Evidence map›Paper›PMID 34835572›Full record

ArticleNanomaterials (Basel, Switzerland)2021

Optimization of Metal-Assisted Chemical Etching for Deep Silicon Nanostructures.

Rabia Akan, Ulrich Vogt

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Silica- and Silicon-Based Nanostructures.Nanomaterials (Basel, Switzerland) · 2022
    Article
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

2 authors.

Rabia AkanKTH Royal Institute of Technology, Department of Applied Physics, Albanova University Center, 106 91 Stockholm, Sweden.ORCID 0000-0002-9520-5820
Ulrich VogtKTH Royal Institute of Technology, Department of Applied Physics, Albanova University Center, 106 91 Stockholm, Sweden.ORCID 0000-0002-4394-0591

Funding

Swedish Research Council 2018-04237Swedish Research Council 2019-06104
6 · The paper itself

Abstract

High-aspect ratio silicon (Si) nanostructures are important for many applications. Metal-assisted chemical etching (MACE) is a wet-chemical method used for the fabrication of nanostructured Si. Two main challenges exist with etching Si structures in the nanometer range with MACE: keeping mechanical stability at high aspect ratios and maintaining a vertical etching profile. In this work, we investigated the etching behavior of two zone plate catalyst designs in a systematic manner at four different MACE conditions as a function of mechanical stability and etching verticality. The zone plate catalyst designs served as models for Si nanostructures over a wide range of feature sizes ranging from 850 nm to 30 nm at 1:1 line-to-space ratio. The first design was a grid-like, interconnected catalyst (brick wall) and the second design was a hybrid catalyst that was partly isolated, partly interconnected (fishbone). Results showed that the brick wall design was mechanically stable up to an aspect ratio of 30:1 with vertical Si structures at most investigated conditions. The fishbone design showed higher mechanical stability thanks to the Si backbone in the design, but on the other hand required careful control of the reaction kinetics for etching verticality. The influence of MACE reaction kinetics was identified by lowering the oxidant concentration, lowering the processing temperature and by isopropanol addition. We report an optimized MACE condition to achieve an aspect ratio of at least 100:1 at room temperature processing by incorporating isopropanol in the etching solution.

Indexed as

high aspect ratiometal-assisted chemical etchingSi nanostructureszone plate

Identifiers

PMID34835572
PMCPMC8619014

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Registered trials

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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.