OPC model accuracy study using high volume contour based gauges and deep learning on memory device

Kim, Young-Seok; Lee, SeIl; Hou, Zhenyu; Zhao, Yiqiong; Li, Meng; Zheng, Yunan; Zhao, Qian; Kang, Daekwon; Wang, Lei; Simmons, Mark; Mu, Feng; Lang, Jun; Choi, Byoung Lyong; Kim, Gilbert; Sim, Hakyong; Park, Jongcheon; Yoo, Gyun; Lee, JeonKyu; Ko, Sung-Woo; Choi, Jae-Seung; Kim, Cheolkyun; Park, Chanha

doi:10.1117/12.2515274

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…It seems that the LWR becomes larger as the absolute defocus becomes larger (left and right images of the bottom row), and the isolated LS is not printing (right image of the bottom row). To quantify the patterning quality, ASML MXP tool 25,26 is used to extract the CD gauges, as well as the CD variation across the focus exposure matrix on the wafer. Figure 19 shows the OPW of the measured two clips where the main features shown in Fig.…”

Section: Opw Evaluation Of Random Logic Metal Design At Pitch 28 Nm F...mentioning

confidence: 99%

Extend 0.33 NA extreme ultraviolet single patterning to pitch 28-nm metal design by low-n mask

Gillijns

Tan

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Extending 0.33 NA extreme ultraviolet single patterning to 28-nm pitch becomes challenging in stochastic defectivity, which demands high-contrast lithographic images. The low-n attenuated phase-shift mask (attPSM) can provide superior solutions for individual pitches by mitigating mask three-dimensional effects. The simulation and experiment results have shown substantial imaging improvements: higher depth of focus at similar normalized image log slope and smaller telecentricity error values than the best binary mask configuration. In this work, the exploration of low-n attPSM patterning opportunity for pitch 28-nm metal design is investigated. Using generic building block features, the lithographic performance of the low-n attPSM is compared with the standard binary Ta-based absorber mask. In addition, the impact of mask tone (bright field (BF) versus dark field) on the pattern fidelity and process window is evaluated both by simulations and experiments. The results indicate that BF low-n attPSM provides the best patterning performance. Consequently, the BF low-n attPSM patterning performance is assessed with an actual imec N3 pitch 28-nm random logic metal design. The wafer data indicate BF low-n attPSM enables good patterning fidelity, as well as good overall process window with high exposure latitude (∼20%).

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Section: Opw Evaluation Of Random Logic Metal Design At Pitch 28 Nm F...mentioning

confidence: 99%

Extend 0.33 NA extreme ultraviolet single patterning to pitch 28-nm metal design by low-n mask

Gillijns

Tan

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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“…A CNN has been used to predict CD from input aerial image [19], in which the accuracy is good but the parameters involved are too many and difficult to optimize. Another option is to use fully convolutional networks to predict resist contours from input aerial image [19]; in this application, reference resist contours (often extracted from SEM image) have to be very high quality [22].…”

Section: For Resist Modelmentioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

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Machine learning models have been applied to a wide range of computational lithography applications since around 2010. They provide higher modeling capability, so their application allows modeling of higher accuracy. Many applications which are computationally expensive can take advantage of machine learning models, since a well trained model provides a quick estimation of outcome. This tutorial reviews a number of such computational lithography applications that have been using machine learning models. They include mask optimization with OPC (optical proximity correction) and EPC (etch proximity correction), assist features insertion and their printability check, lithography modeling with optical model and resist model, test patterns, and hotspot detection and correction.

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“…By describing completely any pattern given by a SEM image, SEM contours are very valuable for OPC compact model [2,3,4,5] or rigorous model [6] calibrations because they improve the design space coverage. SEM-contours are a very rich data source for any machine learning application [7,8]. In addition, SEM-contours from different images can be combined to characterize the evolution of a pattern upon different process conditions, like microlens reflow [9], litho-etch bias [10] or perform in-situ overlay between patterns measured on different layers during the manufacturing [11].…”

Section: Introductionmentioning

confidence: 99%

Investigating SEM-contour to CD-SEM matching

Weisbuch

Schatz

Mattick

et al. 2021

Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV

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Background:The control and the characterization of semiconductor very fine devices on a wafer are commonly performed by mean of a scanning electron microscope (SEM) to derive a critical dimension (CD) from a pair of parallel edges extracted from the images. However, this approach is often not very reliable when dealing with complex 2D patterns. An alternative is to use SEM contour technique to extract all the edges of the image. This method is more versatile and robust but before being implemented in a manufacturing environment, it must demonstrate that it can be matched well with traditional CD-SEM. Aim:The objective of this work is to present a method to evaluate and optimize the CD matching between a reference standard SEM-CD and SEM-Contours. Approach:After describing the metric used to assess the matching performance, we propose to screen some important influent parameters to give an evaluation of the best matching that we achieved with our experimental data. Results:After optimizing the matching calibration parameters and optimizing the selection of the best anchor pattern for the matching we could achieve a 3s-Total Measurement Uncertainty of 0.8 nm and 3.2 nm for 1D and 2D patterns. Conclusions:We established a method to achieve good matching performance that should facilitate the introduction of SEM contour in a manufacturing environment.

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OPC model accuracy study using high volume contour based gauges and deep learning on memory device

Cited by 5 publications

References 2 publications

Extend 0.33 NA extreme ultraviolet single patterning to pitch 28-nm metal design by low-n mask

Extend 0.33 NA extreme ultraviolet single patterning to pitch 28-nm metal design by low-n mask

Computational Lithography Using Machine Learning Models

Investigating SEM-contour to CD-SEM matching

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