Optical Proximity Correction Flow Research Articles

Neural-ILT 2.0: Migrating ILT to Domain-Specific and Multitask-Enabled Neural Network

Optical proximity correction (OPC) in modern design closures has become extremely expensive and challenging. Conventional model-based OPC encounters performance degradation and large process variation, while aggressive approach, such as inverse lithography technology (ILT), suffers from large computational overhead for both mask optimization and mask writing processes. In this article, we developed Neural-ILT, an end-to-end learning-based OPC framework, which literally conducts mask prediction and ILT correction for a given layout in a single neural network, with the objectives of: 1) mask printability enhancement; 2) mask complexity optimization; and 3) flow acceleration. A domain-specific model pretraining recipe, which introduces the domain knowledge of lithography system, is proposed to help Neural-ILT achieving faster and better convergence. Quantitative results show that compared to the state-of-the-art (SOTA) learning-based OPC solutions and conventional OPC flows, Neural-ILT can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> turnaround time (TAT) speedup and the best mask printability with relatively lower mask complexity. Based on the developed infrastructure, we further investigated the feasibility of handling multiple mask optimization tasks for different datasets within a common Neural-ILT platform. We believe this work could bridge well-developed deep learning toolkits to GPU-based high-performance lithographic computations to achieve groundbreaking performance boosting on various computational lithography-related tasks.

I-line photolithographic metalenses enabled by distributed optical proximity correction with a deep-learning model.

High pattern fidelity is paramount to the performance of metalenses and metasurfaces, but is difficult to achieve using economic photolithography technologies due to low resolutions and limited process windows of diverse subwavelength structures. These hurdles can be overcome by photomask sizing or reshaping, also known as optical proximity correction (OPC). However, the lithographic simulators critical to model-based OPC require precise calibration and have not yet been specifically developed for metasurface patterning. Here, we demonstrate an accurate lithographic model based on Hopkin's image formulation and fully convolutional networks (FCN) to control the critical dimension (CD) patterning of a near-infrared (NIR) metalens through a distributed OPC flow using i-line photolithography. The lithographic model achieves an average ΔCD/CD = 1.69% due to process variations. The model-based OPC successfully produces the 260 nm CD in a metalens layout, which corresponds to a lithographic constant k1 of 0.46 and is primarily limited by the resolution of the photoresist. Consequently, our fabricated NIR metalens with a diameter of 1.5 mm and numerical aperture (NA) of 0.45 achieves a measured focusing efficiency of 64%, which is close to the calculated value of 69% and among the highest reported values using i-line photolithography.

LRC Based OPC Recipe Qualification

Typical IC layouts are simple shapes such as rectangles and 45 degree triangles. However, the high-spatial-frequency components of the mask spectrum cut off by the low-pass pupil will result in the rounded images and pattern distortions. Although optical proximity correction (OPC) has been proposed to address this issue as a standard industry practice, there are no theoretically perfect corrections due to some physical or chemical limitations. Therefore, how to qualify the correction results has become a problem. In this paper, the lithography rule check (LRC) based OPC recipe qualification is proposed. By extracting and grouping the LRC databases, the point-to-point post-OPC comparison matrix is shown. Finally, this new methodology can be integrated into the regression flow. When changing to a new software version or different OPC flow, this new validation methodology can not only tell the differences, but also report if there are any OPC quality degradations.