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Micromachines
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Advances in Sensors, Algorithms and Machines for Intelligent Micro- and...
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Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6592

Special Issue Editors

Prof. Dr. Bin Ai

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: intelligent nanosensor system; plasmonic sensors; gas sensors; nanofabrication; artificial intelligence
Prof. Dr. Yongjun Zhang

E-Mail Website
Guest Editor
Center for Advanced Optoelectronic Materials, Key Laboratory of Novel Materials for Sensor of Zhejiang Province, College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: nanomaterials; SERS; LSPR; biomarker; biosensors; detection of environmental pollutants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Studying Sensors, Algorithms, and Machines for Intelligent Micro- and Nano-Systems is essential for advancing technology and improving our daily lives. These systems are designed to be intelligent, meaning that they can perform complex tasks with high accuracy, speed, and efficiency. Sensors are critical in micro- and nano-systems as they detect and measure physical and chemical phenomena at a small scale. Advances in sensor technology have led to miniaturized devices that monitor parameters, such as temperature, pressure, and biological molecules. These sensors integrate with algorithms and machines to create intelligent systems that make decisions and perform actions based on collected data. Algorithms and machines are crucial in intelligent micro- and nano-systems. Machine learning analyzes data to identify patterns and correlations, improving decision-making. Machines, such as robots and drones, perform tasks in hazardous or inaccessible environments, such as space exploration or disaster response. Studying these systems leads to advancements in healthcare, manufacturing, transportation, and environmental monitoring, improving the quality of life worldwide. They enable faster and more accurate diagnoses, efficient production processes, and better natural resource management. Accordingly, this Special Issue invites original research articles, review articles, and communications that address fundamental challenges and opportunities in the design, modeling, and fabrication of intelligent micro- and nano-systems. Topics of interest include, but are not limited to, sensors and actuators, machine learning algorithms, control systems, and applications in healthcare, energy, and environmental monitoring.

Prof. Dr. Bin Ai
Prof. Dr. Yongjun Zhang
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

  • sensor
  • algorithm
  • machine
  • micro-/nano-system

Published Papers (6 papers)

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Research

16 pages, 5460 KiB  
Article
Intelligent Navigation of a Magnetic Microrobot with Model-Free Deep Reinforcement Learning in a Real-World Environment
by Amar Salehi, Soleiman Hosseinpour, Nasrollah Tabatabaei, Mahmoud Soltani Firouz and Tingting Yu
Micromachines 2024, 15(1), 112; https://doi.org/10.3390/mi15010112 - 09 Jan 2024
Viewed by 1200
Abstract
Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot [...] Read more.
Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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13 pages, 3086 KiB  
Article
Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding
by Yueran Qi, Yang Feng, Hai Wang, Chengcheng Wang, Maoying Bai, Jing Liu, Xuepeng Zhan, Jixuan Wu, Qianwen Wang and Jiezhi Chen
Micromachines 2023, 14(12), 2190; https://doi.org/10.3390/mi14122190 - 30 Nov 2023
Viewed by 698
Abstract
To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to implement large-scale sparse coding, wherein various matrix weight training [...] Read more.
To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to implement large-scale sparse coding, wherein various matrix weight training algorithms are verified. Then, with further optimizations of mapping methods and initialization conditions, the variation-sensitive training (VST) algorithm is designed to enhance the processing efficiency and accuracy of the applications of image reconstructions. Based on the comprehensive characterizations observed when considering the impacts of array variations, the experiment demonstrated that the trained dictionary could successfully reconstruct the images in a 55 nm flash memory array based on the proposed architecture, irrespective of current variations. The results indicate the feasibility of using Flash-based CIM architectures to implement high-precision sparse coding in a wide range of applications. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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11 pages, 4403 KiB  
Article
Honeycomb-like Ag Nanocavity Array for SERS Observations Using Plasmon-Mediated Chemical Reactions
by Yongjun Zhang, Zhen Xu, Jiahong Wen, Xiaoyu Zhao, Renxian Gao and Yaxin Wang
Micromachines 2023, 14(10), 1811; https://doi.org/10.3390/mi14101811 - 22 Sep 2023
Viewed by 683
Abstract
Organized two-dimensional polystyrene bead arrays perform ion etching, and protruding nanostructures are created on polystyrene beads due to the shadow effects from the ring beads, leading to nucleus selection and growth in Au nanostructure deposition. Ag nanostructures are prepared via plasmon-mediated chemical reactions [...] Read more.
Organized two-dimensional polystyrene bead arrays perform ion etching, and protruding nanostructures are created on polystyrene beads due to the shadow effects from the ring beads, leading to nucleus selection and growth in Au nanostructure deposition. Ag nanostructures are prepared via plasmon-mediated chemical reactions (PMCRs), leading to the Ag nanocavity geometry of the honeycomb pattern when the etching time and Ag growth time are tuned. Due to the strong electromagnetic coupling, the Ag honeycomb-shaped nanocavity array works as the SERS substrate with high sensitivity and good repeatability, which is used to detect thiram pesticide residues with a concentration down to 10−9 M. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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17 pages, 3239 KiB  
Article
A New Elementary Method for Determining the Tip Radius and Young’s Modulus in AFM Spherical Indentations
by Stylianos Vasileios Kontomaris, Andreas Stylianou, Georgios Chliveros and Anna Malamou
Micromachines 2023, 14(9), 1716; https://doi.org/10.3390/mi14091716 - 31 Aug 2023
Viewed by 1122
Abstract
Atomic force microscopy (AFM) is a powerful tool for characterizing biological materials at the nanoscale utilizing the AFM nanoindentation method. When testing biological materials, spherical indenters are typically employed to reduce the possibility of damaging the sample. The accuracy of determining Young’s modulus [...] Read more.
Atomic force microscopy (AFM) is a powerful tool for characterizing biological materials at the nanoscale utilizing the AFM nanoindentation method. When testing biological materials, spherical indenters are typically employed to reduce the possibility of damaging the sample. The accuracy of determining Young’s modulus depends, among other factors, on the calibration of the indenter, i.e., the determination of the tip radius. This paper demonstrates that the tip radius can be approximately calculated using a single force–indentation curve on an unknown, soft sample without performing any additional experimental calibration process. The proposed method is based on plotting a tangent line on the force indentation curve at the maximum indentation depth. Subsequently, using equations that relate the applied force, maximum indentation depth, and the tip radius, the calculation of the tip radius becomes trivial. It is significant to note that the method requires only a single force–indentation curve and does not necessitate knowledge of the sample’s Young’s modulus. Consequently, the determination of both the sample’s Young’s modulus and the tip radius can be performed simultaneously. Thus, the experimental effort is significantly reduced. The method was tested on 80 force–indentation curves obtained on an agarose gel, and the results were accurate. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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11 pages, 4558 KiB  
Article
A High-Precision Multi-Beam Optical Measurement Method for Cylindrical Surface Profile
by Yinghong Zhou, Zhiliang Wu, Nian Cai, Daohua Zhan, Shaoqiu Xu, Meiyun Chen, Guang Zhou and Han Wang
Micromachines 2023, 14(8), 1555; https://doi.org/10.3390/mi14081555 - 03 Aug 2023
Cited by 1 | Viewed by 906
Abstract
To automatically measure the surface profile of a cylindrical workpiece, a high-precision multi-beam optical method is proposed in this paper. First, some successive images for the cylindrical workpiece’s surface are acquired by a multi-beam angle sensor under different light directions. Then, the light [...] Read more.
To automatically measure the surface profile of a cylindrical workpiece, a high-precision multi-beam optical method is proposed in this paper. First, some successive images for the cylindrical workpiece’s surface are acquired by a multi-beam angle sensor under different light directions. Then, the light directions are estimated based on the feature regions in the images to calculate surface normal vectors. Finally, according to the relationship of the surface normal vector and the vertical section of the workpiece’s surface, a depth map is reconstructed to achieve the curvature surface, which can be employed to measure the curvature radius of the cylindrical workpiece’s surface. Experimental results indicate that the proposed measurement method can achieve good measurement precision with a mean error of the curvature radius of a workpiece’s surface of 0.89% at a reasonable speed of 10.226 s, which is superior to some existing methods. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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11 pages, 4255 KiB  
Article
An Efficient and Robust Partial Differential Equation Solver by Flash-Based Computing in Memory
by Yueran Qi, Yang Feng, Jixuan Wu, Zhaohui Sun, Maoying Bai, Chengcheng Wang, Hai Wang, Xuepeng Zhan, Junyu Zhang, Jing Liu and Jiezhi Chen
Micromachines 2023, 14(5), 901; https://doi.org/10.3390/mi14050901 - 22 Apr 2023
Cited by 1 | Viewed by 1536
Abstract
Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in the partial differential equation (PDE) solver that has been widely utilized in scientific [...] Read more.
Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in the partial differential equation (PDE) solver that has been widely utilized in scientific calculations, high accuracy, processing speed, and low power consumption are the key requirements. This work proposes a novel flash memory-based PDE solver to implement PDE with high accuracy, low power consumption, and fast iterative convergence. Moreover, considering the increasing current noise in nanoscale devices, we investigate the robustness against the noise in the proposed PDE solver. The results show that the noise tolerance limit of the solver can reach more than five times that of the conventional Jacobi CIM solver. Overall, the proposed flash memory-based PDE solver offers a promising solution for scientific calculations that require high accuracy, low power consumption, and good noise immunity, which could help to develop flash-based general computing. Full article
(This article belongs to the Special Issue Advances in Sensors, Algorithms and Machines for Intelligent Micro- and Nano-Systems)
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