CMOS-Compatible High-Performance Silicon Nanowire Array Natural Light Electronic Detection System.

Xin Chen, Jiaye Zeng, Mingbin Liu, Chilin Zheng, Xiaoyuan Wang, Chaoran Liu, Xun Yang

Abstract read

In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Xin ChenSchool of Electronic and Information Engineering, China West Normal University, Nanchong 637002, China.

Jiaye ZengSchool of Electronic and Information Engineering, China West Normal University, Nanchong 637002, China.

Mingbin LiuSchool of Electronic and Information Engineering, China West Normal University, Nanchong 637002, China.

Chilin ZhengSchool of Electronic and Information Engineering, China West Normal University, Nanchong 637002, China.ORCID 0009-0004-4483-9139

Xiaoyuan WangZhejiang Key Laboratory of Ecological and Environmental Big Data, Hangzhou 321001, China.

Chaoran LiuMinistry of Education Engineering Research Center of Smart Microsensors and Microsystems, College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.ORCID 0000-0002-6896-328X

Xun YangSchool of Electronic and Information Engineering, China West Normal University, Nanchong 637002, China.

Funding

Key Laboratory of Detection and Application of Space Effect in Southwest Sichuan at Leshan Normal University, the Education Department of Sichuan Province ZDXM202401004Open Funding of the Zhejiang Key Laboratory of Ecological and Environmental Big Data EEBD-2022-02

6 · The paper itself

Abstract

In this article, we propose a novel natural light detector based on high-performance silicon nanowire (SiNW) arrays. We achieved a highly controllable and low-cost fabrication of SiNW natural light detectors by using only a conventional micromachined CMOS process. The high activity of SiNWs leads to the poor long-term stability of the SiNW device, and for this reason, we have designed a fully wrapped structure for SiNWs. SiNWs are wrapped in transparent silicon nitride and silicon oxide films, which greatly improves the long-term stability of the detector; at the same time, this structure protects the SiNWs from breakage. In addition, the SiNW arrays are regularly distributed on the top of the detector, which can quickly respond to natural light. The response time of the detector is about 0.015 s. Under the illumination of 1 W·m

Indexed as

highly controllablehigh-volume preparationlow costnatural lightsignal detection circuitssilicon nanowire arrays

Identifiers

PMID39459075

DOI10.3390/mi15101201

PMCPMC11509308

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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.