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Data-driven optimization of maskless grayscale laser lithography.

Adrian H A Lutey1, David Kuhness2, Seyyedhossein Mckee3

  • 1Dipartimento di Ingegneria dei Sistemi e delle Tecnologie Industriali, Università degli studi di Parma, 43124, Parma, Italy. adrian.lutey@unipr.it.

Scientific Reports
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

A new data-driven algorithm optimizes maskless grayscale laser lithography (MGLL) by using an artificial neural network (ANN) to correct topography errors. This significantly improves accuracy for fabricating micro-optical elements.

Keywords:
Artificial neural networksData-driven optimizationGrayscale laser lithographyMicro-optical elements

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Computational Science

Background:

  • Maskless grayscale laser lithography (MGLL) faces challenges with topography errors.
  • These errors stem from photoresist non-linear responses, proximity effects, and dose transitions.

Purpose of the Study:

  • To develop a data-driven optimization algorithm for MGLL.
  • To correct topography errors and improve fabrication accuracy.
  • To enable first-time-right fabrication of micro-optical elements.

Main Methods:

  • An artificial neural network (ANN) was developed to model the MGLL process.
  • The ANN uses MGLL virtual photomasks and radial averages as inputs to predict surface topography.
  • Virtual photomask optimization was achieved through iterative prediction and correction of topography errors.
  • A comprehensive dataset of approximately one million data points was used for ANN training.

Main Results:

  • An optimized ANN architecture with a Sigmoid activation function and a single hidden layer (15 neurons) demonstrated high accuracy and generalization.
  • The optimization algorithm reduced the average Euclidean distance between experimental and target surfaces from 3.6 μm to 1.0-1.2 μm for an ultrathin free-form micro-optical element (FFMO).
  • This represents a significant improvement in fabrication precision in a single build.

Conclusions:

  • The developed data-driven algorithm effectively corrects topography errors in MGLL.
  • This approach offers a versatile and adaptable pathway for achieving high-accuracy, first-time-right fabrication.
  • The method is promising for the precise manufacturing of micro-optical elements.