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ARXIV:2603.15584 · PHYSICS-INFORMED NEURAL NETWORKS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.15584PHYSICS-INFORMED NEURAL NETWORKSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
Opportunity summary
Pain A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the…
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
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10.48550/arXiv.2603.15584A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
Abstract
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 with its most computationally expensive components replaced by a neural network. To evaluate performance, the accuracy and inference time of PINNs and NOs are compared against modern numerical solvers for a series of problems with known exact solutions. The emphasis is placed on investigation of solution accuracy by considered artificial neural systems for 13.5 nm and 11.2 nm wavelengths. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the proposed WGNO architecture reaching state-of-the-art performance. The presented neural operator has pronounced generalizing properties, meaning that for unseen problem parameters it delivers a solution accuracy close to that for parameters seen in the training dataset. These results provide a highly efficient solution for accelerating the design and optimization workflows of next-generation lithography masks.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Dimensions overall score 4.0
PROBLEM
A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive components replaced by a neural ne...
METHOD
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 o...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the proposed WGNO architecture...
WHY NOW
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive components replaced by a neural network.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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 with its most computationally expensive components replaced by a neural network.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the proposed WGNO architecture reaching state-of-the-art performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
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Physics-Informed Neural Networks
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