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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/10.1016/j.optlastec.2024.111578openalex-snapshot

Deep learning-driven digital inverse lithography technology for DMD-based maskless projection lithography

inferred← citeshttps://doi.org/10.1038/s41377-025-01923-wopenalex-snapshot

Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches

inferred← citeshttps://doi.org/10.1109/access.2022.3192387openalex-snapshot

Aliasing-Free Nonlinear Signal Processing Using Implicitly Defined Functions

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.2025.3594247openalex-snapshot

A Post-Routing Layout Optimization Framework for Lithography Process Window Enlargement

inferred← citeshttps://doi.org/10.1109/test.2018.8624733openalex-snapshot

Machine Learning for Yield Learning and Optimization

inferred← citeshttps://doi.org/10.1117/12.2504982openalex-snapshot

The self-driving photomask

inferred← citeshttps://doi.org/10.3103/s1060992x21040056openalex-snapshot

Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)

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