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Accurate lithography simulation model based on convolutional neural networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/icassp.2013.6639346openalex-snapshot
Improving deep neural networks for LVCSR using rectified linear units and dropout
inferredcites →https://doi.org/10.1117/12.2217746openalex-snapshot
Automatic layout feature extraction for lithography hotspot detection based on deep neural network
inferredcites →https://doi.org/10.1117/12.474587openalex-snapshot
Universal process modeling with VTRE for OPC
inferredcites →https://doi.org/10.1117/12.535931openalex-snapshot
Neural-network-based approach to resist modeling and OPC
inferredcites →https://doi.org/10.1117/12.728962openalex-snapshot
Evaluation of lithography simulation model accuracy for hotspot-based mask quality assurance
inferredcites →https://doi.org/10.1117/12.881109openalex-snapshot
A new fast resist model: the Gaussian LPM
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4658626openalex-extracted
Deep Learning-Driven Inverse Lithography Technology for Dmd-Based Maskless Projection Lithography
inferred← citeshttps://doi.org/10.1007/978-3-030-04666-8_2openalex-snapshot
Machine Learning for Compact Lithographic Process Models
inferred← citeshttps://doi.org/10.1007/978-3-030-04666-8_4openalex-snapshot
Machine Learning in Physical Verification, Mask Synthesis, and Physical Design
inferred← citeshttps://doi.org/10.1007/s11432-024-4155-7openalex-snapshot
Large circuit models: opportunities and challenges
inferred← citeshttps://doi.org/10.1007/s40009-026-02465-4openalex-snapshot
MSFGQ: An Efficient Multiparametric Model for Improving Sub-Field Scheduling Performance via Novel GA and Q-Learning Optimizations
inferred← citeshttps://doi.org/10.1021/acsphotonics.2c00612openalex-snapshot
Algorithm-Driven Paradigms for Freeform Optical Engineering
inferred← citeshttps://doi.org/10.1063/5.0333553openalex-snapshot
Artificial neural networks in semiconductor device compact modeling: A review
inferred← citeshttps://doi.org/10.1109/cstic52283.2021.9461424openalex-snapshot
Lithographic Simulator Based on Deep Learning with Graph Input
inferred← citeshttps://doi.org/10.1109/dac63849.2025.11133153openalex-snapshot
SDM-PEB: Spatial-Depthwise Mamba for Enhanced Post-Exposure Bake Simulation
inferred← citeshttps://doi.org/10.1109/iseda62518.2024.10617568openalex-snapshot
End-to-End Lithography Modeling Based on Process Parameters and Deep Learning
inferred← citeshttps://doi.org/10.1109/tcad.2018.2864251openalex-snapshot
Data Efficient Lithography Modeling With Transfer Learning and Active Data Selection
inferred← citeshttps://doi.org/10.1109/tcad.2020.3015469openalex-snapshot
From IC Layout to Die Photograph: A CNN-Based Data-Driven Approach
inferred← citeshttps://doi.org/10.1109/tcad.2021.3124762openalex-snapshot
MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
inferred← citeshttps://doi.org/10.1109/tcad.2022.3171441openalex-snapshot
Efficient Proximity Effect Correction Using Fast Multipole Method With Unequally Spaced Grid for Electron Beam Lithography
inferred← citeshttps://doi.org/10.1109/tcad.2024.3378600openalex-snapshot
AdaOPC 2.0: Enhanced Adaptive Mask Optimization Framework for via Layers
inferred← citeshttps://doi.org/10.1109/tcad.2024.3463539openalex-snapshot
LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection
inferred← citeshttps://doi.org/10.1109/tcad.2024.3468016openalex-snapshot
Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning
inferred← citeshttps://doi.org/10.1109/tcad.2024.3513264openalex-snapshot
pFed-Litho: Lithography Modeling With a Personalized Federated Learning-Based Framework
inferred← citeshttps://doi.org/10.1109/test.2018.8624733openalex-snapshot
Machine Learning for Yield Learning and Optimization
inferred← citeshttps://doi.org/10.1109/tsm.2025.3582194openalex-snapshot
Process-Aware Digital Twins by Deep Learning for DUV Photolithography and Plasma Etch
inferred← citeshttps://doi.org/10.1117/1.jmm.18.1.013507openalex-snapshot
Lithography hotspot detection using a double inception module architecture
inferred← citeshttps://doi.org/10.1117/12.2514884openalex-snapshot
Automatic correction of lithography hotspots with a deep generative model
inferred← citeshttps://doi.org/10.1117/12.3051827openalex-snapshot
Hybrid mask 3D modeling driven by both physical solution and machine learning
inferred← citeshttps://doi.org/10.1145/3177540.3178242openalex-snapshot
Data Efficient Lithography Modeling with Residual Neural Networks and Transfer Learning
inferred← citeshttps://doi.org/10.1145/3394885.3431624openalex-snapshot
Deep Learning for Mask Synthesis and Verification
inferred← citeshttps://doi.org/10.1145/3566097.3568362openalex-snapshot
Data-Driven Approaches for Process Simulation and Optical Proximity Correction
inferred← citeshttps://doi.org/10.1145/3649476.3658802openalex-snapshot
Automated Lithography Resolution Enhancement with Deep Learning Enabled Layout Modification during Physical Design Stage
inferred← citeshttps://doi.org/10.1364/oe.416871openalex-snapshot
U-Net convolutional neural network-based modification method for precise fabrication of three-dimensional microstructures using laser direct writing lithography
inferred← citeshttps://doi.org/10.2197/ipsjtsldm.14.2openalex-snapshot
Computational Lithography Using Machine Learning Models
inferred← citeshttps://doi.org/10.23919/date69613.2026.11539572openalex-snapshot
DiffResist: Physics-Constrained Diffusion for Photoresist Modeling
inferred← citeshttps://doi.org/10.7567/jjap.57.06jc01openalex-snapshot
Cooperative simulation of lithography and topography for three-dimensional high-aspect-ratio etching
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