Selected Papers from the 8th International Conference on Sensors and Electronic Instrumentation Advances (SEIA' 2022)

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8384

Special Issue Editor

International Frequency Sensor Association (IFSA), 08860 Castelldefels, Spain
Interests: AI-based smart and intelligent sensors and sensor systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to Data Bridge Market Research analyses, the sensors market will exhibit a CAGR of 10.30% for the forecast period of 2022–2029 and is likely to reach USD 427.61 billion by 2029. Sensors with predictive, intelligent, and safety functions will be in high demand on the market. The sensors market is being driven by the increasing requirement for sensor-rich applications. The developments of sensors that offer precise and accurate measurements have been repeatedly critical in driving demand in novel applications.

This Special Issue contains extended papers from the 8th International Conference on Sensors and Electronic Instrumentation Advances (SEIA' 2022) held on 21–23 September 2022 in Corfu, Greece (https://www.seia-conference.com/).

Topics of interest include but are not limited to:

  • Accelerometers
  • Inclinometers
  • Gyroscopes
  • Mechanical sensors
  • Optical sensors
  • Optical fiber sensors
  • Photonic sensors
  • Chemical sensors
  • Biosensors
  • Immunosensors
  • BioMEMS
  • Temperature sensors
  • Pressure sensors
  • Acoustic sensors
  • Electromagnetic sensors
  • Gas sensors
  • Humidity sensors
  • Infrared sensors, devices, and thermography
  • Radiation sensors
  • Multisensor fusion
  • Smart sensors
  • Intelligent sensors
  • Artificial-intelligence-based sensors and sensor systems
  • Virtual sensors
  • Sensor interfacing and signal conditioning
  • Sensor calibration
  • Nanomaterials and electronics technology for sensors
  • Semiconductor materials for sensors
  • Polymer materials for sensors
  • MEMS and NEMS
  • Quantum sensors
  • Remote sensors and telemetry
  • Sensor applications
  • Sensors and measurements

Dr. Sergey Y. Yurish
Guest Editor

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Keywords

  • sensors
  • transducers
  • electronic devices
  • measuring instrumentation
  • MEMS

Published Papers (5 papers)

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Research

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13 pages, 5022 KiB  
Article
Modeling and Validation of Total Ionizing Dose Effect on the TSVs in RF Microsystem
Micromachines 2023, 14(6), 1180; https://doi.org/10.3390/mi14061180 - 31 May 2023
Viewed by 1002
Abstract
Radio frequency (RF) systems utilizing through-silicon vias (TSVs) have been widely used in the aerospace and nuclear industry, which means that studying the total ionizing dose (TID) effect on TSV structures has become necessary. To investigate the TID effect on TSV structures, a [...] Read more.
Radio frequency (RF) systems utilizing through-silicon vias (TSVs) have been widely used in the aerospace and nuclear industry, which means that studying the total ionizing dose (TID) effect on TSV structures has become necessary. To investigate the TID effect on TSV structures, a 1D TSV capacitance model was established in COMSOL Multiphysics (COMSOL), and the impact of irradiation was simulated. Then, three types of TSV components were designed, and an irradiation experiment based on them was conducted, to validate the simulation results. After irradiation, the S21 degraded for 0.2 dB, 0.6 dB, and 0.8 dB, at the irradiation dose of 30 krad (Si), 90 krad (Si), 150 krad (Si), respectively. The variation trend was consistent with the simulation in the high-frequency structure simulator (HFSS), and the effect of irradiation on the TSV component was nonlinear. With the increase in the irradiation dose, the S21 of TSV components deteriorated, while the variation of S21 decreased. The simulation and irradiation experiment validated a relatively accurate method for assessing the RF systems’ performance under an irradiation environment, and the TID effect on structures similar to TSVs in RF systems, such as through-silicon capacitors. Full article
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16 pages, 3262 KiB  
Article
Effect of Mask Geometry Variation on Plasma Etching Profiles
Micromachines 2023, 14(3), 665; https://doi.org/10.3390/mi14030665 - 16 Mar 2023
Cited by 4 | Viewed by 2030
Abstract
It is becoming quite evident that, when it comes to the further scaling of advanced node transistors, increasing the flash memory storage capacity, and enabling the on-chip integration of multiple functionalities, “there’s plenty of room at the top”. The fabrication of vertical, three-dimensional [...] Read more.
It is becoming quite evident that, when it comes to the further scaling of advanced node transistors, increasing the flash memory storage capacity, and enabling the on-chip integration of multiple functionalities, “there’s plenty of room at the top”. The fabrication of vertical, three-dimensional features as enablers of these advanced technologies in semiconductor devices is commonly achieved using plasma etching. Of the available plasma chemistries, SF6/O2 is one of the most frequently applied. Therefore, having a predictive model for this process is indispensable in the design cycle of semiconductor devices. In this work, we implement a physical SF6/O2 plasma etching model which is based on Langmuir adsorption and is calibrated and validated to published equipment parameters. The model is implemented in a broadly applicable in-house process simulator ViennaPS, which includes Monte Carlo ray tracing and a level set-based surface description. We then use the model to study the impact of the mask geometry on the feature profile, when etching through circular and rectangular mask openings. The resulting dimensions of a cylindrical hole or trench can vary greatly due to variations in mask properties, such as its etch rate, taper angle, faceting, and thickness. The peak depth for both the etched cylindrical hole and trench occurs when the mask is tapered at about 0.5°, and this peak shifts towards higher angles in the case of high passivation effects during the etch. The minimum bowing occurs at the peak depth, and it increases with an increasing taper angle. For thin-mask faceting, it is observed that the maximum depth increases with an increasing taper angle, without a significant variation between thin masks. Bowing is observed to be at a maximum when the mask taper angle is between 15° and 20°. Finally, the mask etch rate variation, describing the etching of different mask materials, shows that, when a significant portion of the mask is etched away, there is a notable increase in vertical etching and a decrease in bowing. Ultimately, the implemented model and framework are useful for providing a guideline for mask design rules. Full article
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15 pages, 3848 KiB  
Article
Thermodynamic Multi-Field Coupling Optimization of Microsystem Based on Artificial Intelligence
Micromachines 2023, 14(2), 411; https://doi.org/10.3390/mi14020411 - 09 Feb 2023
Viewed by 1260
Abstract
An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization [...] Read more.
An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 °C, 55 °C, 35 °C, 180 Mpa and 12 μm, the optimization results are as follows: the peak temperature is 97.90 °C, the bump temperature is 56.01 °C, the TSV temperature is 31.52 °C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 μm. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 μm, the pitch is 65 μm and the thickness of SiO2 is 0.83 μm. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves the efficiency of the optimization design of microsystems, and has great significance for the development of microsystems. Full article
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12 pages, 5476 KiB  
Article
Design of Spectrum Processing Chiplet Based on FFT Algorithm
Micromachines 2023, 14(2), 402; https://doi.org/10.3390/mi14020402 - 07 Feb 2023
Cited by 2 | Viewed by 1704
Abstract
With the rapid development of electronic information and computer science, the fast Fourier transform (FFT) has played an increasingly important role in digital signal processing (DSP). This paper presented a spectrum processing chiplet design method to solve slow speed, low precision, and low [...] Read more.
With the rapid development of electronic information and computer science, the fast Fourier transform (FFT) has played an increasingly important role in digital signal processing (DSP). This paper presented a spectrum processing chiplet design method to solve slow speed, low precision, and low resource utilization in spectrum processing of general-purpose spectrum chips and field programmable gate array (FPGA). To realize signal processing, the Radix-2 4096-point FFT algorithm with pipeline structure is used to process spectral signals extracted from the time domain. To reduce the harm caused by spectrum leakage, a windowing module is added to optimize the input data, and the clock gating unit (CGU) is used to perform low-power management on the entire clock reset. The result shows the chiplet takes 0.368 ms to complete a 4096-point frequency sweep under a clock frequency of 61.44 MHz. The chiplet significantly improves speed and accuracy in spectrum processing, which has great application potential in wireless communication. Full article
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Review

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24 pages, 6018 KiB  
Review
Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems
Micromachines 2023, 14(2), 344; https://doi.org/10.3390/mi14020344 - 29 Jan 2023
Cited by 2 | Viewed by 1540
Abstract
Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks [...] Read more.
Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized. Full article
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