Evidence map›Paper›PMID 38817423›Full record

ArticleNanoscale advances2024

UV-assisted nanoimprint lithography: the impact of the loading effect in silicon on nanoscale patterns of metalens.

Zahrah Alnakhli, Zhiyuan Liu, Feras AlQatari, Haicheng Cao, Xiaohang Li

Abstract read
In one paragraph

Article in Nanoscale advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zahrah AlnakhliKing Abdullah University of Science and Technology (KAUST), Advanced Semiconductor Laboratory Thuwal 23955-6900 Saudi Arabia Zahrah.alnakhli@kaust.edu.sa xiaohang.li@kaust.edu.sa.ORCID https://orcid.org/0000-0002-4969-3325
Zhiyuan LiuKing Abdullah University of Science and Technology (KAUST), Advanced Semiconductor Laboratory Thuwal 23955-6900 Saudi Arabia Zahrah.alnakhli@kaust.edu.sa xiaohang.li@kaust.edu.sa.
Feras AlQatariKing Abdullah University of Science and Technology (KAUST), Advanced Semiconductor Laboratory Thuwal 23955-6900 Saudi Arabia Zahrah.alnakhli@kaust.edu.sa xiaohang.li@kaust.edu.sa.
Haicheng CaoKing Abdullah University of Science and Technology (KAUST), Advanced Semiconductor Laboratory Thuwal 23955-6900 Saudi Arabia Zahrah.alnakhli@kaust.edu.sa xiaohang.li@kaust.edu.sa.
Xiaohang LiKing Abdullah University of Science and Technology (KAUST), Advanced Semiconductor Laboratory Thuwal 23955-6900 Saudi Arabia Zahrah.alnakhli@kaust.edu.sa xiaohang.li@kaust.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work studies the impact of the silicon (Si) loading effect induced by deep reactive ion etching (DRIE) of silicon master molds on the UV-nanoimprint lithography (NIL) patterning of nanofeatures. The silicon molds were patterned with metasurface features with widths varying from 270 to 60 nm. This effect was studied by focus ion beam scanning electron microscopy (FIB-SEM) and atomic force microscopy (AFM). The Si loading etching effect is characterized by the variation of pattern feature depth concerning feature sizes because smaller features tend to etch more slowly than larger ones due to etchants being more difficult to pass through the smaller hole and byproducts being harder to diffuse out too. Thus, the NIL results obtained from the Si master mold contain different pattern geometries concerning pattern quality and residual photoresist layer thickness. The obtained results are pivotal for NIL for fabricating devices with various geometrical nanostructures as the research field moves towards commercial applications.

Identifiers

PMID38817423
PMCPMC11134229

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.