICC out are both N′ × S 2 and N′ denote the number of pixels on the margins of drawn patterns. From another point of view, Eq.…”

Section: Cost Functionsmentioning

confidence: 99%

“…In recent years, source optimization (SO) and mask optimization (MO) have attracted great interests among semiconductor foundries and equipment vendors because of its capability for further extending the life of 193-nm optical lithography [1][2][3][4][5][6][7][8][9][10][11][12][13]. With the availability of free-form sources using diffractive optical elements (DOE), SO serves as a new option for achieving higher resolution without increasing the complexity of mask design.…”

Section: Introductionmentioning

confidence: 99%

Fast source optimization involving quadratic line-contour objectives for the resist image

Yu¹,

Yu²,

Chao³

2012

Opt. Express

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In Abbe's formulation, source optimization (SO) is often formulated into a linear or quadratic problem, depending on the choice of objective functions. However, the conventional approach for the resist image, involving a sigmoid transformation of the aerial image, results in an objective with a functional form. The applicability of the resist-image objective to SO or simultaneous source and mask optimization (SMO) is therefore limited. In this paper, we present a linear combination of two quadratic line-contour objectives to approximate the resist image effect for fast convergence. The line-contour objectives are based on the aerial image on drawn edges using a constant threshold resist model and that of pixels associated with an intensity minimum for side-lobe suppression. A conjugate gradient method is employed to assure the convergence to the global minimum within the number of iterations less than that of source variables. We further compare the optimized illumination with the proposed line-contour objectives to that with a sigmoid resist-image using a steepest decent method. The results show a 100x speedup with comparable image fidelity and a slightly improved process window for the two cases studied.

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Improving model prediction accuracy for ILT with aggressive SRAFs

Cited by 2 publications

References 4 publications

Process optimization through model based SRAF printing prediction

Process optimization through model based SRAF printing prediction

Fast source optimization involving quadratic line-contour objectives for the resist image

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