Industrial Electronics Applications Research Articles

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

Occurrence of forever chemicals in Chennai waters, India

BackgroundPer- and polyfluoroalkyl substances (PFAS) are considered ‘contaminants of emerging concern’ due to their environmental persistence, bio-accumulative potential, and adverse effects on human health. They are widely employed in producing various goods used in daily life, such as non-stick cookware, cleaning agents, and many industrial applications in aerospace, automotive, construction, electronics, and military, all of which ultimately end up in different environmental matrices. India’s rapid economic growth necessitates a comprehensive understanding of its PFAS contamination levels and potential human and ecological exposure.ResultsThe present study reports the quantification of one long-chain and two short-chain perfluoroalkyl acids and three long-chain and two short-chain perfluorosulfonic acids and qualitative analysis using suspect screening in the waters of Chennai, Tamil Nadu, India. Samples were collected from surface and groundwater sources, including the Adyar river, Buckingham canal, Chembarambakkam lake, and the water treatment plant. The concentrations analyzed in all the samples ranged from 0.10 ng/L to 136.27 ng/L. Groundwater had some of the highest concentrations of PFAS. L-PFBS (up to 136.27 ng/L) and PFOA (up to 77.61 ng/L) are present in all the samples. The concentrations of all the target PFAS increased in the 5 to 103% range in the final treated water compared to the raw water of the water treatment plant. This treated water is distributed as a source of drinking water for the residents of Chennai. Additionally, the dominance of short-chain compounds (4–7 carbons) over long-chain compounds (> 7 carbons) was observed. Suspect screening revealed numerous precursors and other fluorinated compounds abundantly present in the samples.ConclusionsOur study revealed PFAS levels up to 136.27 ng/L in both surface and groundwater samples from Chennai. These findings raise concerns about potential risks to ecosystems and human well-being. The dominance of short-chain PFAS coupled with abundant precursors and unidentified fluorinated compounds indicates an ongoing shift toward alternatives. Conventional water treatment is ineffective in eliminating these chemicals from the water system; rather, increasing the PFAS concentrations from raw water to treated water necessitates advanced polishing steps. Industrial emissions, untreated domestic wastewater discharge, and open dump sites have been suspected as the significant sources of contamination, highlighting the need for further investigation to fully assess the extent of PFAS contamination in Chennai.Graphical

Digital Electronic System-on-Chip Design: Methodologies, Tools, Evolution, and Trends.

This paper reviews the evolution of methodologies and tools for modeling, simulation, and design of digital electronic system-on-chip (SoC) implementations, with a focus on industrial electronics applications. Key technological, economic, and geopolitical trends are presented at the outset, before reviewing SoC design methodologies and tools. The fundamentals of SoC design flows are laid out. The paper then exposes the crucial role of the intellectual property (IP) industry in the relentless improvements in performance, power, area, and cost (PPAC) attributes of SoCs. High abstraction levels in design capture and increasingly automated design tools (e.g., for verification and validation, synthesis, place, and route) continue to push the boundaries. Aerospace and automotive domains are included as brief case studies. This paper also presents current and future trends in SoC design and implementation including the rising, evolution, and usage of machine learning (ML) and artificial intelligence (AI) algorithms, techniques, and tools, which promise even greater PPAC optimizations.

Recent Advances in MXene-based Composite for Piezoelectric Sensors

Piezoelectric sensors are crucial in medical, industrial, and consumer electronics applications, yet their performance and sensitivity often fall short due to limitations in current piezoelectric materials. To address these deficiencies,...

Growth and Local Structures of Single Crystalline Flakes of Three-Dimensional Covalent Organic Frameworks in Water.

Due to the enormous chemical and structural diversities and designable properties and functionalities, covalent organic frameworks (COFs) hold great promise as tailored materials for industrial applications in electronics, biology, and energy technologies. They were typically obtained as partially crystalline materials, although a few single-crystal three-dimensional (3D) COFs have been obtained recently with structures probed by diffraction techniques. However, it remains challenging to grow single-crystal COFs with controlled morphology and to elucidate the local structures of 3D COFs, imposing severe limitations on the applications and understanding of the local structure-property correlations. Herein, we develop a method for designed growth of five types of single crystalline flakes of 3D COFs with controlled morphology, front crystal facets, and defined edge structures as well as surface chemistry using surfactants that can be self-assembled into layered structures to confine crystal growth in water. The flakes enable direct observation of local structures including monomer units, pore structure, edge structure, grain boundary, and lattice distortion of 3D COFs as well as gradually curved surfaces in kinked but single crystalline 3D COFs with a resolution of up to ∼1.7 Å. In comparison with flakes of two-dimensional crystals, the synthesized flakes show much higher chemical, mechanical, and thermal stability.

F-Type Single Phase Five Level Inverter

In modern industrial and power electronics applications, high-voltage and high-current integrated modular power transistors (IGBTs) are increasingly being utilized. The F-type inverter, which is a modified version of the T-type inverter, has emerged as a promising option. This configuration separates the common point of the bidirectional power switch, reducing voltage stress and resulting in lower inverter costs and losses. The F-type inverter has a simplified design and lower conduction losses, making it a viable choice for medium-voltage applications compared to the clamp diode NPC inverter configuration. Detailed analysis of losses and efficiency has been conducted, and a simulation and experimental prototype have been built to validate the theoretical basis. In addition, the voltage rating of the power switches in the T-type three-level inverter is a limiting factor, as it is only 50% of the input DC voltage for each inverter phase leg.

Silicone Migration Risks and Mitigation in Thermal Materials

Thermal interface materials (TIMs) are ubiquitous in electronics cooling in a wide variety of applications in Industrial, Automotive and Consumer electronics. TIMs comprise of a compliant polymeric matrix with thermally conductive fillers. Silicones are the polymer of first choice due to the special characteristics of thermal stability and ability to fine tune the modulus across a wide range. Silicone migration can be a concern, whether real or through perception. Here we describe various types of silicone migration quantitatively in terms of polymer basics. We will describe analytical methods characterizing migration and provide typical quantitative ranges. We also address what the real risks are for specific applications, quantitative as well as qualitative. Finally, we will describe mitigation measures, if needed, through silicone modification or replacement with other polymers.

A Fifteen Levels Inverter with A Lower Number of Devices and Higher Performance

Multi-level inverters, as a result of the significant contributions they have made to the fields of high voltage and renewable energy applications, MLI has earned a prestigious place in the field of industrial electronics applications. The use of MLI makes it possible to generate an alternating voltage from a DC voltage or from voltages that are continuously applied thanks to this capability. The quality of the produced wave depends on minimizing the level of total harmonic distortion (THD) in the ensuing output voltage. Increasing the total number of levels is required in order to bring down the THD. The bigger the number of layers, the lower the THD. On the other hand, this necessitates an increase in the number of power switches that are utilized, in addition to an increase in the number of DC sources for certain types. A greater number of levels are achieved in this work with a reduced number of switches, and the DC source necessitates the use of specialized control over the switches as well as the grading of the DC source values. In order to demonstrate that the suggested converter achieves the needed outcomes, the MATLAB simulator is utilized.

A Fifteen Levels Inverter with A Lower Number of Devices and Higher Performance

Multi-level inverters, as a result of the significant contributions they have made to the fields of high voltage and renewable energy applications, MLI has earned a prestigious place in the field of industrial electronics applications. The use of MLI makes it possible to generate an alternating voltage from a DC voltage or from voltages that are continuously applied thanks to this capability. The quality of the produced wave depends on minimizing the level of total harmonic distortion (THD) in the ensuing output voltage. Increasing the total number of levels is required in order to bring down the THD. The bigger the number of layers, the lower the THD. On the other hand, this necessitates an increase in the number of power switches that are utilized, in addition to an increase in the number of DC sources for certain types. A greater number of levels are achieved in this work with a reduced number of switches, and the DC source necessitates the use of specialized control over the switches as well as the grading of the DC source values. In order to demonstrate that the suggested converter achieves the needed outcomes, the MATLAB simulator is utilized.

Similarity function for one-shot learning to enhance the flexibility of myoelectric interfaces.

This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (p < 0.01). This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics.

Lithium-Ion Battery Calendar Health Prognostics Based on Knowledge-Data-Driven Attention

In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain knowledge</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</i> , which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement.

Flexomagnetism and vertically graded Néel temperature of antiferromagnetic Cr2O3 thin films

Antiferromagnetic insulators are a prospective materials platform for magnonics, spin superfluidity, THz spintronics, and non-volatile data storage. A magnetomechanical coupling in antiferromagnets offers vast advantages in the control and manipulation of the primary order parameter yet remains largely unexplored. Here, we discover a new member in the family of flexoeffects in thin films of Cr2O3. We demonstrate that a gradient of mechanical strain can impact the magnetic phase transition resulting in the distribution of the Néel temperature along the thickness of a 50-nm-thick film. The inhomogeneous reduction of the antiferromagnetic order parameter induces a flexomagnetic coefficient of about 15 μB nm−2. The antiferromagnetic ordering in the inhomogeneously strained films can persist up to 100 °C, rendering Cr2O3 relevant for industrial electronics applications. Strain gradient in Cr2O3 thin films enables fundamental research on magnetomechanics and thermodynamics of antiferromagnetic solitons, spin waves and artificial spin ice systems in magnetic materials with continuously graded parameters.

3D Characterization of Materials: a Key Enabler in Material Development

The aim of new material development is often to achieve enhanced and reliable performance, and to provide higher customer flexibility, at a lower cost. Characterization of the structure and morphology of those materials is critically important for the optimization of conversion process conditions, and subsequently improve the properties of the materials themselves. MicroComputed Tomography technology (µCT) allows quantitative 3D micro-analysis of a wide range of materials, enabling nondestructive characterization of structure and morphology from sub-micrometric to macroscopic scales. This presentation will include discussion of three SABIC research examples where µCT was a crucial supporting technique for new material development. PVC is a commodity polymer widely used in building &amp; construction, flexible tubing, food packaging, and medical implants. The suspension process used for 95% of the world PVC production leads to the formation of porous powder particles. The porosity is an important aspect of the product properties as it strongly influences the heat transfer efficiency preventing degradation, the free monomer removal required in most applications, and the ability of plasticizer penetration that is essential to processability and molding. The control over process parameters is therefore essential to produce a material suitable for different applications. In the study presented, the particle size and porosity is assessed as a function of the process conditions. (or: This study describes investigation of particle size and porosity and their relationship to process conditions. Foam is a type of polymer material which finds applications in many industrial segments, including packaging, mass transportation, consumer electronics and automotive. Foam combines interesting lightweight solutions with unique mechanical performance. However, the material performance is strongly influenced by the foam structure, including cell volume and wall thickness, which ultimately result from the processing conditions. The 3D characterization of foam morphology by µCT is particularly well suited to guide the optimization of process conditions and improve material performance , especially impact and compression properties. Finally, technology trends in material development include the development of organic or organic hybrid materials such as polymeric fiber reinforcement of a polymer matrix. Not only are the mechanical properties enhanced but further functionality can be added to the material, such as anti-dripping characteristics which are critical to obtain high FR performance. However, the fibrillation efficiency strongly depends on the processing conditions, which leads to challenges in the robustness of the process and the performance reliability of the material. The characterization of the morphology of such materials remains challenging in terms of spatial resolution and contrast generation. A microscopic approach combining µCT and SEM is developed, providing new insights on the formation of a 3D network of nano-fibrils. The 3D characterization of polymer materials by X-ray micro-Computed Tomography has proven to be a useful tool allowing further understanding the material process-structure-properties relationship.

Synthesis and physical characterization of magnetron sputtered Graphene-CdS bilayer

CdS/Graphene Nano composites have been extetinsively investigated in the field of basic industrial research and electronic device applications because of their unique physical, chemical properties and photo stability under visible‐light irradiation. In this study, we explore the electrical properties of Cadmium sulfide with the addition of graphene. CdS/Graphene hybrid was fabricated by simple RF magnetron sputtering method using CdS as a sputtering source. The hybrid material formation and structural properties of Graphene, CdS, CdS/Graphene have been discussed using XRD, FTIR, Raman, and UV–vis spectroscopy techniques. Herein, we present a facile and efficient method for hybridization of CdS Nano-sphere with graphene Nano sheet and subsequent investigation of enhancement of current of the hybrid material. Field emission scanning electron microscopy (FESEM) micrographs reveal the formation of CdS nanospheres and homogeneous scattering on the surface of graphene sheets. The UV absorption spectrum of CdS/Graphene hybrid presented a red-shifted. The enhancement in the current of CdS/Graphene hybrid has been observed due to the generation of electron-hole pairs. Also, current-voltage (I-V) characteristics of an as-grown thin film of the hybrid are conducted using 4-point probe measurement and revealed their semiconducting nature with a drastic enhancement of electrical conductivity.

A Three-Dimensional Discontinuous Galerkin Time-Domain Finite Element Method for Electromagnetic Modeling of Wireless Power Transfer Coils

Inductive coupling based wireless power transfer (WPT) is a popular short-range power delivery mechanism for many industrial, biomedical, and home electronic appliances applications. A numerical methodology is needed for the analysis of the electromagnetic propagation, radiation, scattering and coupling of highly efficient WPT systems. This study is based on the discontinuous Galerkin time-domain (DGTD) finite element method. A brief survey of the DGTD method is given, which is a finite element method using discontinuous piecewise polynomials as basis functions and numerical fluxes based on up-winding for stability. DGTD method is characterized by the fact that no continuity is enforced between the elements, then it is easy to parallelize and results in fast solvers. Even though the finite element method is used by a few researchers to study WPT problems, we found no study using the DGTD method to study WPT problems, which is surprising given that this discretization technique seems particularly well suited for these problems. A design of two coils at the frequency of 3 MHz is introduced, and the effects of the distance and misalignment between two coils on the mutual coupling are studied. The numerical results are validated by experimental and analytical results.

Thermoplastic Polymer Compounding with ZnS Nanostructures: Innovations and Applications

This study investigates the compounding of thermoplastic polymers with ZnS nanostructures, focusing on innovative synthesis methods and applications. The principal objective was to enhance the mechanical, thermal, and electrical properties of thermoplastic polymers by incorporating well-characterized ZnS nanoparticles. Utilizing a chemical precipitation method, ZnS nanoparticles with controlled size and high purity were synthesized. Characterization techniques, including XRD, SEM, TEM, EDS, and FTIR, confirmed the crystalline structure and elemental composition of the nanoparticles. Three compounding techniques—melt blending, solution casting, and in-situ polymerization—were explored for integrating ZnS nanoparticles into thermoplastic matrices. Each method was optimized for uniform nanoparticle dispersion and strong interfacial bonding. The findings revealed that nanocomposites produced via these techniques exhibited significantly enhanced tensile strength, impact resistance, thermal stability, and electrical conductivity compared to pure thermoplastics. The study's implications extend to industrial applications in electronics, automotive components, aerospace parts, and advanced packaging materials. The environmental benefits, such as reduced material consumption and improved recyclability, along with potential economic savings, underscore the broader impact of these advanced materials. This research provides a solid foundation for future development of high-performance ZnS-thermoplastic nanocomposites, promoting innovation in material science and industrial applications.

Demonstration of Synaptic Connections with Unipolar Junction Transistor based Neuron Emulators

Neuron emulator circuits can be used for teaching and proving concepts. Such emulators should be made with cheap and off-the-shelf components. There are bipolar and MOSFET transistor-based neuron emulator circuits heavily used in literature. Opamp-based neuron emulators are also commonly used. Such circuits provide simple and cheap solution instead of using microcontroller-based neuron emulators if many neurons are to be used in the studies such as showing circadian resonance. Unipolar junction transistor (UJT) is commonly used in industrial electronics applications. It provides a cheap timing circuit. Although there are a few UJT-based artificial neuron patents, we were unable to find research articles on UJT-based artificial neurons. In this study, we examined a simple network of UJT-based artificial neurons and show their spiking and bursting behavior with synaptic connections between neurons. It is shown that the firing rate of a UJT-neuron can be increased by utilizing spikes generated by another one with simulations. This behavior represents excitatory connectivity between two neurons.

Physical Study of SiC Power MOSFETs Towards HTRB Stress Based on C-V Characteristics

The quality of the gate-oxide and Oxide/SiC interfaces is one of the crucial issues in the implementation of silicon carbide (SiC) Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) in the industrial power electronic applications. The main goal of this work is to investigate the gate-oxide integrity and to understand the basic phenomena involved on 4H-SiC MOSFET by the mean of Capacitance-Voltage (C-V) characterizations. The paper presents HTRB (High Temperature Reverse Bias) test results on the second and third generations of SiC MOSFETs. The C-V measurements are compared to physical simulation results. The good agreement between 2D numerical simulations and measurements suggests failures related to acceptor interface traps and doping concentration variations.

Behavioral Acoustic Emanations: Attack and Verification of PIN Entry Using Keypress Sounds.

This paper explores the security vulnerability of Personal Identification Number (PIN) or numeric passwords. Entry Device (PEDs) that use small strings of data (PINs, keys or passwords) as means of verifying the legitimacy of a user. Today, PEDs are commonly used by personnel in different industrial and consumer electronic applications, such as entry at security checkpoints, ATMs and customer kiosks, etc. In this paper, we propose a side-channel attack on a 4–6 digit random PIN key, and a PIN key user verification method. The intervals between two keystrokes are extracted from the acoustic emanation and used as features to train machine-learning models. The attack model has a 60% chance to recover the PIN key. The verification model has an 88% accuracy on identifying the user. Our attack methods can perform key recovery by using the acoustic side-channel at low cost. As a countermeasure, our verification method can improve the security of PIN entry devices.

Comprehensive study of finite control set model predictive control algorithms for power converter control in microgrids

Advances in power electronics and digital control open a new horizon in the control of power converters. Particularly, model predictive control has been developed for control applications in industrial electronics and power systems. This study presents a comprehensive study on recent achievements of model predictive control algorithms to overcome the challenges in the real-time implementation of power converter control, which is the lowest level control of hierarchical control in microgrids. The study shows that most of these alternate solutions can enhance system reliability, stability, and efficiency. The control platform devices for the real-time implementation of these algorithms are compared. The related issues are discussed and classified, respectively. Finally, a summary is provided, leading to some further research questions and future work.