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Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

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Plasmonic lithography fast imaging model based on the decomposition machine learning method under arbitrary

Zhenyu Xing, Huwen Ding, Yajuan Su

|May 4, 2026

Summary

This summary is machine-generated.

A new machine learning model enables faster and more accurate imaging simulations for surface plasmonic lithography (SPL), handling complex illumination conditions effectively. This advancement accelerates computational lithography processes like source-mask optimization (SMO) and optical proximity correction (OPC).

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Area of Science:

Computational lithography
Plasmonic nanophotonics
Machine learning for scientific simulation

Background:

Existing fast imaging models for surface plasmonic lithography (SPL) face limitations with complex illumination.
Efficient and accurate simulation tools are crucial for advanced lithography processes such as source-mask optimization (SMO) and optical proximity correction (OPC).

Purpose of the Study:

To develop a general fast imaging model for SPL applicable to arbitrary illumination systems.
To improve computational efficiency and maintain sub-wavelength imaging accuracy in SPL simulations.

Main Methods:

A decomposition machine learning method was employed, utilizing rigorous electromagnetic field (EMF) simulations to build a training library.
An imaging transfer matrix (ITM) was trained for fast mapping of mask features.
An approximation method based on inverse spatial distance weighting was developed for non-reference point sources and partially coherent illumination.

Main Results:

The model demonstrated robustness for TE and TM polarization states, with predicted photoresist images (PRI) showing high consistency with rigorous EMF simulations (RMSE < 0.075).
Computational speed improvements ranged from 7 to 60 times compared to rigorous simulations, depending on the illumination complexity.
The model significantly reduced computational costs while ensuring sub-wavelength imaging accuracy.

Conclusions:

The proposed general fast imaging model effectively addresses limitations of existing methods for SPL.
This work provides a key efficient simulation tool for SMO and OPC in plasmonic lithography, reducing computational burden.
The model offers a pathway for faster design and optimization in advanced lithography technologies.