Model-based SRAF insertion through pixel-based mask optimization at 32nm and beyond

Sakajiri, Kyohei; Tritchkov, Alexander; Granik, Yuri

doi:10.1117/12.798440

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…Inverse lithography (IL) defines the objective function in such a way that the target intensity of the dark region is set to zero and the target intensity of the clear region is set to I max , where I max is the peak intensity taken from the dense array's intensity measurement [5]. Pixel optimization based on this objective function definition tries to achieve uniformity for all patterns such that all patterns attain nearly the same peak intensity while keeping the background intensity very close to zero.…”

Section: % Attpsm and Inverse Lithographymentioning

confidence: 99%

Application of pixel-based mask optimization technique for high transmission attenuated PSM

et al. 2009

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Sub-resolution assist features (SRAF) insertion using mask synthesis process based on pixel-based mask optimization schemes has been studied in recent years for various lithographical schemes, including 6% attenuated PSM (AttPSM) with off-axis illumination. This paper presents results of application of the pixelbased optimization technology to 6% and 30% AttPSM mask synthesis. We examine imaging properties of mask error enhancement factor (MEEF), critical dimension (CD) uniformity, and side-lobe printing for random contact hole patterns. We also discuss practical techniques for manipulating raw complex shapes generated by the pixel-based optimization engine that ensure mask manufacturability.

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Section: % Attpsm and Inverse Lithographymentioning

confidence: 99%

Application of pixel-based mask optimization technique for high transmission attenuated PSM

et al. 2009

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“…On one hand, there are rulebased approaches that can achieve acceptable accuracy within short execution time for simple designs and regular target patterns; yet fall short of handling complex shapes [52], [53]. On the other hand, model-based SRAF generation methods have been proposed relying on either simulated aerial images to seed the SRAF generation [54], [55], or inverse lithography technology (ILT) to compute the image contour and guide the SRAF generation [56]. Despite better lithographic performance compared to the rule-based approach, the model-based SRAF generation is very time-consuming [51].…”

Section: B Tempomentioning

confidence: 99%

Re-examining VLSI manufacturing and yield through the lens of deep learning

Alawieh

Pan

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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“…Recent advances in the field of model-based assist feature placement, mask optimization and inverse lithography has resulted in fully 2-dimensional assist feature patterns with nearly freeform shape and placement, constrained only by mask manufacturability rules and the risk of SRAF printing in resist. Advanced code has been developed to generate the most optimum mask shapes for both main and sub-resolution assist features as shown in Figure 1(c), for the patterning of the resist with the highest fidelity across process conditions [2][3][4][5][6]. …”

Section: Sub-resolution Assist Features Evolutionmentioning

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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Model-based SRAF insertion through pixel-based mask optimization at 32nm and beyond

Cited by 12 publications

References 6 publications

Application of pixel-based mask optimization technique for high transmission attenuated PSM

Application of pixel-based mask optimization technique for high transmission attenuated PSM

Re-examining VLSI manufacturing and yield through the lens of deep learning

Process optimization through model based SRAF printing prediction

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