GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial Nets

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

doi:10.1109/tcad.2019.2939329

Cited by 52 publications

(36 citation statements)

References 28 publications

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“…In early examples, multilayer perceptron networks (MLPs) and CNNs were used to predict optimal mask layouts given the desired exposure pattern. Generative neural networks serving as an inverse design tool for lithography have also been trained in a generative adversarial network framework to produce candidate quasi-optimal masks for given target patterns (Figure b). Concepts from OPC have been readily extended to the modeling of etching errors, where CNNs have been used to accurately model the etching masks for given target patte profiles of nanoscale features (Figure c).…”

Section: Fabrication Of Freeform Devicesmentioning

confidence: 99%

Algorithm-Driven Paradigms for Freeform Optical Engineering

2022

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Advances in modern manufacturing have enabled the multiscalar patterning of dielectric media with nearly arbitrary layouts, presenting unique opportunities to revolutionize the design and fabrication pipeline for photonic technologies. In this Perspective, we discuss how algorithms based on classical optimization and deep learning are establishing a new conceptual framework for freeform optical engineering. These tools can specify suitable design parameters for a desired objective, automate the high-speed optimization of freeform devices, and augment manufacturing processes to mitigate challenges set by freeform fabrication. A central feature of many of these algorithms is their utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. We anticipate that these algorithm-driven methods will streamline optical systems design at the physical limits of structured media and become standard academic and industry tools for scientists and engineers.

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Section: Fabrication Of Freeform Devicesmentioning

confidence: 99%

Algorithm-Driven Paradigms for Freeform Optical Engineering

2022

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“…Generative learning has been applied to the computational lithography nowadays in different disciplines 18 . Except OPC generation 19 some study also propose generating assist features with GAN method. Mohamed proposes a sub resolution assist feature (SRAF) insertion framework, GAN-SRAF, which uses conditional generative adversarial networks (CGANs) to generate SRAFs directly 20 .…”

Section: Introductionmentioning

confidence: 99%

Curvilinear mask optimization with refined generative adversarial nets

Cao

Xu²,

Sun³

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

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.Inverse lithography technology (ILT) can optimize the mask to gain the best process window and image quality when the design dimension shrinks. However, as a pixel level correction method, ILT is very time-consuming. In order to make the ILT method useful in real mask fabrication, the runtime of ILT-based optical proximity correction mask must evidently decrease while keeping the good lithographic metric performance. Our study proposes a framework to obtain the curvilinear ILT mask with generative adversarial network (GAN). It is subsequently refined with the traditional ILT to exclude unexpected outliers generated by the GAN method. We design conditional GAN, reverse GAN (RGAN), and high discretion GAN (HDGAN) to generate curvilinear ILT mask. Their runtime and the performance are compared. Compared with the CILT method, the speed of GAN type methods with the afterward refinement is increased by an order of magnitude. The RGAN has a better performance in edge placement error and process variation band evaluation, and HDGAN has a better performance in the mask error enhancement factor evaluation. The designed RGAN and HDGAN are promising in actual application to generate the curvilinear mask. They can evidently decrease the runtime and have better lithographic metric performance.

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“… – 3 Another mainstream was mask pattern generation by utilizing U-Net architecture or generative adversarial networks (GANs) 4 – 6 Generally, the latter approach produces curvilinear mask patterns that best mimic the ILT technique, but critical dimension (CD) accuracy in practical production are less investigated.…”

Section: Introductionmentioning

confidence: 99%

Machine learning optical proximity correction with generative adversarial networks

Ciou

Tsai

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: Algorithmic breakthroughs in machine learning (ML) have allowed increasingly more applications developed for computational lithography, gradually shifting focus from hotspot detection to inverse lithography and optical proximity correction (OPC). We proposed a pixelated mask synthesis method utilizing deep-learning techniques, to generate afterdevelopment-inspection (ADI) contour and mask feature generation.Aim: Conventional OPC correction consists of two parts, the simulation model which predicts the expected contour signal, and the correction script that modifies the actual layout. With practicality in mind, we collected modeling wafer data from scratch, then implemented ML models to reproduce conventional OPC actions, mask to contour prediction, and design to mask correction.Approach: Two generative adversarial networks (GANs) were constructed, a pix2pix model was first trained to learn the correspondences between mask image and paired ADI contour image collected on wafer. The second model is embedded into machine learning mask correction (ML-OPC) framework, output mask is optimized through minimizing pixel difference between design target and simulated contour.Results: Two different magnification SEM image datasets were collected and studied, with the higher magnification showing better simulator pixel accuracy. Supervised training of the correction model provided a quick prototype mask synthesis generator, and combination of unsupervised training allowed mask pattern synthetization from any given design layout. Conclusions:The experimental results demonstrated that our ML-OPC framework was able to mimic conventional OPC model in producing exquisite mask patterns and contours. This ML-OPC framework could be implemented across full chip layout.

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GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial Nets

Cited by 52 publications

References 28 publications

Algorithm-Driven Paradigms for Freeform Optical Engineering

Algorithm-Driven Paradigms for Freeform Optical Engineering

Curvilinear mask optimization with refined generative adversarial nets

Machine learning optical proximity correction with generative adversarial networks

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