Automatic detection of photoresist residual layer in lithography using a neural classification approach

Issam Gereige, Stéphane Robert, Jessica Eid

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.
Original languageEnglish (US)
Pages (from-to)29-32
Number of pages4
JournalMicroelectronic Engineering
StatePublished - Sep 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Surfaces, Coatings and Films
  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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