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Micromachines
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Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application
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Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "D1: Semiconductor Devices".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 6264

Special Issue Editors

Dr. Zeinab Ramezani

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33416, USA
Interests: semiconductor physics and devices; nanoelectronics; biosensor; nanomagnetic; neuromorphic computing devices
Special Issues, Collections and Topics in MDPI journals
Dr. Seyed Amir Ghoreishi

E-Mail Website
Co-Guest Editor
Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad. University, Tehran 1477893855, Iran
Interests: artificial intelligence; semiconductor physics and devices; optimal control; time series analysis; biosensors

Special Issue Information

Dear Colleagues,

The fascinating and frequently unrivaled properties of nanoscale materials and devices have enabled the development of new application domains. These platforms have a wide range of applications, including the development of drug materials, biosensors and infrared sensors, spintronic devices, data storage media, magnetic read heads for computer hard disks, single-electron devices, and microwave electronic devices. Nanostructures with diameters ranging from 1 to 100 nm are commonly regarded as tailored precursors to nanostructured materials and their corresponding nanodevices.

This Special Issue aims to present novel insights, recent accomplishments, ground-breaking research, and future trends in the field of semiconductor science, ranging from materials to advanced semiconductor devices. Its scope encompasses a variety of innovative process technologies, including, but not limited to discoveries and cutting-edge technologies in the development of nanoscale FETs, TFETs, 2D and 3D FETs, ferroelectric-gate FET memories, FET biosensors, and nanoscale photonic transistors and devices. 

Dr. Zeinab Ramezani
Dr. Seyed Amir Ghoreishi
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. Micromachines is an international peer-reviewed open access monthly 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

  • nano transistors
  • short channel effects
  • ferroelectric-gate FET memories
  • FET biosensors
  • 3D FETs
  • nanoscale photonic transistors

Published Papers (4 papers)

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Research

17 pages, 3956 KiB  
Article
Positive and Negative Photoconductivity in Ir Nanofilm-Coated MoO3 Bias-Switching Photodetector
by Mohamed A. Basyooni-M. Kabatas, Redouane En-nadir, Khalid Rahmani and Yasin Ramazan Eker
Micromachines 2023, 14(10), 1860; https://doi.org/10.3390/mi14101860 - 28 Sep 2023
Cited by 1 | Viewed by 1012
Abstract
In this study, we delved into the influence of Ir nanofilm coating thickness on the optical and optoelectronic behavior of ultrathin MoO3 wafer-scale devices. Notably, the 4 nm Ir coating showed a negative Hall voltage and high carrier concentration of 1.524 × [...] Read more.
In this study, we delved into the influence of Ir nanofilm coating thickness on the optical and optoelectronic behavior of ultrathin MoO3 wafer-scale devices. Notably, the 4 nm Ir coating showed a negative Hall voltage and high carrier concentration of 1.524 × 1019 cm−3 with 0.19 nm roughness. Using the Kubelka–Munk model, we found that the bandgap decreased with increasing Ir thickness, consistent with Urbach tail energy suggesting a lower level of disorder. Regarding transient photocurrent behavior, all samples exhibited high stability under both dark and UV conditions. We also observed a positive photoconductivity at bias voltages of >0.5 V, while at 0 V bias voltage, the samples displayed a negative photoconductivity behavior. This unique aspect allowed us to explore self-powered negative photodetectors, showcasing fast response and recovery times of 0.36/0.42 s at 0 V. The intriguing negative photoresponse that we observed is linked to hole self-trapping/charge exciton and Joule heating effects. Full article
(This article belongs to the Special Issue Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application)
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10 pages, 2518 KiB  
Article
Novel Method for Image-Based Quantified In Situ Transmission Electron Microscope Nanoindentation with High Spatial and Temporal Resolutions
by Jiabao Zhang, Xudong Yang, Zhipeng Li, Jixiang Cai, Jianfei Zhang and Xiaodong Han
Micromachines 2023, 14(9), 1708; https://doi.org/10.3390/mi14091708 - 31 Aug 2023
Viewed by 933
Abstract
In situ TEM mechanical stages based on micro-electromechanical systems (MEMS) have developed rapidly over recent decades. However, image-based quantification of MEMS mechanical stages suffers from the trade-off between spatial and temporal resolutions. Here, by taking in situ TEM nanoindentation as an example, we [...] Read more.
In situ TEM mechanical stages based on micro-electromechanical systems (MEMS) have developed rapidly over recent decades. However, image-based quantification of MEMS mechanical stages suffers from the trade-off between spatial and temporal resolutions. Here, by taking in situ TEM nanoindentation as an example, we developed a novel method for image-based quantified in situ TEM mechanical tests with both high spatial and temporal resolutions. A reference beam was introduced to the close vicinity of the indenter–sample region. By arranging the indenter, the sample, and the reference beam in a micron-sized area, the indentation depth and load can be directly and dynamically acquired from the relative motion of markers on the three components, while observing the indentation process at a relatively high magnification. No alteration of viewing area is involved throughout the process. Therefore, no deformation events will be missed, and the collection rate of quantification data can be raised significantly. Full article
(This article belongs to the Special Issue Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application)
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10 pages, 3307 KiB  
Article
Disturbance Characteristics of 1T DRAM Arrays Consisting of Feedback Field-Effect Transistors
by Juhee Jeon, Kyoungah Cho and Sangsig Kim
Micromachines 2023, 14(6), 1138; https://doi.org/10.3390/mi14061138 - 28 May 2023
Cited by 4 | Viewed by 1951
Abstract
Challenges in scaling dynamic random-access memory (DRAM) have become a crucial problem for implementing high-density and high-performance memory devices. Feedback field-effect transistors (FBFETs) have great potential to overcome the scaling challenges because of their one-transistor (1T) memory behaviors with a capacitorless structure. Although [...] Read more.
Challenges in scaling dynamic random-access memory (DRAM) have become a crucial problem for implementing high-density and high-performance memory devices. Feedback field-effect transistors (FBFETs) have great potential to overcome the scaling challenges because of their one-transistor (1T) memory behaviors with a capacitorless structure. Although FBFETs have been studied as 1T memory devices, the reliability in an array must be evaluated. Cell reliability is closely related to device malfunction. Hence, in this study, we propose a 1T DRAM consisting of an FBFET with a p+–n–p–n+ silicon nanowire and investigate the memory operation and disturbance in a 3 × 3 array structure through mixed-mode simulations. The 1T DRAM exhibits a write speed of 2.5 ns, a sense margin of 90 μA/μm, and a retention time of approximately 1 s. Moreover, the energy consumption is 5.0 × 10−15 J/bit for the write ‘1’ operation and 0 J/bit for the hold operation. Furthermore, the 1T DRAM shows nondestructive read characteristics, reliable 3 × 3 array operation without any write disturbance, and feasibility in a massive array with an access time of a few nanoseconds. Full article
(This article belongs to the Special Issue Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application)
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9 pages, 1592 KiB  
Article
Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning
by Sola Woo, Juhee Jeon and Sangsig Kim
Micromachines 2023, 14(3), 504; https://doi.org/10.3390/mi14030504 - 21 Feb 2023
Cited by 3 | Viewed by 1847
Abstract
In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of [...] Read more.
In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods. Full article
(This article belongs to the Special Issue Advances in Nanoscale Semiconductor Devices: Design, Fabrication and Application)
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