Automatic correction of lithography hotspots with a deep generative model

Sim, Woo Seog; Lee, Kibok; Yang, Dingdong; Jeong, Jaeeun; Hong, Ji-Suk; Lee, Sooryong; Lee, Honglak

doi:10.1117/12.2514884

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Automatic correction of hotspot using cycleGAN (cycleconsistent GAN [37]) has been proposed [38]. A set of sample hotspot patterns is X, and a set of coldspot patterns is Y as illustrated in Fig.…”

Section: For Hotspot Detection and Correctionmentioning

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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Section: For Hotspot Detection and Correctionmentioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

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“…Deep learning has been used to solve a series of problems in computational lithography, for example the defect characterization and classification of masks based on convolutional neural networks [19] , and hotspots correction based on the cycle-consistent generative adversarial network [20] . 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] .…”

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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“…Furthermore, while recent works have focused on largescale settings where millions of unlabeled data is available, it would not be practical in real-world applications. For example, in lithography, acquiring data is very expensive in terms of both time and cost due to the complexity of manufacturing process (Lin et al, 2018;Sim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Lee,

Zhu,

Sohn

et al. 2020

Preprint

Self Cite

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Contrastive representation learning has shown to be an effective way of learning representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective regularization strategy for improving contrastive representation learning in both vision and nonvision domains. We cast contrastive learning as training a non-parametric classifier by assigning a unique virtual class to each data in a batch. Then, data instances are mixed in both the input and virtual label spaces, providing more augmented data during training. In experiments, we demonstrate that i-Mix consistently improves the quality of self-supervised representations across domains, resulting in significant performance gains on downstream tasks. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes.

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Automatic correction of lithography hotspots with a deep generative model

Cited by 6 publications

References 4 publications

Computational Lithography Using Machine Learning Models

Computational Lithography Using Machine Learning Models

Study of Inverse Lithography Approaches based on Deep Learning

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

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