Semiconductor Manufacturing

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Semiconductor manufacturing studies how to fabricate, measure, inspect, and assemble chips while controlling defects, cost, and production time. Work spans lithography and etch simulation, wafer and package inspection, virtual metrology, equipment maintenance, and fab scheduling. Common approaches include computer vision and self-supervised learning for inspection, physics-informed neural networks and operators for process simulation, and optimization or reinforcement learning for process settings and factory control. A central challenge is making models reliable beyond limited, proprietary training data while preserving physical accuracy, quantifying uncertainty, and validating decisions in production.

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45 papers7 papers in the last four weeks, up 17% on the four weeks before. 0.0% of all new papers.

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
Jun 8, 2026Yaser Mike Banad, Sarah SharifManufacturing Constrained Generative Modeling
School of Electrical and Computer Engineering, University of Oklahoma · 3Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory · 4Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA +1
Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
Jun 8, 2026Pangyun Jeong, Jiyeong Kong, Yuehua Hu +2Vision-Language Models VLM Adaptation
1HYU-KITECH Joint Department, Hanyang University, Ansan, 15588, Korea · 4Korea Institute of Industrial Technology, Ansan, 15588, Korea · 2Micro/Nano System Department, Korea University, Seoul 02841, Korea +1
LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
May 29, 2026Yao Lai, Xuyuan Xiong, Zeyue Xue +7Semiconductor Manufacturing Inverse Design
The University of Hong Kong, Hong Kong · University of Cambridge, Cambridge, UK · The Chinese University of Hong Kong, Hong Kong +2
FAB-Bench: A Framework for Adaptive RAG Benchmarking in Semiconductor Manufacturing
May 26, 2026Jingbin Qian, Congwen Yi, Min Xia +3Retrieval-Augmented Generation Benchmark Design
FutureFab.AI
Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
May 26, 2026Yuxuan Yin, Chen He, Todd Jacobs +4Semiconductor Manufacturing Industrial Anomaly Detection
Department of Electrical and Computer Engineering, University of California Santa Barbara, CA, USA · Automotive Processing, NXP Semiconductors, TX, USA
AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection
May 12, 2026Joaquín Figueira, Rob Van Gastel, Giacomo D'Amicantonio +4Self-Supervised Pre-Training Semiconductor Manufacturing
AIMS Lab, Eindhoven University of Technology · CoC, ASMPT · iCIS, Radboud University
Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification
May 8, 2026Min Gao, Julia Maria Perathoner, Anton Ludwig Bonin +2Concept Drift Semiconductor Manufacturing
Merck Group, Versum Materials US LLC, Wilmington, USA · Merck Group, Merck Electronics KGaA, Darmstadt, Germany · Technical University of Darmstadt, Darmstadt, Germany +1
Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry
May 8, 2026A. Azamnouri, M. Haug, L. Woltmann +3Software Engineering Semiconductor Manufacturing
Chair of Software Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany · Zeiss, Oberkochen, Germany · Business School, Pforzheim University, Pforzheim, Germany +1
Full-chip CMP modelling based on Fully Convolutional Network leveraging White Light Interferometry
May 6, 2026Jules Exbrayat, Renan Bouis, Elie Sezestre +4Neural Surrogate Modeling Semiconductor Manufacturing
Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France
WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning
Apr 30, 2026Ke Xu, Zhongyuan LianVLM Evaluation Visual Question Answering
Shanghai Huahong Grace Semiconductor Manufacturing Corporation, Shanghai, 201203, China · Dept. of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning
Apr 13, 2026Yuting Hu, Lei Zhuang, Chen Wang +4Semiconductor Manufacturing
University at Buffalo, Buffalo, NY, USA · IBM T. J. Watson Research Center, Yorktown Heights, NY, USA · Villanova University, Villanova, PA, USA
AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design
Apr 1, 2026Utkarsh Pratiush, Huaixun Huyan, Maryam Zahiri Azar +4Microscopy Image Analysis Scientific Workflow Automation
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA · Western Digital corporation, 5601 Great Oaks Parkway, San Jose, CA 95119, USA
Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
Mar 24, 2026Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon +1Semiconductor Manufacturing Predictive Process Monitoring
Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea
Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
Mar 16, 2026Vasiliy A. Es'kin, Egor V. IvanovNeural PDE Solvers Neural Operators
Department of Radiophysics, University of Nizhny Novgorod 23 Gagarin Ave., Nizhny Novgorod 603022, Russia
Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation
Mar 15, 2026Zihan ZhangImage Segmentation Industrial Anomaly Detection
Engineering Technology Research Center of Henan Province for MEMS Manufacturing and Applications, School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450001, China

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Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction

Yaser Mike Banad, Sarah Sharif

School of Electrical and Computer Engineering, University of Oklahoma · 3Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory · 4Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA+1

Manufacturing Constrained Generative Modeling Physics-Informed Generative Modeling Semiconductor Manufacturing Generative Modeling Physics-Informed ML+1

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Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than by perceptual plausibility. Semiconductor manufacturing provides a demanding test case: generated masks, layouts, synthetic defect data, and process recipes must obey lithography, transport, reaction, and device-physics constraints, because physically invalid samples are not merely low quality but unusable. This Perspective argues that semiconductor manufacturing exposes a broader computational-science challenge, namely that generative AI for constrained physical domains must be physics-informed by construction, not corrected only through post-hoc filtering. We survey the emerging architectural toolkit, including physics-informed diffusion, PDE-constrained variational models, neural-operator priors, and conservation-law-respecting generative networks, and show how it connects to differentiable lithography, TCAD, process simulation, and autonomous experimentation. We identify four integration patterns between generative models and physics-based simulators, and we propose a research agenda centered on physics-fidelity benchmarks, differentiable simulator infrastructure, and multimodal foundation models for physical design and manufacturing. The central claim is analytical rather than rhetorical: where physical validity is the binding criterion of success, architectures that enforce it by construction should be expected to outperform those that filter for it after the fact, and the fab is the setting where this distinction is sharpest.