Machine learning optical proximity correction with generative adversarial networks

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

doi:10.1117/1.JMM.21.4.041606

23 November 2022 Machine learning optical proximity correction with generative adversarial networks

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

Journal of Micro/Nanopatterning, Materials, and Metrology, Vol. 21, Issue 4, 041606 (November 2022). https://doi.org/10.1117/1.JMM.21.4.041606

Abstract

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 after-development-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.

Citation Download Citation

Weilun Ciou, Tony Hu, Yi-Yen Tsai, Chung-Te Hsuan, Elvis Yang, Ta-Hung Yang, and Kuang-Chao Chen "Machine learning optical proximity correction with generative adversarial networks," Journal of Micro/Nanopatterning, Materials, and Metrology 21(4), 041606 (23 November 2022). https://doi.org/10.1117/1.JMM.21.4.041606

Received: 25 May 2022; Accepted: 2 November 2022; Published: 23 November 2022

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KEYWORDS

Photomasks

Education and training

Optical proximity correction

Data modeling

Networks

Gallium nitride

Machine learning

Background

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