A new paradigm for in-line detection and control of patterning defects

Hunsche, S.; Jochemsen, M.; Jain, Vivek; Zhou, Xinjian; Chen, Fanglin; Vellanki, Venu; Spence, Chris; Halder, Sandip; Heuvel, Dieter Van den; Truffert, Vincent

doi:10.1117/12.2087178

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…The topography being a cause of focus intrafield excursions, this method allows the definition of care areas where focus margin will be reduced and defectivity may occur at a higher rate. The use of this data as an input for PWO [9,10] will allow an improved defect prediction. The care areas were also used as an input for a smart leveling methodology where the scanner will correct preferentially the critical areas of the chip.…”

Section: Resultsmentioning

confidence: 99%

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Verification and application of multi-source focus quantification

Simiz

Hasan²,

Staals

et al. 2016

SPIE Proceedings

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Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Proc. of SPIE Vol. 9781 97810S-2 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Proc. of SPIE Vol. 9781 97810S-3 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Section: Resultsmentioning

confidence: 99%

“…areas in which critical patterns in terms of imaging, determined by full chip LMC [9,10], and high local topography variation might be found, causing high probability of patterning failure. The capability of defining smart care areas is key for process improvement efficiency.…”

Section: -Modelling Topography With Gds Densitiesmentioning

confidence: 99%

Verification and application of multi-source focus quantification

Simiz

Hasan²,

Staals

et al. 2016

SPIE Proceedings

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“…The layout dependent local topography investigation in this paper is also linked to focus induced defectivity. The defect detection and control of this is covered in the paper and talk: A new paradigm for inline detection and control of patterning defects [8].…”

Section: Resultsmentioning

confidence: 99%

Predictability and impact of product layout induced topology on across-field focus control

et al. 2015

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With continuing dimension shrinkage using the TWINSCAN NXT:1950i scanner on the 28nm node and beyond, the imaging depth of focus (DOF) becomes more critical. Focus budget breakdown studies [Ref 1, 5] show that even though the intrafield component stays the same this becomes a larger relative percentage of the overall DOF. Process induced topography along with reduced Process Window can lead to yield limitations and defectivity issues on the wafer. To improve focus margin, a study has been started to determine if some correlations between scanner levelling performance, product layout and topography can be observed. Both topography and levelling intrafield fingerprints show a large systematic component that seems to be product related. In particular, scanner levelling measurement maps present a lot of similarities with the layout of the product. The present paper investigates the possibility to model the level sensor's measured height as a function of layer design densities or perimeter data of the product. As one component of the systematics from the level sensor measurements is process induced topography due to previous deposition, etching and CMP, several layer density parameters were extracted from the GDS's. These were combined through a multiple variable analysis (PLS: Partial Least Square regression) to determine the weighting of each layer and each parameter. Current work shows very promising results using this methodology, with description quality up to 0.8 R² and expected prediction quality up to 0.78 Q². Since product layout drives some intrafield focus component it is also important to be able to assess intrafield focus uniformity from post processing. This has been done through a hyper dense focus map experiment which is presented in this paper.

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“…Nowadays, the designed feature size of IC is below 10 nm, and the number of transistors of an IC is as high as tens of billions [1,2]. With the demand for high integration and better performance, the physical design of IC continues to shrink, and the lithographic printability has become one of the critical issues in IC design and manufacturing [3,4]. Affected by the layout design and lithography process, the lithography results of some patterns in the layout are quite different from the target patterns, resulting in short-circuit or open-circuit problems.…”

Section: Introductionmentioning

confidence: 99%

Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network

Liao

Che

et al. 2022

Applied Sciences

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As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic printability of the design has become one of the important issues in IC design and manufacturing. There are patterns that cause lithography hotspots in the IC layout. Hotspot detection affects the turn-around time and the yield of IC manufacturing. The precision and F1 score of available machine-learning-based hotspot-detection methods are still insufficient. In this paper, a lithography hotspot detection method based on transfer learning using pre-trained deep convolutional neural network is proposed. The proposed method uses the VGG13 network trained with the ImageNet dataset as the pre-trained model. In order to obtain a model suitable for hotspot detection, the pre-trained model is trained with some down-sampled layout pattern data and takes cross entropy as the loss function. ICCAD 2012 benchmark suite is used for model training and model verification. The proposed method performs well in accuracy, recall, precision, and F1 score. There is significant improvement in the precision and F1 score. The results show that updating the weights of partial convolutional layers has little effect on the results of this method.

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A new paradigm for in-line detection and control of patterning defects

Cited by 6 publications

References 10 publications

Verification and application of multi-source focus quantification

Verification and application of multi-source focus quantification

Predictability and impact of product layout induced topology on across-field focus control

Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network

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