A cryogenic complementary metal-oxide semiconductor (cryo-CMOS) ultra-high-frequency (UHF) analog filter designed for enabling scalable quantum control interfaces is presented in this work. Effective filtering at both analog-to-digital and digital-to-analog conversion stages is critical to maintain signal integrity and achieve high-fidelity qubit control and readout. The proposed filter employs a differential flipped-voltage-follower-based topology, facilitating compact single-branch design, high linearity, and low power consumption. An on-chip buffer stage and a de-embedding technique for precise chip measurements are also presented. The filter is designed using the GlobalFoundries 22nm fully depleted silicon-on-insulator (FD-SOI) technology, leveraging a low-voltage operation of 0.8 V, facilitating low power consumption. A key advantage of using FD-SOI technology for enabling cryogenic quantum computing hardware is the additional degree of freedom provided by the available back-gate transistor connection through which the – increased by the cryogenic (4 K) environment – threshold voltage can be restored. The filter's performance is validated through post-layout simulations, demonstrating improved performance compared to the state-of-the-art literature regarding bandwidth, linearity, and power consumption.

In the realm of neurostimulation, magnetic techniques have emerged as a promising modality for both fundamental neuroscience investigations and the clinical management of neurological disorders, potentially surpassing the limitations associated with conventional electrical stimulation. This study investigated the feasibility of spiral µ-coils for magnetic neurostimulation, employing both computational modeling and experimental validation. Computational modeling demonstrated that varying current amplitudes (1-5 A) across the tested frequency range resulted in average magnetic (B) flux densities ranging from 8.41 to 42.05 mT, sufficient to achieve a neurostimulation depth of approximately 2 cm within the model. Experimental optimisation identified a laser cutting power of 2.6 W. Corresponding measurements of the B-field revealed a spatial gradient, with values ranging from 1.72 µT at the coil's center to 772.26 nT at a radial distance of 25 mm. These results indicate the potential of magnetic neurostimulation for modulating neural activity in subcortical brain regions.

This paper presents an automated, rapid performance-driven design approach for tuning miniaturized, implantable microstrip patch antenna (MPA). The proposed method automates the design process by integrating the simulation platform (e.g., HFSS) with a K-Nearest Neighbors (KNN) based on-patch pixel slot screening process. Using this method, a custom-designed MPA with dimensions of 6 × 6 × 0.635 mm3is proposed, operating at 2.45 GHz for wireless biotelemetry and achieving a wide bandwidth of 770 MHz. Compared to time-consuming full sweep antenna tuning methods, this approach achieves up to a 76% reduction in computing time and resource usage. Additionally, an L-shaped impedance matching circuit is implemented to match the antenna to the source’s characteristic impedance for high power transfer efficiency. The proposed antenna design method, enhanced by AI-driven optimization, offers a highly efficient and user-friendly approach for customized biotelemetry antennas. Integrated with cardiovascular implantable biosensors, the antenna plays a crucial role in enabling reliable wireless communication for the early detection and timely intervention within real-time health monitoring systems.

This brief presents a new method to characterize the linearity of on-chip filters with accurate characterization of the filter’s transfer function (TF) in both its bandpass and stopband. Unlike conventional methods, this approach uses only one buffer, simplifying the design and improving accuracy. The filter and buffer are designed using GlobalFoundries (GF) 22-nm FDX technology, incorporating a back-gate biasing tuning mechanism in the buffer design that aims to maintain the performance of the buffer under process variation. The postlayout simulations demonstrate that the new method achieves a filter linearity of$\text {IIP3}=10.46~\text {dBm}$, with an accuracy of 99.4% compared to the standalone filter’s linearity. Similar consistency is observed across process corners.

An electrocardiogram (ECG) is a medical test that records the electrical activity of the heart over a period of time. This non-invasive and painless test makes ECG an essential tool in cardiology for diagnosing and monitoring heart health. Phonocardiogram (PCG) are typically used as an adjunct to the expertise of healthcare professionals rather than as a standalone diagnostic tool. By applying ECG signals to predict the corresponding PCG signals, no additional test for PCG is needed. These predictions can assist doctors in making informed decisions about patient care and treatment plans by merely collecting ECG signals. Physical reservoir computing (RC), as a bio-inspired algorithm, has received growing research interest. Under the term neuromorphic, physical RC processes the raw signals in the analogue domain with in-memory computing, thus reducing massive power consumption. At the same time, RC is a special type of Recurrent Neural Network (RNN) that is suitable for time-dependent signal processing. The ECG/PCG dataset we used here was experimentally collected by 10 people. We applied a recently proposed physical RC architecture called Rotating Neuron Reservoir and achieved an average NRMSE of 0.3690 for prediction, aiming to propose and construct a neuromorphic processing core for the ECG-to-PCG prediction task.

A systematic research on the development of cryogenic complementary metal-oxide semiconductor (cryo-CMOS) circuits, for implementing the required control electronics to manipulate the quantum bit (qubit) state, is performed over the last few years. Scalability constitutes a key term regarding the evolution of quantum computing from theory to practical application and CMOS technology has been proven to be a promising candidate for implementing the coveted scalable next-generation quantum computers (QCs). Mixed-signal blocks, used for uniting the analog and digital domains, play a key role in the efficient functionality of the qubit control/readout system, thus there is an ever-increasing interest in their high-performance circuit realization. The critical challenge in this venture is to achieve efficient cryogenic operation at low temperatures, i.e., close to the qubit around 4 K, simultaneously keeping power requirements at low values. An overview and comparison of the cryo-CMOS Digital-to-Analog converter (DAC) and Analog-to-Digital converter (ADC) circuit implementations for quantum computing applications that heretofore have been proposed in the literature is presented in this work. A discussion on the challenges and future strategic steps that are henceforth required to proceed toward the development of a functional scalable quantum computer is also conducted.

The increasing demand for considering multisensor data fusion technology has drawn attention for precise human activity recognition (HAR) over standalone technology due to its reliability and robustness. This article presents a framework that fuses data from multiple sensing systems and applies neuromorphic computing to sense and classify human activities. The data is collected by utilizing inertial measurement unit (IMU) sensors, software-defined radios, and radars, and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after two steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to the probabilistic statistics on inference predictions, the proposed method, that is the neuromorphic computing of the three data fused framework, achieved the box plot for the highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that neuromorphic computing is most capable of multisensor data-fusion-based HAR. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data.

Contact lenses have traditionally been used for vision correction applications. Recent advances in microelectronics and nanofabrication on flexible substrates have now enabled sensors, circuits, and other essential components to be integrated on a small contact lens platform. This has opened up the possibility of using contact lenses for a range of human–machine interaction (HMI) applications, including vision assistance, eye tracking, displays, and healthcare. In this article, we systematically review the range of smart contact lens materials, device architectures, and components that facilitate this interaction for different applications. In fact, evidence from our systematic review demonstrates that these lenses can be used to display information, detect eye movements, restore vision, and detect certain biomarkers in tear fluid. Consequently, whereas previous state-of the-art reviews in contact lenses focused exclusively on biosensing, our systematic review covers a wider range of smart contact lens applications in HMI. Moreover, we present a new method of classifying the literature on smart contact lenses according to their six constituent building blocks, which are the sensing, energy management, driver electronics, communications, substrate, and the input/output interfacing modules. Based on recent developments in each of these categories, we speculate the challenges and opportunities of smart contact lenses for HMI. Moreover, based on our analysis of the state-of-the-art, we develop guidelines for the future design of a self-powered smart contact lens concept with integrated energy harvesters, sensors, and communications modules. Therefore, our review is a critical evaluation of current data and is presented with the aim of guiding researchers to new research directions in smart contact lenses.

Integration with the curvilinear, soft, and time-dynamic surfaces of the human body is critical for most implantable and wearable biomedical systems. Devices that can imitate the mechanics of the body provide opportunities to create human-machine interfaces. Additionally, wireless functionality is essential to monitor health/wellness, study disease conditions, and execute other functions. The use of metamaterials in wireless applications is becoming widespread due to its extraordinary properties such as evanescent wave amplification and negative refractive index. This paper studies a soft, flexible and stretchable Complementary Split Ring Resonator (CSRR) metamaterial energy harvester using a volume of 5.6 x 5.6 x 1 mm3on a Polydimethylsiloxane (PDMS) substrate. The CSRR is backed by a ground plane to absorb the incident power, and a via (load) is used to maximize the power harvesting efficiency. For stretchability, a typically rigid patch of the CSRR is replaced by the serpentine mesh. From the ANSYS HFSS simulation, it is found that the serpentine structure helps to reduce the size of the CSRR due to an increase in electrical length. The structure can also achieve high-quality factor (Q-factor), thereby enabling almost unity efficiency. The CSRR metamaterials can be used in future for wireless applications to integrate with the skin, the heart, and the brain.

In the teaching of electronic engineering, some practical projects need to be added in order to connect various courses together. To strengthen the connections between courses, the project-based teaching method proposed in this paper advocates linking the knowledge of different courses through the combination of theory and practice. With this as a guide, projects have been set up for students. One of them is about designing and making a managed ethernet switch. In the process of making and completing this project, the students' ability has been significantly improved, which fully proves the benefits of the teaching method.

This paper presents proof-of-principle of a miniatured low noise, low power, and high-sensitive malaria detection method based on the magnetoresistance (MR) sensor with a CMOS analog front-end (AFE) readout circuit for the detection of paramagnetic hemozoin particles. COMSOL Multiphysics̅ is employed for the finite-element (FEM) simulation of hemozoin particles to prove that the magnetic field generated from a multi-hemozoin particles system is within the sensing range of MR sensors. A CMOS AFE circuit is designed to convert the tiny current (approximately 60 μA) from MR sensors into a strong voltage signal able to be sampled and suppress high frequency and large amplitude noises stemming from the shift of the resultant magnetic field during the malaria diagnostic process. This CMOS AFE circuit is composed of a transimpedance amplifier (TIA) and a pair of Butterworth filters. This TIA can achieve a 98.5 dB dc gain and a 2.823 MHz bandwidth with low power consumption (375.65 μW) at a 3.3 V voltage supply and low input-referred noise (21.3857 nA/√Hz at 100 Hz). Butterworth filters can significantly reduce the high frequency and large amplitude noises caused by the unexpected shift of the magnetic field. The experimental results prove that the system provides an immediate response to samples with hemozoin particles and has the potential to achieve malaria parasite detection.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Nowadays, the field of microelectronics has become the drive for the advancement of the times, which promotes new demands on the cultivation of the students in colleges and universities. In order to keep up with the trend of the global engineering educational reform, three important reforms in education have been in progress step by step, including classroom teaching, innovative training and virtual laboratories. At first, for enhancing and integrating the existing courses related to the circuit, so that the students can comprehend the existing knowledge much more effectively, an important and effective curriculum reform has been performed by combining “Circuit Analysis” and “Analog Circuit Foundation” into one course; Then, innovative training has been carried out to cultivate the team skills among the students; Finally, in consideration of the rapid development of the electrical and electronic experiment, the conventional laboratory equipment may not satisfy the demand of every student due to financial constraints, therefore, the construction of virtual simulation experiment center is an efficient way to break this bottleneck. As a result, the atmosphere of academic innovation of the pursuit of truth, advocacy of science, brave exploration, dare to practice have been formed in colleges and universities through the above innovative engineering education reform.

Cardiovascular disease is the leading cause of global mortality, with 17.5 Million deaths per annum (World Health Authority, WHO). Innovative hardware based cardiac recording devices could help elevate this burden. Delay-based reservoir computing is a novel computational framework with only a single nonlinear node. This feature makes it a strong candidate for the hardware implementation of an analogue cognitive system. Such a system can be exploited to improve the energy efficiency of data processing in implantable bioelectronic devices. This paper presents a system modelling of this network that is capable of cognitively processing Electrocardiograph (ECG) signals from the MIT-BIH arrhythmia database. The proposed single-input single-output model receives an encoded ECG signal while the output amplitude pattern aids the diagnostic interpretation. The information processor is an analogue circuit with the dynamic properties of Mackey-Glass nonlinearity and fading memory. To validate this system and mimic real-time operation, the simulation is designed to detect ventricular ectopic beats, an ectopic heartbeat type, using a continuous ECG signal without any signal segmentation or feature extraction. After training, the model successfully locates ventricular ectopic beat with 87.51% sensitivity and 94.12% accuracy for the testing dataset from three patients.

Brain micromotion is a major contributor to the failure of implantable neural interfaces. Brain micromotions and tissue damage can be effectively reduced in two ways: (i) miniaturization of the implantable device footprint and (ii) choosing flexible materials for the device substrate. To meet these requirements, in this work we perform two sets of modelling using finite element method in COMSOL Multiphysics. First, we model the performance of different materials ranging from stiff (e.g. Silicon) to very soft (e.g. PDMS) with different sizes to find the optimal dimension and material for the microprobe. For the device size optimization, the main degree of freedom is thickness, while the minimum shank width and length depend on the recording sites and the target recording point, respectively. Modelling devices with different thicknesses (50 - 200 μm) and fixed shank width (100 μm) based on different substrates, we show that the Polyimide-based microprobe exhibits a safety factor of 5 to 15 and maximum von mises stress of 248-770 MPa. Further, simulations indicate that the Polyimide-based microprobe of 50 μm thickness, exhibiting safety factor of 5 and stress of 248 MPa, provides the optimal solution in terms of size and material. Second, to analyse the device shape factor, we model different layouts based on the obtained optimal design and find that the optimal layout features von mises stress of 134.123 MPa, which is versatile and suitable to be used as microprobe especially for the brain micromotion effects mitigation purpose.

In this work, a high precision adaptive slope compensation circuit is proposed used in DC-DC converter for System-on-Chip Power Management. Compared with the conventional adaptive slope compensation circuit, this work uses the comparator to sample the output voltage and the input voltage, so that the accuracy has been greatly improved. In this paper, the circuit is designed based on UMC 0.18-μm CMOS Technology and verified by Cadence simulation environment. Simulation results show that, the compensation precision of the slope can reach more than 96% when changes the input voltage with output voltage fixed or changes the output voltage while keeping input voltage constant.

Principle and Application of Microcontroller Unit” is the core course in Circuits and Systems. Microcontroller Unit is widely used in industrial production. Students with basic design ability of MCU system will have good prospects. The teaching of MCU is mainly based on two simulation softwares, Proteus and Keil. They are very helpful in teaching MCU, but the shortcomings are obvious. The simulation can only test the functionality of the circuit and the correctness of the program, and cannot verify the reliability and practicability of the designed system. This paper proposes an experiment-based MCU teaching method, which combines theory with practice to improve students' practical ability and comprehensive ability.

This paper presents a CMOS readout circuit for an integrated and highly-sensitive tunnel-magnetoresistive (TMR) sensor. Based on the characterization of the TMR sensor in the finite-element simulation, using COMSOL Multiphysics, the circuit including a Wheatstone bridge and an analogue front-end (AFE) circuit, were designed to achieve low-noise and low-power sensing. We present a transimpedance amplifier (TIA) that biases and amplifies a TMR sensor array using switched-capacitors external noise filtering and allows the integration of TMR sensors on CMOS without decreasing the measurement resolution. Designed using TSMC 0.18 μm 1V technology, the amplifier consumes 160 nA at 1.8 V supply to achieve a dc gain of 118 dB and a bandwidth of 3.8 MHz. The results confirm that the full system is able to detect the magnetic field in the pico-Tesla range with low circuit noise (2.297 pA/√Hz) and low power consumption (86 μW). A concurrent reduction in the power consumption and attenuation of noise in TMR sensors makes them suitable for long-term deployment in spintronic sensing systems.

This paper presents a capacitive array optimization technique capable to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for smart sensor specifications. Monte Carlo simulation results show that the proposed optimization technique makes the SFDR, SNDR and (Signal-to-Noise Ratio) SNR better definitely concentrated, which means with a spread between maximum and minimum value much smaller than the one obtained by conventional calibration techniques. This gives rise to more stable and better performances. The averaged SFDR improves from 72.9 dB to 91.1 dB with σu = 0.4%, the 18.2 dB improvement required an off-line processing and a small digital logic circuits.

Motivated by the demands of the integrated power system in the modern wearable electronics, this paper presents a new method of inductor-less switched-capacitor (SC) based DC-DC converter designed to produce two simultaneous boost and buck outputs by using a 4-phases logic switch mode regulation. While the existing SC converters missing their reconfigurability during needed spontaneous multi-outputs at the load ends, this work overcomes this limitation by being able to reconfigure higher gain mode at dual outputs. From an input voltage of 2.5 V, the proposed converter achieves step-up and step-down voltage conversions of 3.74 V and 1.233 V for Normal mode, and 4.872 V and 2.48 V for High mode, with the ripple variation of 20-60 mV. The proposed converter has been designed in a standard 0.35 μm CMOS technology and with conversion efficiencies up to 97-98% is in agreement with state-of-the-art SC converter designs. It produces the maximum load currents of 0.21 mA and 0.37 mA for Normal and High modes respectively. Due to the flexible gain accessibility and fast response time with only two clock cycles required for steady state outputs, this converter can be applicable for multi-function wearable devices, comprised of various integrated electronic modules.

This paper presents linearity enhancement capacitor re-configuring technique to improve the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) of ADC simultaneously without sacrificing the sampling rate in a 14-bit successive approximation register (SAR) analog-to-digital converter (ADC) for wearable electronics application. Behavioural Monte-Carlo simulations are presented to demonstrate the effect of the proposed method where no complex least-mean-square (LMS) algorithm. Simulation results show that with a mismatch error typical of modern technology, the SFDR is enhanced by about 18 dB and the SNDR is 15 dB better with the proposed technique for a 14-bit SAR ADC, which makes it suitable for accurate and linear smart sensor nodes in wearable sensing systems.

This paper reports recent advances related to the piezoelectric oxide semiconductor field effect transistor (POS-FET) based touch sensing system research. We reported in past, the POSFETs with basic electronics realized on planar silicon substrates using CMOS technology. However, the planar POSFETs could not be used on 3D or curved surfaces such as the fingertip of a robot. To overcome this challenge we are now investigating the ultra-thin-chip approach for obtaining bendable POSFETs tactile sensing array. This paper presents this approach towards obtaining bendable POSFETs. Furthermore, for the first time the theoretical behavior of POSFETs devices are examined by combining the piezoelectric capacitor model proposed and the physics of underlying metal-oxide-semiconductor (MOS) FETs in the linear and saturation regions. The device characteristic equations are simulated using MATLAB and comparable matching is achieved with the experimental measurements. The model result gives a unique insight into geometrical and material properties of piezoelectric polymer on the electrical properties of transistor for flexible electronics applications. Using this model, the Spice simulation of POSFET device in a single-ended op-amp configuration, and the effect of chip thickness on deflection are presented.

This paper presents the directions for computer aided design, modelling and simulation of bendable MOSFET transistors towards futuristic bendable ICs. In order to compensate the bending stress a generalised geometry variation is discussed. Based on drain-current and threshold-voltage parameters varying under the bending stress, a Verilog-A compact model is proposed and describes I-V characteristics of a MOSFET in a standard 0.18-μm CMOS technology. This model has been compiled into Cadence environment to predict value and orientation of the bending stress. The proposed model validates against macro-model simulation results, and agrees for both the electron and hole conduction. It has been found that there is significant performance advantage in process-induced uniaxial stressed n-MOSFET, exhibiting a smaller drain-current variation and thresh old voltage shift by monitoring the bending stress and changing the supply voltage.

A magnetic Hall sensor working in the current-mode is presented. The proposed sensing device is composed by two Hall plates able to provide a differential current at the output nodes. The sensor, fabricated in a standard 0.18-μm CMOS technology, uses the spinning-current technique to compensate for the offset and obtains a sensitivity IHall/(B⊥Ibias) better than 0.02 T-1for magnetic fields ranging from 0 to 10 mT.