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Related Experiment Video

Updated: Aug 15, 2025

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

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

Huwen Ding, Lihong Liu, Ziqi Li

|January 6, 2023

Summary

This summary is machine-generated.

Plasmonic lithography overcomes diffraction limits using surface plasmon polaritons (SPPs). A new machine learning model enables fast, accurate imaging for advanced nanolithography applications.

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Area of Science:

Nanotechnology
Optics
Computational Science

Background:

Conventional lithography faces diffraction limits, hindering nanoscale fabrication.
Plasmonic lithography utilizes surface plasmon polaritons (SPPs) to amplify evanescent waves, enabling sub-diffraction imaging.
Existing fast imaging models for DUV/EUV lithography provide a basis for plasmonic lithography advancements.

Purpose of the Study:

To introduce a novel fast imaging model for plasmonic lithography.
To enable direct imaging from mask patterns to photoresist images.
To compare the proposed model's efficiency and accuracy against rigorous electromagnetic models.

Main Methods:

Decomposition machine learning method applied to mask diffraction near-field (DNF).
Development of a fast imaging model for arbitrary 2D patterns in plasmonic lithography.
Utilizing various illumination methods: normal incidence (x/y polarization) and quadrupole illumination (TM polarization).

Main Results:

The proposed model accurately images binary mask inputs to photoresist image intensity distributions.
Significant improvement in calculation efficiency compared to rigorous electromagnetic models.
High accuracy maintained, comparable to traditional methods.

Conclusions:

The developed fast imaging model is a significant advancement for plasmonic lithography.
This model facilitates computational lithography techniques like SMO/OPC for plasmonic systems.
Enables efficient and accurate nanolithography for low-cost, large-area applications.