Fast OPC repair flow based on machine learning

Shi, Bibo; Deng, Rock; Shu, Zhongli; Zhu, Yu; Tu, Yuanying; Sun, Cheng

doi:10.1117/12.2552731

Cited by 1 publication

(1 citation statement)

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“…However, the complex nature of matrix OPC had drawbacks of extreme long correction process. With algorithmic breakthroughs, machine-learning techniques became increasingly more common in chip manufacturing, such as this study on machine learning hotspot correction [4], a method that fragmented layout data and computed segment displacement with machine learning model. The results of the study revealed that ML application could save tape-out runtime, but some remaining defects still need fixing by traditional OPC methods.…”

Section: Introductionmentioning

confidence: 99%

Lithography hotspot correction on post-OPC layout using generative adversarial networks

Ciou

Cheng

et al. 2023

DTCO and Computational Patterning II

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Suboptimal layout geometries after optical proximity correction (OPC) might induce lithography hotspot, and result in degradation of wafer yield during integrated circuit (IC) manufacturing. Conventional hotspot correction methods have been widely conducted on post-OPC layout, such as rule-based or model-based hotspot fixing, but these methods might not completely solve hotspot issues due to the time-consuming process or model inaccuracy. Over the past of few years, the explosive growth of machine learning techniques has boosted the capability of computational lithography including hotspot detection and correction. In this paper, we focus on lithography hotspot correction with Generative Adversarial Network (GAN) to modify pattern shapes of hotspot and further improve lithographic printing of designed layout. The proposed approach first built a hotspot correction model based on different types of lithography rule check (LRC) hotspots, by training a pix2pix model to learn the correspondences between paired post-OPC layout image and after development inspection (ADI) contour image simulated from LRC tool. Then, we input hotspot-free contour image created from original hotspot into the deep learning model to generate supposedly hotspot-free mask image, and converted the mask image back into polygonal layout. Finally, mask layout with hotspot were partially replaced with predicted mask layout, and then examined with LRC simulation. Furthermore, we also implemented transfer learning for new hotspots captured from new design layout to expand the capability of our hotspot correction flow. Experimental results showed that this methodology successfully corrected lithography hotspots and significantly enhanced the efficiency of hotspot correction.

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Section: Introductionmentioning

confidence: 99%