Evidence map›Paper›PMID 29301265›Full record

ArticleSensors (Basel, Switzerland)2018

Atomic Force Microscopy Sidewall Imaging with a Quartz Tuning Fork Force Sensor.

Danish Hussain, Yongbing Wen, Hao Zhang, Jianmin Song, Hui Xie

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. 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

5 authors.

Danish HussainState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, Nangang District, Harbin 150080, China. danishasefi@hit.edu.cn.ORCID 0000-0002-5541-605X
Yongbing WenState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, Nangang District, Harbin 150080, China. wenbo6210@hit.edu.cn.
Hao ZhangState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, Nangang District, Harbin 150080, China. Haoz@hit.edu.cn.
Jianmin SongState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, Nangang District, Harbin 150080, China. hitsjm@hit.edu.cn.
Hui XieState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, Nangang District, Harbin 150080, China. xiehui@hit.edu.cn.ORCID 0000-0002-5541-605X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sidewall roughness measurement is becoming increasingly important in the micro-electromechanical systems and nanoelectronics devices. Atomic force microscopy (AFM) is an emerging technique for sidewall scanning and roughness measurement due to its high resolution, three-dimensional imaging capability and high accuracy. We report an AFM sidewall imaging method with a quartz tuning fork (QTF) force sensor. A self sensing and actuating force sensor is fabricated by microassembling a commercial AFM cantilever (tip apex radius ≤10 nm) to a QTF. The attached lightweight cantilever allows high-sensitivity force detection (7.4% Q factor reduction) and sidewall imaging with high lateral resolution. Owing to its unique configuration, the tip of the sensor can detect sidewall surface orthogonally during imaging, which reduces lateral friction. In experiments, sidewalls of a micro-electro-mechanical system (MEMS) structure fabricated by deep reactive ion etching process and a standard step grating are scanned and the sidewall roughness, line edge roughness and sidewall angles are measured.

Indexed as

atomic force microscopycantileverquartz tuning forksidewall

Identifiers

PMID29301265
PMCPMC5795913

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.