Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.23408

LithoWorld: An Endpoint World Model for Lithography Process-Window Recovery

Botong Zhao·Fang Yu·Xinyuan Chen·Yue Lu
View original source ↗Read source document ↗Export metadata ↓

Abstract

Lithography process drift can move a fabricated pattern outside its process window, producing unqualified scanning electron microscopy (SEM) morphology. We present LithoWorld, an endpoint world model for SEM-guided parameter recovery. Interior representative targets jointly determine qualified reference appearance and correction supervision. A causal predictive branch aligns currentimage features with endpoint representations, while a conditional flow policy generates a two-dimensional correction as an action chunk. The model learns from 173 groups of measured SEMs organized as focus-exposure maps, preserving unmeasured cells as unknown. On 200 test pairs from the 73-group subset, LithoWorld achieves 88.5% qualified-region success and 46.0% interior success, exceeding the strongest comparison method by 2.5 and 6.0 percentage points, respectively. Ablations separate the effects of target selection, endpoint alignment, and output parameterization. Qualitative SEM retrieval illustrates recovery to qualified interiors without requiring exact reference-coordinate matching.

Explore related subjects

cs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Botong Zhao, Fang Yu, Xinyuan Chen, Yue Lu. 2026-10-16. LithoWorld: An Endpoint World Model for Lithography Process-Window Recovery. https://arxiv.org/abs/2610.23408

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Referring Multi-Object Tracking with Diverse Dynamic Expressions

Referring understanding is a fundamental task that bridges natural language and visual content by localizing objects described in free-form expressions. However, existing works are constrained by limited language expressiveness, lacking the capacity to model object dynamics in spatial numbers and temporal states. To address these limitations, we introduce a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking, comprehensively accounting for variations in object quantity and temporal semantics. Along with RMOT, we introduce a RMOT benchmark named Refer-KITTI-V2, featuring scalable and diverse language expressions. To efficiently generate high-quality annotations covering object dynamics with minimal manual effort, we propose a semi-automatic labeling pipeline that formulates a total of 9,758 language prompts. In addition, we propose TempRMOT, an elegant end-to-end Transformer-based framework for RMOT. At its core is a query-driven Temporal Enhancement Module that represents each object as a Transformer query, enabling long-term spatial-temporal interactions with other objects and past frames to efficiently refine these queries. TempRMOT achieves state-of-the-art performance on both Refer-KITTI and Refer-KITTI-V2, demonstrating the effectiveness of our approach. The source code and dataset is available at https://github.com/zyn213/TempRMOT.

cs.CV↗
arXiv2026

RAD: A Realistic Multi-View Benchmark for Pose-Agnostic Anomaly Detection

Anomaly detection is essential for robotic perception and industrial inspection, yet most benchmarks are collected under controlled conditions with fixed viewpoints and stable illumination. These settings do not reflect robotic deployment, where camera pose, lighting, and surface reflectance vary continuously. We present RAD (Realistic Anomaly Detection), a robot-captured multi-view benchmark for pose-agnostic anomaly detection. RAD contains 4,047 RGB images from 13 everyday object categories, captured from 68 viewpoints per object with a Franka robotic arm and an RGB-D camera under uncontrolled lighting. The dataset covers four realistic defect types: scratched, missing, stained, and squeezed, with pixel-level annotations for localization. We benchmark representative 2D feature-based methods, 3D reconstruction pipelines, and vision-language models under a realistic setting in which test poses are unknown. Results show a clear gap between performance on conventional benchmarks and performance on RAD. Strong 2D feature-embedding methods remain the most reliable at image-level detection, whereas 3D approaches are more competitive for pixel-level localization but remain vulnerable to reflective materials, geometric symmetry, and sparse viewpoint coverage. Vision-language models perform poorly in both classification and localization. RAD provides a challenging testbed for robust robotic inspection and highlights open problems in jointly modeling appearance, geometry, and viewpoint uncertainty. Our website and code are available at https://chang-xinhai.github.io/rad-website/.

cs.CV↗
arXiv2026

Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding

Visual grounding aims to localize the image regions based on a textual query. Given the difficulty of large-scale data curation, we investigate how to effectively learn visual grounding under data-scarce settings in this paper. To address the data scarcity, we propose a novel framework, POBF (Paint Outside the Box and Filter). POBF synthesizes images by inpainting outside the box, tackling a label misalignment issue encountered in previous works. Furthermore, POBF leverages an innovative filtering scheme to select the most effective training data. This scheme combines a hardness score and an overfitting score, balanced by a penalty term. Extensive experiments across four benchmark datasets demonstrate that POBF consistently improves performance, achieving an average gain of 5.83\% over the real-data-only method and outperforming leading baselines by 2.29\%-3.85\% in accuracy. Additionally, we validate the robustness and generalizability of POBF across various generative models, training data sizes, and model architectures.

cs.CV↗