Evidence map›Paper›PMID 33567759›Full record

ArticleNanomaterials (Basel, Switzerland)2021

Highly Efficient Near-Infrared Detector Based on Optically Resonant Dielectric Nanodisks.

Reza Masoudian Saadabad, Christian Pauly, Norbert Herschbach, Dragomir N Neshev, Haroldo T Hattori, Andrey E Miroshnichenko

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Reza Masoudian SaadabadSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.
Christian PaulyIEE S.A., Bissen L-7795, Luxembourg.
Norbert HerschbachIEE S.A., Bissen L-7795, Luxembourg.
Dragomir N NeshevARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS), Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT 2601, Australia.
Haroldo T HattoriSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0003-3092-0126
Andrey E MiroshnichenkoSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.ORCID 0000-0001-9607-6621

Funding

Australian Research Council 501100000923
6 · The paper itself

Abstract

Fast detection of near-infrared (NIR) photons with high responsivity remains a challenge for photodetectors. Germanium (Ge) photodetectors are widely used for near-infrared wavelengths but suffer from a trade-off between the speed of photodetection and quantum efficiency (or responsivity). To realize a high-speed detector with high quantum efficiency, a small-sized photodetector efficiently absorbing light is required. In this paper, we suggest a realization of a dielectric metasurface made of an array of subwavelength germanium PIN photodetectors. Due to the subwavelength size of each pixel, a high-speed photodetector with a bandwidth of 65 GHz has been achieved. At the same time, high quantum efficiency for near-infrared illumination can be obtained by the engineering of optical resonant modes to localize optical energy inside the intrinsic Ge disks. Furthermore, small junction capacitance and the possibility of zero/low bias operation have been shown. Our results show that all-dielectric metasurfaces can improve the performance of photodetectors.

Indexed as

bandwidthdielectric nanodisksNIR photodetector

Identifiers

PMID33567759
PMCPMC7914410

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.