GPU-accelerated Matrix Cover Algorithm for Multiple Patterning Layout Decomposition

• URL: http://arxiv.org/abs/2303.14335v1
• Date: Sat, 25 Mar 2023 02:51:12 GMT
• Title: GPU-accelerated Matrix Cover Algorithm for Multiple Patterning Layout Decomposition
• Authors: Guojin Chen, Haoyu Yang, Bei Yu
• Abstract summary: Multiple patterning lithography (MPLD) technology is becoming increasingly crucial for improving the manufacturability in advanced nodes. In this research, we substitute the CPU's dance link data structure with parallel GPU matrix operations to accelerate the solution for exact cover-based MPLD algorithms.
• Score: 8.528609848514511
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology. As the feature size continues to decrease, layout decomposition for multiple patterning lithography (MPLD) technology is becoming increasingly crucial for improving the manufacturability in advanced nodes. The decomposition process refers to assigning the layout features to different mask layers according to the design rules and density requirements. When the number of masks $k \geq 3$, the MPLD problems are NP-hard and thus may suffer from runtime overhead for practical designs. However, the number of layout patterns is increasing exponentially in industrial layouts, which hinders the runtime performance of MPLD models. In this research, we substitute the CPU's dance link data structure with parallel GPU matrix operations to accelerate the solution for exact cover-based MPLD algorithms. Experimental results demonstrate that our system is capable of full-scale, lightning-fast layout decomposition, which can achieve more than 10$\times$ speed-up without quality degradation compared to state-of-the-art layout decomposition methods.

Related papers

• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [73.49548565633123]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images. We propose a view framework based on 3D Gaussian Splatting, named MCGS, enabling scene reconstruction from sparse input views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• Q-VLM: Post-training Quantization for Large Vision-Language Models [73.19871905102545]
  We propose a post-training quantization framework of large vision-language models (LVLMs) for efficient multi-modal inference. We mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy. Experimental results demonstrate that our method compresses the memory by 2.78x and increase generate speed by 1.44x about 13B LLaVA model without performance degradation.
  arXiv  Detail & Related papers  (2024-10-10T17:02:48Z)
• Inverse Lithography Physics-informed Deep Neural Level Set for Mask Optimization [0.8547032097715571]
  Level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution. Deep learning (DL) methods have shown great potential in accelerating ILT. We propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization.
  arXiv  Detail & Related papers  (2023-08-15T01:56:22Z)
• Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening [13.593522290577512]
  Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency.
  arXiv  Detail & Related papers  (2023-04-28T03:34:36Z)
• Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm [11.366935475887239]
  We model the layout decomposition of MPL as a generalized graph coloring problem. DEA-PPM can strike a balance between decomposition results and running time.
  arXiv  Detail & Related papers  (2023-04-09T10:23:30Z)
• LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models [50.73105631853759]
  We present a novel generative model named LayoutDiffusion for automatic layout generation. It learns to reverse a mild forward process, in which layouts become increasingly chaotic with the growth of forward steps. It enables two conditional layout generation tasks in a plug-and-play manner without re-training and achieves better performance than existing methods.
  arXiv  Detail & Related papers  (2023-03-21T04:41:02Z)
• DevelSet: Deep Neural Level Set for Instant Mask Optimization [11.847061281805463]
  inverse lithography technique (ILT) has drawn significant attention and is becoming prevalent in emerging OPC solutions. In this paper, we present DevelSet, a GPU and deep neural network (DNN) accelerated level set OPC framework for metal layer.
  arXiv  Detail & Related papers  (2023-03-18T13:48:53Z)
• Progressively-connected Light Field Network for Efficient View Synthesis [69.29043048775802]
  We present a Progressively-connected Light Field network (ProLiF) for the novel view synthesis of complex forward-facing scenes. ProLiF encodes a 4D light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses.
  arXiv  Detail & Related papers  (2022-07-10T13:47:20Z)
• A modular software framework for the design and implementation of ptychography algorithms [55.41644538483948]
  We present SciCom, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art reconstruction algorithms. Despite its simplicity, the software leverages accelerated processing through the PyTorch interface. Results are shown on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2022-05-06T16:32:37Z)
• Multi-Grid Back-Projection Networks [18.291563524631986]
  Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. We propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output.
  arXiv  Detail & Related papers  (2021-01-01T03:17:34Z)
• Semantic Layout Manipulation with High-Resolution Sparse Attention [106.59650698907953]
  We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. We propose a high-resolution sparse attention module that effectively transfers visual details to new layouts at a resolution up to 512x512.
  arXiv  Detail & Related papers  (2020-12-14T06:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.