Subresolution Assist Feature Generation With Supervised Data Learning

Xu, Xiaoqing; Lin, Yibo; Li, Meng; Matsunawa, Tetsuaki; Nojima, Shigeki; Kodama, Chikaaki; Kotani, Takayuki; Pan, David Z.

doi:10.1109/tcad.2017.2748029

Cited by 20 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GAN-SRAF Sub-resolution assist feature generation is a key RET to improve the target pattern quality and lithographic process window. These assist features are not actually printed; instead, the SRAF patterns would deliver light to the positions of target patterns at proper phase which can improve the robustness of target printing to lithographic variations [51].…”

Section: B Tempomentioning

confidence: 99%

“…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%

“…Xu et al [51] introduced machine learning to tackle the problem of SRAF insertion more efficiently [2], [51]. The proposed method relies on SRAF features extraction with local sampling scheme to obtain the optimal SRAF map.…”

Section: B Tempomentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

Section: B Tempomentioning

confidence: 99%

Section: B Tempomentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

“…Recent years have seen an increasing employment of machine learning (ML) that target both front-end [1][2][3][4] and back-end [5][6][7][8][9] design tools. For example, Xu [5] proposed a supervised learning based sub-resolution assist feature (SRAF) generator that is used to improve yield in the manufacturing process. Hotspot detection Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Painting on Placement

Zhang

2019

Proceedings of the 56th Annual Design Automation Conference 2019

View full text Add to dashboard Cite

Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step. Demands for accurate routing quality prediction raise to a new level to accelerate hardware innovation with advanced technology nodes. This work presents an approach that forecasts the density of all routing channels over the entire floorplan, with features collected up to placement, using conditional GANs. Specifically, forecasting the routing congestion is constructed as an image translation (colorization) problem. The proposed approach is applied to a) placement exploration for minimum congestion, b) constrained placement exploration and c) forecasting congestion in real-time during incremental placement, using eight designs targeting a fixed FPGA architecture.

show abstract

“…Ma et al proposed fast mask optimization algorithms based on non-parametric kernel regression, which can effectively improve the computational efficiency and mask manufacturability [14] . Xu et al proposed a fast SRAF generation method that involved support vector machines (SVM) and logistic regression models in the complete mask optimization process [15] . K. Luo et al and R. Luo et al respectively proposed fast mask optimization methods based on SVMs [16] and multilayer perceptual neural networks [17] .…”

Section: Introductionmentioning

confidence: 99%

Study of Inverse Lithography Approaches based on Deep Learning

Zhang¹,

Ma²,

Zhang³

et al. 2019

Journal of Microelectronic Manufacturing

View full text Add to dashboard Cite

Computational lithography (CL) has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography. The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom. However, as the growth of data volume of photomask layouts, computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms. In the past, a number of innovative methods have been developed to improve the computational efficiency of CL algorithms, such as machine learning and deep learning methods. Based on the brief introduction of optical lithography, this paper reviews some recent advances of fast CL approaches based on deep learning. At the end, this paper briefly discusses some potential developments in future work.

show abstract

Subresolution Assist Feature Generation With Supervised Data Learning

Cited by 20 publications

References 20 publications

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

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

Painting on Placement

Study of Inverse Lithography Approaches based on Deep Learning

Contact Info

Product

Resources

About