MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning

• URL: http://arxiv.org/abs/2605.12528v1
• Date: Mon, 13 Apr 2026 14:40:34 GMT
• Title: MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning
• Abstract summary: Optical proximity correction (OPC) is widely used to ensure pattern fidelity and manufacturability.<n>Recent generative mask optimization models can synthesize near-optimal masks, serving as fast machine learning (ML) surrogates for traditional OPC.<n>We propose textitMorphOPC, a multi-scale hierarchical model with neural morphological modules to learn these transformations.
• Score: 11.329737398904875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly challenging. Optical proximity correction (OPC) is widely used to ensure pattern fidelity and manufacturability. Recent generative mask optimization models based on encoder-decoder architecture can synthesize near-optimal masks, serving as fast machine learning (ML) surrogates for traditional OPC. However, these models often fail to capture the geometric transformations from target layouts to mask patterns, leading to suboptimal quality. In this work, we formulate mask generation as a sequence of morphological operations on local layout features and propose \textit{MorphOPC}, a multi-scale hierarchical model with neural morphological modules to learn these transformations. Experiments on edge-based OPC and ILT benchmarks across metal and via layers show that \textit{MorphOPC} consistently outperforms state-of-the-art methods, achieving higher printing fidelity and lower manufacturing cost, demonstrating strong potential for scalable mask optimization.

Related papers

• LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching [51.14153938143746]
  Inverse Lithography Technology (ILT) generates optimized masks that enhance the fidelity of pattern transfer onto wafers.<n>We introduce LithoGRPO, an ILT framework that integrates the flow-matching paradigm with GRPO-based reinforcement learning (RL) fine-tuning.<n>To improve RL sampling efficiency, we propose a fast shot-counting algorithm for manufacturability evaluation, achieving over 130x speedup.
  arXiv  Detail & Related papers  (2026-05-29T18:01:59Z)
• MaskOpt: A Large-Scale Mask Optimization Dataset to Advance AI in Integrated Circuit Manufacturing [9.945697214043385]
  We present MaskOpt, a large-scale benchmark dataset constructed from real IC designs at the 45$mathrmnm$ node.<n>We evaluate state-of-the-art deep learning models for IC mask optimization to build up benchmarks.
  arXiv  Detail & Related papers  (2025-12-18T05:53:45Z)
• Towards Efficient General Feature Prediction in Masked Skeleton Modeling [59.46799426434277]
  We propose a novel General Feature Prediction framework (GFP) for efficient mask skeleton modeling.<n>Our key innovation is replacing conventional low-level reconstruction with high-level feature prediction that spans from local motion patterns to global semantic representations.
  arXiv  Detail & Related papers  (2025-09-03T18:05:02Z)
• Polyline Path Masked Attention for Vision Transformer [52.90241449955985]
  Vision Transformers (ViTs) have achieved remarkable success in computer vision.<n>Mamba2 has demonstrated its significant potential in natural language processing tasks.<n>We propose Polyline Path Masked Attention (PPMA) that integrates the self-attention mechanism of ViTs with an enhanced structured mask of Mamba2.
  arXiv  Detail & Related papers  (2025-06-19T00:52:30Z)
• LAMM-ViT: AI Face Detection via Layer-Aware Modulation of Region-Guided Attention [4.0810988694972385]
  We introduce Layer-aware Mask Modulation Vision Transformer (LAMM-ViT), a Vision Transformer designed for robust facial forgery detection.<n>LAMM-ViT integrates Region-Guided Multi-Head Attention (RG-MHA) and Layer-aware Mask Modulation (LAMM) components within each layer.<n>In cross-model generalization tests, LAMM-ViT demonstrates superior performance, achieving 94.09% mean ACC and 98.62% mean AP.
  arXiv  Detail & Related papers  (2025-05-12T16:42:19Z)
• Fast inverse lithography based on a model-driven block stacking convolutional neural network [10.170465557304897]
  This paper presents a novel inverse lithographic approach to Optical Proximity Correction (OPC)<n>It employs a model-driven, block stacking deep learning framework that expedites the generation of masks conducive to manufacturing.<n> Numerical experiments have substantiated the efficacy of the proposed end-to-end approach.
  arXiv  Detail & Related papers  (2024-12-19T07:42:07Z)
• Cross-Scan Mamba with Masked Training for Robust Spectral Imaging [51.557804095896174]
  We propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding.<n>Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns [13.03056139396295]
  Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. We propose a self-adaptive OPC framework to improve efficiency. Experimental results show that our framework achieves substantial improvement in both performance and efficiency.
  arXiv  Detail & Related papers  (2023-03-15T13:40:37Z)
• Realistic mask generation for matter-wave lithography via machine learning [0.0]
  We present a machine learning approach to mask generation targeted for metastable atoms. A novel deep neural architecture is trained to produce an initial approximation of the mask. This approximation is then used to generate the initial population of the genetic optimisation algorithm that can converge to arbitrary precision.
  arXiv  Detail & Related papers  (2022-07-15T09:14:03Z)
• Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training [54.16367467777526]
  We present a Convolutional Neural Operator (CFCF) that can efficiently learn mask tasks. For the first time, our machine learning-based framework outperforms state-of-the-art numerical mask dataset.
  arXiv  Detail & Related papers  (2022-07-08T16:39:31Z)
• Progressive Encoding for Neural Optimization [92.55503085245304]
  We show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations.
  arXiv  Detail & Related papers  (2021-04-19T08:22:55Z)
• LevelSet R-CNN: A Deep Variational Method for Instance Segmentation [79.20048372891935]
  Currently, many state of the art models are based on the Mask R-CNN framework. We propose LevelSet R-CNN, which combines the best of both worlds by obtaining powerful feature representations. We demonstrate the effectiveness of our approach on COCO and Cityscapes datasets.
  arXiv  Detail & Related papers  (2020-07-30T17:52:18Z)

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.