Optimal feature vector design for computational lithography

Shi, Xuelong; Zhao, Yuhang; Cheng, Shoumian; Li, Ming; Yuan, Wei; Yao, Lihang; Zhao, Wenzhi; Xiao, Yanjun; Kang, Xiaohui; Li, Angmar

doi:10.1117/12.2515446

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Lan et al proposed a new technique to apply deep neural networks in GPU-accelerated mask optimization platform, which provided a fast and accurate ILT solution for 10nm and below technology nodes [21] . Shi et al proposed an optimal feature vector automatic design method based on convolution neural network (CNN), which greatly improved the computational efficiency of ILT [22] . Chen et al used Auto Pattern Selection (APS) tool to train the Newron SRAF deep learning network and successfully realized the inverse mask optimization on full-chip layout [23] .…”

Section: Ilt Based On Standard Deep Learningmentioning

confidence: 99%

Study of Inverse Lithography Approaches based on Deep Learning

Zhang¹,

Ma²,

Zhang³

et al. 2019

Journal of Microelectronic Manufacturing

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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.

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Section: Ilt Based On Standard Deep Learningmentioning

confidence: 99%

Study of Inverse Lithography Approaches based on Deep Learning

Zhang¹,

Ma²,

Zhang³

et al. 2019

Journal of Microelectronic Manufacturing

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“… – 9 For machine learning OPC, the majority of the researches have been focused on designing some scheme that can measure the neighboring environment of a segment, then using the constructed feature vector from those measurements to predict the OPC amount of the segment through a trained neural network model 10 – 13 Such an approach may be adequate for hole layers, for which the types of rectangles (x dimension and y dimension) are very limited, consequently, the segmentation rules and control point setting rules are very limited. However, for line/space layers, such as metal layers, the situation becomes much more complicated.…”

Section: Introductionmentioning

confidence: 99%

Can optical proximity correction solution be learned? The learning limit and a general learning framework

Shi

Yan

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: Optical proximity correction (OPC) is an indispensable technology that has been propelling the advancement of computational lithography technology. To tightly control edge placement error (EPE) and maintain lithography process window, the demands on OPC computational resources and OPC turnaround time are growing rapidly with alarming acceleration. To tame the trend, machine learning technologies have been explored; however, an in-depth discussion on OPC solution learning limit is still lacking.Aim: We aim to present an in-depth discussion on OPC solution learning limit and propose a general machine learning OPC framework that can be extended to curvilinear mask OPC technology.Approach: In this study, we first investigate the machine learning OPC learning limit by examining noninverse lithography technology (non-ILT) OPC solution space characteristics inherited from edge segmentation and control point setting rules and then propose a general machine learning OPC framework that can take full advantage of deep convolution neural network (DCNN) learning capability while being able to preserve mask data high resolution.Results: With this machine learning OPC framework, we have achieved models with average absolute model error <1 nm for 14-nm metal layer. With single GPU, the average time for machine learning OPC models to produce results of 3840 nm × 3840 nm area is 8.74 ms for single channel input model and 12.65 ms for six channels input model. Conclusions:For non-ILT OPC solution, there is an intrinsic learning limit inherited from edge segmentation rules. Machine learning OPC models should be content with learning low order OPC solutions. This intrinsic learning limit of non-ILT OPC solution may diminish for ILT OPC solution when the constraint on degrees of freedom of OPC solution is lifted. The machine learning OPC framework we proposed is general and extendable to curvilinear OPC technology.

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Machine Learning Hotspot Prediction Significantly Improve Capture Rate on Wafer

Yuan

Lü

et al. 2020

2020 International Workshop on Advanced Patterning Solutions (IWAPS)

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Optimal feature vector design for computational lithography

Cited by 6 publications

References 8 publications

Study of Inverse Lithography Approaches based on Deep Learning

Study of Inverse Lithography Approaches based on Deep Learning

Can optical proximity correction solution be learned? The learning limit and a general learning framework

Machine Learning Hotspot Prediction Significantly Improve Capture Rate on Wafer

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