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Semiconductor Sensor

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 15916

Special Issue Editors

Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: silicon carbide; gallium nitride; indium phosphide; processing; characterization; sensors; microwave devices; photo detectors; high power devices; microfluidic cells
George Mason University, Fairfax, VA 22030, USA
Interests: gallium nitride; chemical sensor; device modeling; sensor array; FET device

Special Issue Information

Dear Colleagues,

Sensors play an important role in advancing technology for various applications. We encourage authors to submit original research papers on various types of semiconductor-based sensors, not including photosensors. The types of sensors we are interested in for this Special Issue include but are not limited to chemical/gas, radiation, biological, stress, and motion sensors. The articles can be related to the fabrication, characterization, modeling and reliability of the sensor devices. The semiconductors can be elemental or any compound semiconductor. Articles on novel applications of the existing sensor devices are welcome as well.

Prof. Dr. Mulpuri V Rao
Dr. Md Ashfaque Hossain Khan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Semiconductor
  • Sensor
  • Fabrication
  • Characterization
  • Modeling
  • Reliability
  • Application

Published Papers (4 papers)

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Research

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7 pages, 930 KiB  
Communication
Back-Gate GaN Nanowire-Based FET Device for Enhancing Gas Selectivity at Room Temperature
by Md Ashfaque Hossain Khan, Ratan Debnath, Abhishek Motayed and Mulpuri V. Rao
Sensors 2021, 21(2), 624; https://doi.org/10.3390/s21020624 - 17 Jan 2021
Cited by 11 | Viewed by 2586
Abstract
In this work, a TiO2-coated GaN nanowire-based back-gate field-effect transistor (FET) device was designed and implemented to address the well-known cross-sensitive nature of metal oxides. Even though a two-terminal TiO2/GaN chemiresistor is highly sensitive to NO2, it [...] Read more.
In this work, a TiO2-coated GaN nanowire-based back-gate field-effect transistor (FET) device was designed and implemented to address the well-known cross-sensitive nature of metal oxides. Even though a two-terminal TiO2/GaN chemiresistor is highly sensitive to NO2, it suffers from lack of selectivity toward NO2 and SO2. Here, a Si back gate with C-AlGaN as the gate dielectric was demonstrated as a tunable parameter, which enhances discrimination of these cross-sensitive gases at room temperature (20 °C). Compared to no bias, a back-gate bias resulted in a significant 60% increase in NO2 response, whereas the increase was an insignificant 10% in SO2 response. The differential change in gas response was explained with the help of a band diagram, derived from the energetics of molecular models based on density functional theory (DFT). The device geometries in this work are not optimized and are intended only for proving the concept. Full article
(This article belongs to the Special Issue Semiconductor Sensor)
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20 pages, 4464 KiB  
Article
Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors
by Beata Bąk, Jakub Wilk, Piotr Artiemjew, Jerzy Wilde and Maciej Siuda
Sensors 2020, 20(14), 4014; https://0-doi-org.brum.beds.ac.uk/10.3390/s20144014 - 19 Jul 2020
Cited by 9 | Viewed by 2435
Abstract
Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a [...] Read more.
Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class—empty chamber (13 objects), 2nd class—fragments of combs containing brood sick with varroosis (19 objects), and 3rd class—fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)—with default settings in RSES tool—was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments—in multiple Monte Carlo Cross Validation model—using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context. Full article
(This article belongs to the Special Issue Semiconductor Sensor)
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10 pages, 2935 KiB  
Article
Theoretical Investigation of an Alcohol-Filled Tellurite Photonic Crystal Fiber Temperature Sensor Based on Four-Wave Mixing
by Yue Sun, Xin Yan, Fang Wang, Xuenan Zhang, Shuguang Li, Takenobu Suzuki, Yasutake Ohishi and Tonglei Cheng
Sensors 2020, 20(4), 1007; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041007 - 13 Feb 2020
Cited by 23 | Viewed by 2234
Abstract
For this study, a temperature sensor utilizing a novel tellurite photonic crystal fiber (PCF) is designed. In order to improve the sensor sensitivity, alcohol is filled in the air holes of the tellurite PCF. Based on the degenerate four-wave mixing theory, temperature sensing [...] Read more.
For this study, a temperature sensor utilizing a novel tellurite photonic crystal fiber (PCF) is designed. In order to improve the sensor sensitivity, alcohol is filled in the air holes of the tellurite PCF. Based on the degenerate four-wave mixing theory, temperature sensing in the mid-infrared region (MIR) can be achieved by detecting the wavelength shift of signal waves and idler waves during variations in temperature. Simulation results show that at a pump wavelength of 3550 nm, the temperature sensitivity of this proposed sensor can be as high as 0.70 nm/°C. To the best of our knowledge, this is the first study to propose temperature sensing in the MIR by drawing on four-wave mixing (FWM) in a non-silica PCF. Full article
(This article belongs to the Special Issue Semiconductor Sensor)
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Review

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22 pages, 3445 KiB  
Review
Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review
by Md Ashfaque Hossain Khan and Mulpuri V. Rao
Sensors 2020, 20(14), 3889; https://0-doi-org.brum.beds.ac.uk/10.3390/s20143889 - 13 Jul 2020
Cited by 52 | Viewed by 7760
Abstract
In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by [...] Read more.
In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed. Full article
(This article belongs to the Special Issue Semiconductor Sensor)
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