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Special Issue "Feature Papers of Micromachines in Physics 2023"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 3735

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Prof. Dr. Yi Zhang

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School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: micro/nanomedicine; microfluidics; diagnostics; biosensing; nanomaterials
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Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue, entitled “Feature Papers of Micromachines in Physics 2023”. Over the past several years, we have worked in conjunction with excellent scholars and research groups to publish several high-impact, high-quality manuscripts, which have received a large number of views and citations. Our goal is to publish the latest scientific and technological advances in areas related to the fundamentals and physics of micro/nanoscale multiphysics phenomena and devices (N/MEMS, mechanical and electrical transducers, sensors, actuators, optic devices, photonic devices, optoelectronic devices, micro/nanorobots and so on), in the hopes of providing great contributions to the scientific community.

This Special Issue will be a collection of high-quality papers from excellent scholars around the world, with both original research articles and comprehensive review papers being welcome, published with full open access after peer review, benefiting both authors and readers.

You are welcome to send short proposals for the submission of Feature Papers to our Editorial Office (micromachines@mdpi.com or dikies.zhang@mdpi.com) before submission. The proposals will first be evaluated by Editors, and please note that selected full papers will still be subject to a thorough and rigorous peer review.

We look forward to receiving your excellent work.

Dr. Yi Zhang
Guest Editor

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 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.

Published Papers (4 papers)

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Research

Open AccessArticle

A Compact Polarization MMI Combiner Using Silicon Slot-Waveguide Structures

Omer Brand

Benjamin Wolftson

and

Dror Malka

Micromachines 2023, 14(6), 1203; https://doi.org/10.3390/mi14061203 - 06 Jun 2023

Cited by 3 | Viewed by 710

Abstract

The study of designing a compact transverse electric (TE)/transverse magnetic (TM) polarization multimode interference (MMI) combiner based on silicon slot-waveguide technology is proposed for solving the high demands for high-speed ability alongside more energy power and minimizing the environmental impact of power consumption, achieving a balance between high-speed performance and energy efficiency has become an important consideration in an optical communication system. The MMI coupler has a significant difference in light coupling (beat-length) for TM and TE at 1550 nm wavelength. By controlling the light propagation mechanism inside the MMI coupler, a lower order of mode can be obtained which can lead to a shorter device. The polarization combiner was solved using the full-vectorial beam propagation method (FV-BPM), and the main geometrical parameters were analyzed using Matlab codes. Results show that after a short light propagation of 16.15 μm, the device can function as TM or TE combiner polarization with an excellent extinction ratio of 10.94 dB for TE mode and 13.08 dB for TM mode with low insertion losses of 0.76 dB (TE) and 0.56 dB (TM) and the combiner function well over the C-band spectrum. The polarization combiner also has a robust MMI coupler length tolerance of 400 nm. These attributes make it a good candidate for using this proposed device in photonic integrated circuits for improving power ability at the transmitter system. Full article

(This article belongs to the Special Issue Feature Papers of Micromachines in Physics 2023)

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Open AccessArticle

Relieving Compression-Induced Local Wear on Non-Volatile Memory Block via Sliding Writes

and

Rachata Ausavarungnirun

Micromachines 2023, 14(3), 568; https://doi.org/10.3390/mi14030568 - 27 Feb 2023

Viewed by 755

Abstract

Due to its non-volatility and large capacity, NVM devices gradually take place at various levels of memories. However, their limited endurance is still a big concern for large-scale data centres. Compression algorithms have been used to save NVM space and enhance the efficiency of those lifetime extension methods. However, their own influence on the NVM lifetime is not clear. In order to fully investigate the impact of compression on NVM, this paper first studies bit flips involved in several typical compression algorithms. It is found that more bit flips would happen in the shrunken area of a memory block. This induces the phenomenon of intra-block wear unevenness, which sacrifices NVM lifetime. We propose a new metric called local bit flips to describe this phenomenon. In order to relieve the intra-block wear unevenness caused by compression, this paper proposes a sliding write method named SlidW to distribute the compressed data across the whole memory block. We first divide the memory block into several areas, and then consider five cases about the relationship between new data size and left space. Then, we place the new data according to the case. Comprehensive experimental results show that SlidW can efficiently balance wear and enhance NVM lifetime. Full article

(This article belongs to the Special Issue Feature Papers of Micromachines in Physics 2023)

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Open AccessArticle

Spin Hall Effect of Double-Index Cylindrical Vector Beams in a Tight Focus

Alexey A. Kovalev

and

Victor V. Kotlyar

Micromachines 2023, 14(2), 494; https://doi.org/10.3390/mi14020494 - 20 Feb 2023

Cited by 5 | Viewed by 821

Abstract

We investigate the spin angular momentum (SAM) of double-index cylindrical vector beams in tight focus. Such a set of beams is a generalization of the conventional cylindrical vector beams since the polarization order is different for the different transverse field components. Based on the Richards-Wolf theory, we obtain an expression for the SAM distribution and show that if the polarization orders are of different parity, then the spin Hall effect occurs in the tight focus, which is there are alternating areas with positive and negative spin angular momentum, despite linear polarization of the initial field. We also analyze the orbital angular momentum spectrum of all the components of the focused light field and determine the overwhelming angular harmonics. Neglecting the weak harmonics, we predict the SAM distribution and demonstrate the ability to generate the focal distribution where the areas with the positive and negative spin angular momentum reside on a ring and are alternating in pairs, or separated in different semicircles. Application areas of the obtained results are designing micromachines with optically driven elements. Full article

(This article belongs to the Special Issue Feature Papers of Micromachines in Physics 2023)

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Open AccessArticle

Area-Efficient Mapping of Convolutional Neural Networks to Memristor Crossbars Using Sub-Image Partitioning

Seokjin Oh

Jiyong An

and

Kyeong-Sik Min

Micromachines 2023, 14(2), 309; https://doi.org/10.3390/mi14020309 - 25 Jan 2023

Cited by 1 | Viewed by 1057

Abstract

Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS (complementary metal–oxide–semiconductor) digital circuits. One of the important operations used in neural networks is convolution. For performing the convolution by memristor crossbars, the full image should be partitioned into several sub-images. By doing so, each sub-image convolution can be mapped to small-size unit crossbars, of which the size should be defined as 128 × 128 or 256 × 256 to avoid the line resistance problem caused from large-size crossbars. In this paper, various convolution schemes with 3D, 2D, and 1D kernels are analyzed and compared in terms of neural network’s performance and overlapping overhead. The neural network’s simulation indicates that the 2D + 1D kernels can perform the sub-image convolution using a much smaller number of unit crossbars with less rate loss than the 3D kernels. When the CIFAR-10 dataset is tested, the mapping of sub-image convolution of 2D + 1D kernels to crossbars shows that the number of unit crossbars can be reduced almost by 90% and 95%, respectively, for 128 × 128 and 256 × 256 crossbars, compared with the 3D kernels. On the contrary, the rate loss of 2D + 1D kernels can be less than 2%. To improve the neural network’s performance more, the 2D + 1D kernels can be combined with 3D kernels in one neural network. When the normalized ratio of 2D + 1D layers is around 0.5, the neural network’s performance indicates very little rate loss compared to when the normalized ratio of 2D + 1D layers is zero. However, the number of unit crossbars for the normalized ratio = 0.5 can be reduced by half compared with that for the normalized ratio = 0. Full article

(This article belongs to the Special Issue Feature Papers of Micromachines in Physics 2023)

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