Sub-resolution assist feature placement with generative adversarial network

Ciou, Weilun; Hu, Tony; Tsai, Yi-Yen; Hsuan, Chung-Te; Yang, Elvis; Yang, Tahone; Chen, Kuang-Chao

doi:10.1117/1.jmm.20.4.041209

Cited by 2 publications

(4 citation statements)

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“…Neural network models have been applied in several areas of optical systems and computational lithography in DUV and extreme ultraviolet lithography (EUVL), such as forward aberration measurement, wavefront sensing, wavefront reconstruction, sub-resolution-assist feature placement, source optimization, controlling aberration in the optical design stage. The application of neural networks is very helpful for the design of optical systems and the improvement of lithography performance [13][14][15][16][17]. Backward propagation (BP) neural used in this work is a multilayer feedforward neural network, which is also a scientific way to represent the relationship between multiple factors and responded variables.…”

Section: A Network Prediction Model For Asymmetric Spectral Effectsmentioning

confidence: 99%

The influence of asymmetric spectrum of DUV light on imaging performance in advanced technology nodes

Zhu,

Dong,

Wei

2024

Optical and EUV Nanolithography XXXVII

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In the manufacturing of advanced integrated circuits, ArF immersion lithography remains the primary tool for the patterning process. With the increasing requirements for critical dimension uniformity (CDU) in advanced technology nodes, every nanometer of CD variation becomes crucial. Over the past decades, scanner manufacturers have continuously adopted many technologies to improve the performance of DUV light sources, aiming to reduce CD variation caused by bandwidth, central wavelength, and other factors as much as possible. Previous investigations into the impacts of source bandwidth on imaging performance have provided the foundation for this work. In previous simulations of limited light bandwidth, Gaussian and Lorentzian symmetric line shapes were often used to represent the laser spectrum. However, the emitted beam from excimer lasers always exhibits some level of asymmetry in reality. This study focuses on the impact of spectral asymmetry on imaging performance. We conducted researches on the effects of various laser bandwidth shapes on the imaging of the critical patterns such as line space and other 2D patterns. We introduced an asymmetry parameter Δn to characterize the spectrum change. In the modeling part of this study, backward propagation (BP) neural network was established to predict the effects of a certain range of asymmetric spectra. We conclude that although the laser bandwidth in modern scanners is relatively low, the asymmetry of the laser spectrum does have certain impacts on CD variations depending on the mask pattern.

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Section: A Network Prediction Model For Asymmetric Spectral Effectsmentioning

confidence: 99%

The influence of asymmetric spectrum of DUV light on imaging performance in advanced technology nodes

Zhu,

Dong,

Wei

2024

Optical and EUV Nanolithography XXXVII

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“…To allow application of ML-OPC to arbitrary sized layout, the stitch approach 7 shown in Fig. 4 was utilized.…”

Section: Ml-opc Infrastructurementioning

confidence: 99%

“…8. Decoder units upsample the input feature maps by utilizing pixel-shuffle mechanism, 7 transferring channel elements of feature maps to spatial elements. Previous studies have discovered that instance normalization provided better performance for style transfer tasks, 11 and thus, instance normalization was implemented in our model rather than batch normalization of the original pix2pix model.…”

Section: Gan Training Algorithmsmentioning

confidence: 99%

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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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Sub-resolution assist feature placement with generative adversarial network

Cited by 2 publications

References 0 publications

The influence of asymmetric spectrum of DUV light on imaging performance in advanced technology nodes

The influence of asymmetric spectrum of DUV light on imaging performance in advanced technology nodes

Machine learning optical proximity correction with generative adversarial networks

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