Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask

PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (27)

[1]
Laser discharge in a high-pressure jet of heavy noble gas: Expansion of emitting volume promises an efficient source of EUV light for lithography
2025I. S. Abramov, S. Golubev et al.
[2]
Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?
2024V. A. Es'kin, A. O. Malkhanov et al.
[3]
New Concept for the Development of High-Performance X-ray Lithography
2024N. I. Chkhalo
[4]
3D EUV mask simulator based on physics-informed neural networks: effects of polarization and illumination
2024V. Medvedev, A. Erdmann et al.
[5]
Weakly guiding approximation of a three-dimensional waveguide model for extreme ultraviolet lithography simulation.
2024Hiroyoshi Tanabe, Akira Jinguji et al.
[6]
3D mask simulation and lithographic imaging using physics-informed neural networks
2024V. Medvedev, A. Erdmann et al.
[7]
Respecting causality for training physics-informed neural networks
2024Sifan Wang, Shyam Sankaran et al.
[8]
Accelerating extreme ultraviolet lithography simulation with weakly guiding approximation and source position dependent transmission cross coefficient formula
2024Hiroyoshi Tanabe, Akira Jinguji et al.
[9]
Highly reflective Ru/Sr multilayer mirrors for wavelengths 9-12 nm.
2022R. Shaposhnikov, V. Polkovnikov et al.
[10]
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
2022Haoyu Yang, Zong-Yi Li et al.
[11]
Fourier Neural Operator for Parametric Partial Differential Equations
2020Zong-Yi Li, Nikola B. Kovachki et al.
[12]
A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks
2020S. Dong, Naxian Ni
[13]
Neural Operator: Graph Kernel Network for Partial Differential Equations
2020Zong-Yi Li, Nikola B. Kovachki et al.
[14]
PyTorch: An Imperative Style, High-Performance Deep Learning Library
2019Adam Paszke, Sam Gross et al.
[15]
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
2019Lu Lu, Pengzhan Jin et al.
[16]
Beryllium-based multilayer X-ray optics
2019V. Polkovnikov, N. Salashchenko et al.
[17]
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2019M. Raissi, P. Perdikaris et al.
[18]
High-reflection Mo/Be/Si multilayers for EUV lithography.
2017N. Chkhalo, S. A. Gusev et al.
[19]
New development in freefem++
2012F. Hecht
[20]
Understanding the difficulty of training deep feedforward neural networks
2010Xavier Glorot, Yoshua Bengio

Showing 20 of 27 references

Founder's Pitch

"A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks."

Physics-Informed Neural NetworksScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 3/16/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.

Related Papers

Loading…