Advancements in Photolithography Techniques

Latest papers

• VPEvolve: A Self-Evolving Virtual Process Engineer for Computational Lithography

  Tianyi Li, Wenxuan Dong, Donger Luo, Nan Wang, Yanpeng Chen, Jiaqi Liu, Xinyun Zhang, Hao Geng · 29 September 2026

  Optical proximity correction (OPC) recipes grow as engineers add local rules to repair newly discovered lithography hotspots. Each correction can interact with existing rules, while lessons from commercial-tool trials remain scattered across code and logs. \system combines a Virtual Process Engineer…

• LensDesigner: A Self-Improving Agent for Optical Lens Design

  Lei Sun, Haoran Liang, Dannong Xu, Yao Gao, Yuyu Geng, Jinjin Gu, Kaiwei Wang, Danda Pani Paudel, Luc Van Gool · 28 September 2026

  Optical lens design is a complex, non-convex optimization challenge that relies heavily on human experience and intuition. Existing optimized-based automatic lens design methods struggle to navigate this vast parameter space without meticulous manual tuning. In this paper, we present LensDesigner, a…

• A Two-Mirror Faceted Projection System for EUV Lithography

  Vasiliy A. Es'kin, Egor V. Ivanov, Olga V. Martynova · 11 September 2026

  We propose an all-reflective two-mirror projection system for extreme ultraviolet (EUV) lithography operating at exposure wavelengths of $13.5$~nm (Mo/Si) and $11.2$~nm (Ru/Be), delivering a fourfold ($4\times$) demagnification of the periodic mask pattern at a numerical aperture approaching unity (…

• LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds

  Huanglong Ji, Botong Zhao, Shujing Lv, Yue Lv · 5 August 2026

  Multimodal large language models have demonstrated strong defect recognition capability in industrial anomaly detection. However, in lithography review, merely determining whether an image contains a defect is insufficient for engineering inspection; models must also understand defect morphology, sp…

• DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections

  Haebin Shin, Lei Ji, Xiao Liu, Zhiwei Yu, Hyunwoo Yoo, Qi Chen, Yeyun Gong · 7 July 2026

  As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a critical challenge. To address this, we propose DynamixSFT, a dynamic and automated method for instruction-tuning dataset mixture optimization. We formulate the problem as a m…

• LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography

  Yuqi Jiang, Yumeng Liu, Zimu Li, Jinyuan Deng, Qian Jin, Yucheng Cui, Yu Li, Xunzhao Yin, Qi Sun, Cheng Zhuo · 26 June 2026

  As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance. However, lithography is a continuous physical process involving mask optimization, optical imaging, resist exposure, and development, which existing models fail to capture. To overcome…

• Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator

  Vasiliy A. Es'kin, Egor V. Ivanov · 25 June 2026

  Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable waveguide method and the recently proposed waveguide neural operator~(WGNO) as end-to-end physics engines, recovering the permittivity of the absorber of …

• Do Value Vectors in Deep Layers Need Context from the Residual Stream?

  Muyu He, Yuchen Liu, Qingya Huang, Li Zhang · 3 June 2026

  The success of the transformer architecture as the backbone of modern LLMs is in large part due to its use of attention layers. An attention layer follows the standard neural network paradigm: it takes the residual stream as input and thereby produces context-dependent query, key, and value vectors.…

• LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching

  Yao Lai, Xuyuan Xiong, Zeyue Xue, Guojin Chen, Jing Wang, Xihui Liu, Rui Zhang, Robert Mullins, Bei Yu, Ping Luo · 2 June 2026

  In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the wavelength of light, optical diffraction causes the printed patterns to deviate from their intended layouts. Inverse Lithography Technology (ILT) add…

• Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization

  Yusuke Ohtsubo, Kota Dohi, Koichiro Yawata, Koki Takeshita, Tatsuya Sasaki · 2 June 2026

  Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly. Although generative models such as diffusion models and Generative Adversarial Networks (GANs) can augment training data, they cannot guarantee the …

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

  Yuting Hu, Lei Zhuang, Chen Wang, Ruiyang Qin, Hua Xiang, Gi-joon Nam, Jinjun Xiong · 14 May 2026

  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 …

• Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection

  Yuqi Li, Xingyou Lin, Yanli Li, Kai Zhang, Chuanguang Yang, Zhongliang Guo, Jianping Gou, Tingwen Huang, Yingli Tian · 1 May 2026

  As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional multimedia data, and federated learning provides a promising potential solution to this challenge. However, existing approaches rely solely on either parame…

• Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework

  Shomik Jain, Jack Lanchantin, Maximilian Nickel, Candace Ross, Karen Ullrich, Ashia Wilson, Jamelle Watson-Daniels · 23 April 2026

  Large language models often generate homogeneous outputs, but whether this is problematic depends on the specific task. For objective math tasks, responses may vary in terms of problem-solving strategy but should maintain the same verifiable answer. Whereas, for creative writing tasks, we often expe…

• Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask

  Vasiliy A. Es'kin, Egor V. Ivanov · 17 March 2026

  Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from contemporary lithography masks are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method …

• Pushing the Limits of Inverse Lithography with Generative Reinforcement Learning

  Haoyu Yang, Haoxing Ren · 24 February 2026

  Inverse lithography (ILT) is critical for modern semiconductor manufacturing but suffers from highly non-convex objectives that often trap optimization in poor local minima. Generative AI has been explored to warm-start ILT, yet most approaches train deterministic image-to-image translators to mimic…

• Transforming Computational Lithography with AC and AI -- Faster, More Accurate, and Energy-efficient

  Saumyadip Mukhopadhyay, Kiho Yang, Kasyap Thottasserymana Vasudevan, Mounica Jyothi Divvela, Selim Dogru, Dilip Krishnamurthy, Fergo Treska, Werner Gillijns, Ryan Ryoung han Kim, Kumara Sastry, Vivek Singh · 18 February 2026

  From climate science to drug discovery, scientific computing demands have surged dramatically in recent years -- driven by larger datasets, more sophisticated models, and higher simulation fidelity. This growth rate far outpaces transistor scaling, leading to unsustainably rising costs, energy consu…

• Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information

  Ming Shi, Yingbin Liang, Ness B. Shroff · 27 January 2026

  Partially observable Markov decision processes (POMDPs) are a general framework for sequential decision-making under latent state uncertainty, yet learning in POMDPs is intractable in the worst case. Motivated by sensing and probing constraints in practice, we study how much online state information…

• MaskOpt: A Large-Scale Mask Optimization Dataset to Advance AI in Integrated Circuit Manufacturing

  Yuting Hu, Lei Zhuang, Hua Xiang, Jinjun Xiong, Gi-Joon Nam · 25 December 2025

  As integrated circuit (IC) dimensions shrink below the lithographic wavelength, optical lithography faces growing challenges from diffraction and process variability. Model-based optical proximity correction (OPC) and inverse lithography technique (ILT) remain indispensable but computationally expen…

• Why mask diffusion does not work

  Haocheng Sun, Cynthia Xin Wen, Edward Hong Wang · 24 December 2025

  The main advantages of diffusion language models over autoregressive (AR) models lie in their ability to support parallel generation and bidirectional attention, enabling a more controllable generation process. In recent years, open-source mask diffusion language models have emerged, most of which a…

• PATCH: Learnable Tile-level Hybrid Sparsity for LLMs

  Younes Hourri, Mohammad Mozaffari, Maryam Mehri Dehnavi · 24 December 2025

  Large language models (LLMs) deliver impressive performance but incur prohibitive memory and compute costs at deployment. Model pruning is an effective way to reduce these overheads, yet existing approaches face challenges: unstructured sparsity, where nonzeros can appear anywhere, preserves accurac…

• OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction

  Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi, Amir Yazdanbakhsh · 17 December 2025

  Post-training model pruning is a promising solution, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimization methods recover accuracy but are computationally infeasible at modern scale. One-shot methods such as SparseGPT offer …

• LLM Output Homogenization is Task Dependent

  Shomik Jain, Jack Lanchantin, Maximilian Nickel, Karen Ullrich, Ashia Wilson, Jamelle Watson-Daniels · 9 December 2025

  A large language model can be less helpful if it exhibits output response homogenization. But whether two responses are considered homogeneous, and whether such homogenization is problematic, both depend on the task category. For instance, in objective math tasks, we often expect no variation in the…

• Atom of Thoughts for Markov LLM Test-Time Scaling

  Fengwei Teng, Quan Shi, Zhaoyang Yu, Jiayi Zhang, Chenglin Wu, Yuyu Luo, Zhijiang Guo · 1 December 2025

  Large Language Models (LLMs) achieve superior performance through training-time scaling, and test-time scaling further enhances their capabilities by conducting effective reasoning during inference. However, as the scale of reasoning increases, existing test-time scaling methods suffer from accumula…

• Physics-Constrained Adaptive Neural Networks Enable Real-Time Semiconductor Manufacturing Optimization with Minimal Training Data

  Rub\'en Dar\'io Guerrero · 18 November 2025

  The semiconductor industry faces a computational crisis in extreme ultraviolet (EUV) lithography optimization, where traditional methods consume billions of CPU hours while failing to achieve sub-nanometer precision. We present a physics-constrained adaptive learning framework that automatically cal…

• LithoSeg: A Coarse-to-Fine Framework for High-Precision Lithography Segmentation

  Xinyu He, Botong Zhao, Bingbing Li, Shujing Lyu, Jiwei Shen, Yue Lu · 18 November 2025

  Accurate segmentation and measurement of lithography scanning electron microscope (SEM) images are crucial for ensuring precise process control, optimizing device performance, and advancing semiconductor manufacturing yield. Lithography segmentation requires pixel-level delineation of groove contour…