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Micromachines
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Artificial Intelligence for Micro/Nano Materials and Devices
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Artificial Intelligence for Micro/Nano Materials and Devices

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 6928

Special Issue Editors

Dr. Saeed Roshani

E-Mail Website
Guest Editor
Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 6718997551, Iran
Interests: microwave design; antennas; artificial intelligence; couplers; power dividers; active and passive circuits
Dr. Sobhan Roshani

E-Mail Website
Guest Editor
Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 6718997551, Iran
Interests: artificial intelligence; microwave design; diplexer; power dividers; active and passive circuits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few decades, the development of Artificial Intelligence (AI) and Machine Learning (ML) has extended across various areas, such as electronics, machines, software engineering, robotics, materials, physics, energy, and many other technology dimensions. Many extended models are improving the practicality and effectiveness of artificial intelligence in engineering and technology. They offer a more powerful system to analyze uncertainty and extract knowledge from big heterogeneous data. Providing more power, nanodevices and nanomaterials are effective architectures for implementing machine learning methods and Artificial Intelligence. Furthermore, statistical, mathematical, and optimization methods have recently been widely used to solve different engineering problems. This Special Issue aims to provide a platform for researchers to discuss the research, developments, and innovations in intelligent approaches in micro/nano materials and devices. 

Dr. Saeed Roshani
Dr. Sobhan Roshani
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

  • artificial intelligence
  • machine learning
  • statistical methods
  • mathematical methods
  • micro/nano materials
  • optimization
  • micromachines
  • robotics
  • power amplifiers
  • micro/nano materials devices
  • micro/nano structures

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Published Papers (4 papers)

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Research

12 pages, 5576 KiB  
Article
Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device
by Manman Wang, Yuhai Yuan and Yanfeng Jiang
Micromachines 2023, 14(10), 1820; https://doi.org/10.3390/mi14101820 - 23 Sep 2023
Cited by 1 | Viewed by 1288
Abstract
As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial [...] Read more.
As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plasticity (STDP) rules. The emergence and growing maturity of spintronic devices present a new approach for constructing the SNN. In the paper, a novel SNN is proposed, in which both the synapse and the neuron are mimicked with the spin transfer torque magnetic tunnel junction (STT-MTJ) device. The synaptic weight is presented by the conductance of the MTJ device. The mapping of the probabilistic spiking nature of the neuron to the stochastic switching behavior of the MTJ with thermal noise is presented based on the stochastic Landau–Lifshitz–Gilbert (LLG) equation. In this way, a simplified SNN is mimicked with the MTJ device. The function of the mimicked SNN is verified by a handwritten digit recognition task based on the MINIST database. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro/Nano Materials and Devices)
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12 pages, 1501 KiB  
Article
Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines
by Shuxia Yan, Fengqi Qian, Chenglin Li, Jian Wang, Xu Wang and Wenyuan Liu
Micromachines 2023, 14(8), 1600; https://doi.org/10.3390/mi14081600 - 14 Aug 2023
Cited by 1 | Viewed by 1151
Abstract
In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), [...] Read more.
In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro/Nano Materials and Devices)
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19 pages, 7121 KiB  
Article
A Compact Rat-Race Coupler with Harmonic Suppression for GSM Applications: Design and Implementation Using Artificial Neural Network
by Salah I. Yahya, Saeed Roshani, Mohammad Ami, Yazeed Yasin Ghadi, Muhammad Akmal Chaudhary and Sobhan Roshani
Micromachines 2023, 14(7), 1294; https://doi.org/10.3390/mi14071294 - 24 Jun 2023
Cited by 6 | Viewed by 2132
Abstract
In this paper, a compact microstrip rat-race coupler at a 950 MHz operating frequency is designed, simulated, and fabricated. New branches are proposed in this design using high-/low- impedance open-ended resonators. In the conventional rat-race coupler, there are three long λ/4 branches and [...] Read more.
In this paper, a compact microstrip rat-race coupler at a 950 MHz operating frequency is designed, simulated, and fabricated. New branches are proposed in this design using high-/low- impedance open-ended resonators. In the conventional rat-race coupler, there are three long λ/4 branches and a 3λ/4 branch, and they occupy a very large area. In the presented designed, three compact branches are proposed for use instead of three λ/4 branches and an ultra-compact branch is suggested for use instead of the 3λ/4 branch. Additionally, an artificial neural network (ANN) approach is incorporated to improve the performance of the resonators using a radial basis function (RBF) network. The proposed compact structure has achieved a reduction of more than 82% compared with the size of the conventional coupler structures. Additionally, the proposed coupler can suppress the 2nd up to the 5th harmonic to improve the performance of the device. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro/Nano Materials and Devices)
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15 pages, 5404 KiB  
Article
A Miniaturized Dual-Band Diplexer Design with High Port Isolation for UHF/SHF Applications Using a Neural Network Model
by Muhammad Akmal Chaudhary, Saeed Roshani and Salman Shabani
Micromachines 2023, 14(4), 849; https://doi.org/10.3390/mi14040849 - 14 Apr 2023
Cited by 6 | Viewed by 1836
Abstract
In this paper, a compact dual-band diplexer is proposed using two interdigital filters. The proposed microstrip diplexer correctly works at 2.1 GHz and 5.1 GHz. In the proposed diplexer, two fifth-order bandpass interdigital filters are designed to pass the desired frequency bands. Applied [...] Read more.
In this paper, a compact dual-band diplexer is proposed using two interdigital filters. The proposed microstrip diplexer correctly works at 2.1 GHz and 5.1 GHz. In the proposed diplexer, two fifth-order bandpass interdigital filters are designed to pass the desired frequency bands. Applied interdigital filters with simple structures pass the 2.1 GHz and 5.1 GHz frequencies and suppress other frequency bands with high attenuation levels. The dimensions of the interdigital filter are obtained using the artificial neural network (ANN) model, constructed from the EM-simulation data. The desired filter and diplexer parameters, such as operating frequency, bandwidth, and insertion loss, can be obtained using the proposed ANN model. The insertion loss parameter of the proposed diplexer is 0.4 dB, and more than 40 dB output port isolation is obtained (for both operating frequencies). The main circuit has the small size of 28.5 mm × 23 mm (0.32 λg × 0.26 λg). The proposed diplexer, with the achieved desired parameters, is a good candidate for UHF/SHF applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro/Nano Materials and Devices)
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