GAN-OPC: Mask Optimization with Lithography-guided Generative Adversarial Nets

Yang, Haoyu; Li, Shuhe; Ma, Yuzhe; Yu, Bei; Young, Evangeline F. Y.

doi:10.1109/dac.2018.8465816

Cited by 41 publications

(59 citation statements)

References 13 publications

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“…Recently, GANs [10] have shown promising potential in many applications, including image processing, computer vision, and design for manufacturability, etc. [11,12]. A conventional GAN architecture is diagrammed in Fig.…”

Section: Gan-based Well Guidance Creationmentioning

confidence: 99%

WellGAN

Lin

Tang

et al. 2019

Proceedings of the 56th Annual Design Automation Conference 2019

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In back-end analog/mixed-signal (AMS) design flow, well generation persists as a fundamental challenge for layout compactness, routing complexity, circuit performance and robustness. The immaturity of AMS layout automation tools comes to a large extent from the difficulty in comprehending and incorporating designer expertise. To mimic the behavior of experienced designers in well generation, we propose a generative adversarial network (GAN) guided well generation framework with a post-refinement stage leveraging the previous high-quality manually-crafted layouts. Guiding regions for wells are first created by a trained GAN model, after which the well generation results are legalized through post-refinement to satisfy design rules. Experimental results show that the proposed technique is able to generate wells close to manual designs with comparable post-layout circuit performance.

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Section: Gan-based Well Guidance Creationmentioning

confidence: 99%

WellGAN

Lin

Tang

et al. 2019

Proceedings of the 56th Annual Design Automation Conference 2019

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“…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] . As examples, this section will detail two ILT methods based on variational autoencoder (VAE) [24] and generative adversarial network (GAN) [25] .…”

Section: Ilt Based On Standard Deep Learningmentioning

confidence: 99%

“…The (a) workflow and the (b) simulation results of the proposed GAN-OPC method (adopted from Figs. 6 and 8 in Ref [25]…”

mentioning

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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“…VLSI layout patterns are significant resources for flows of various design for manufacturability (DFM) research, such as optical proximity correction (OPC) [7,10,13,15,26], layout hotspot detection [5,27,30,33,34], pattern matching [4], lithography simulation [16,17,31] and so on. However, VLSI layout pattern libraries are very often not available for research or testing due to the long and iterative technology life cycle, especially in the initial stage, which may slow down the technology node development [10].…”

Section: Introductionmentioning

confidence: 99%