20 March 2018 Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency
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Abstract
Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time.

Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO).

Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy.

ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges.

In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shibing Wang, Shibing Wang, Stanislas Baron, Stanislas Baron, Nishrin Kachwala, Nishrin Kachwala, Chidam Kallingal, Chidam Kallingal, Dezheng Sun, Dezheng Sun, Vincent Shu, Vincent Shu, Weichun Fong, Weichun Fong, Zero Li, Zero Li, Ahmad Elsaid, Ahmad Elsaid, Jin-Wei Gao, Jin-Wei Gao, Jing Su, Jing Su, Jung-Hoon Ser, Jung-Hoon Ser, Quan Zhang, Quan Zhang, Been-Der Chen, Been-Der Chen, Rafael Howell, Rafael Howell, Stephen Hsu, Stephen Hsu, Larry Luo, Larry Luo, Yi Zou, Yi Zou, Gary Zhang, Gary Zhang, Yen-Wen Lu, Yen-Wen Lu, Yu Cao, Yu Cao, } "Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency", Proc. SPIE 10587, Optical Microlithography XXXI, 105870N (20 March 2018); doi: 10.1117/12.2299421; https://doi.org/10.1117/12.2299421
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