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The rapid growth in wearable biosensing devices is driven by the strong desire to monitor the human health data and to predict the symptoms of chronic diseases at an early stage. Different sensors are developed for continuous monitoring of various biomarkers through wearable and implantable sensing patches. Temperature sensor has proved to be an important physiological parameter amongst the various wearable biosensing patches. This paper highlights the recent progresses made in printing of functional nanomaterials for developing wearable temperature sensors on polymeric substrates. A special focus is given to the advanced functional nanomaterials as well as their deposition through printing technologies. The geometric resolutions, shape, physical and electrical characteristics as well as sensing properties using different materials are compared and summarized. Wearability is the main concern of these newly developed sensors, which is summarized by discussing representative examples. Finally, the challenges concerning the stability, repeatability, reliability, sensitivity, linearity, ageing, and large-scale manufacturing are discussed with future outlook of the wearable systems. [ABSTRACT FROM AUTHOR]

In this article, we present a CMOS hybrid magnetic field sensor for real-time motor speed monitoring for smart energy-efficient autonomous systems. The sensor system consists of a micro Hall effect sensor ($\mu $ HES) and a micro search coil magnetometer ($\mu $ SCM) using Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- $\mu \text{m}$ 1P6M CMOS process with a die size of $1.5\times2$ mm. The hybrid sensor is realized completely by the CMOS process without postprocessing. A wide range of rotation speeds can be measured by the presented passive $\mu $ SCM without signal amplification (>600 r/min) and the combination of the $\mu $ SCM and $\mu $ HES on the same chip allows the low rotation speed range measurement (< 600 r/min). Furthermore, the $\mu $ SCM has zero offset and very low-temperature dependency. The magnetic field of a permanent magnet on the rotating shaft of the motor is measured by the $\mu $ SCM and $\mu $ HES to determine the rotation speed. The sensitivity of the $\mu $ HES (at 5-V bias voltage) and the $\mu $ SCM inside a uniform magnetic field are 113.6 and $11.3 \mu \text{V}$ /mT at 1 Hz without amplification, respectively. The noise equivalent magnetic induction (NEMI) of the $\mu $ SCM was calculated and verified experimentally by utilizing a zero-drift instrumentation amplifier. Furthermore, a simplified equivalent circuit model for the $\mu $ SCM is presented and verified experimentally. According to the equivalent circuit model, the linear frequency response region of the $\mu $ SCM is up to 20 kHz, which suits the controlling motors in smart autonomous systems and other low-frequency applications. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) transceivers suffer from optical interference and ambient light saturation, which severely affect the reception of optical signals. In this paper, we discuss various techniques for the suppression of ambient light in electronic, optical, communication and wavelength domains with focus on VLC systems for vehicular communication. Designing transceivers for outdoor VLC systems is challenging due to the dynamic environment and the time-varying nature of interference from different types of ambient light sources. This is a unique study that addresses the problem of optical interference in multiple physical domains and offers qualitative insights in a systematic manner. [ABSTRACT FROM AUTHOR]

In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralow manufacturing cost, the deposited material’s morphology can be accurately controlled with fabricated general-purpose AAO template. Compared with nonstandard lithography-based template fabrication method, the need of complicated Bosch etching process and its associated complex process parameter tuning is eliminated. As a result, the cost-effective mass production of 3-D nanotemplate-based material and devices can be enabled. In addition, the target material’s limited coverage and time efficiency issues widely existing in the previous deposition methods are well-addressed by our customized USP deposition, especially for the 3-D nanotemplate with large surface-to-volume ratio, leading to significantly improved gas-sensing performance. Moreover, the proposed fabrication recipe, together with the adopted gas recognition algorithms based on linear discriminant analysis (LDA), is validated based on the reported extensive measurement results for five gas biomarkers widely exploited for patients’ exhaled gas-sensing and recognition applications. This shows great potential for the early disease diagnose of diabetes, breast cancer, acute lung injury, colon diseases, lung cancer, and so on. [ABSTRACT FROM AUTHOR]

This article presents a low-power column-parallel $\Sigma \Delta $ analog-to-digital converter (ADC) with shared operational transconductance amplifiers (OTAs) for CMOS image sensors (CISs). Through the proposed time division multiplexing of OTAs, the power consumption of the modulator achieves a reduction of about 40%. The proposed structure alleviates the layout requirement of OTAs, enabling implementation of a high-resolution low-power image sensor in advanced CMOS technology nodes. As the coupled noise introduced by the proposed column-shared scheme is suppressed to a lower level than the inherent crosstalk of adjacent pixels, the proposed structure only contributes a negligible inter-column coupled noise from the ADCs. Moreover, a compact digital decimation filter with a single-bit-bit-wise-inversion (BWI) topology is also proposed, which can reduce chip area significantly. The prototype sensor is fabricated in a 40-nm standard CMOS technology with 256 $\times256$ pixel array and 256 proposed column-parallel $\Sigma \Delta $ ADCs. Each $\Sigma \Delta $ ADC occupies a core area of 4.5 $\mu \text{m}\,\,\times $ 310 $\mu \text{m}$ , while consuming a power of 58.8 $\mu \text{W}$. The measured differential nonlinearity (DNL) and integral nonlinearity (INL) are +0.49/−0.65 LSB and +5.1/−4.8 LSB, respectively. Measurement results show a dynamic range of 79.9 dB and an effective-number-of-bit (ENOB) of 11.4 bit. This work achieves a figure of merit (FOM) of 97.2 fJ/step. [ABSTRACT FROM AUTHOR]

In the past few years, a tremendous advancement in the outcome of biomedical circuits and systems has been reported. Unfortunately, at the time of the sudden outbreak of COVID-19, the electronic engineering researchers felt dearth on their side to combat the pandemic, as no such immediate cutting-edge solutions were ready to recognize the virus with some standard and smart electronic devices. Likely, in this paper, a detailed comparative and comprehensive study on circuit architectures of the biomedical devices is presented. Mostly, this study relates the industry standard circuit schemes applicable in non-invasive health monitoring to combat respiratory illnesses. The trending circuit architectural schemes casted-off to tapeout non-invasive health-care devices available in the past literature are meticulously and broadly discussed in this study. Further, the comprehensive comparison of the state of art of the device performance in terms of supply voltage, chip area, sensitivity, dynamic range, etc. is also shown in this paper. The inclusive design processes of the health monitoring devices from Lab to Industry is thoroughly discussed for the readers. The authors think, that this critical review summarising all the trending and most cited health-care devices in a single paper will alternately help the industrialists to adapt and modify the circuit architectures of the health monitoring devices more precisely and straightforwardly. Finally, the demand for health monitoring devices particularly responsible to detect respiratory illnesses, measuring blood pressure and heart-rate is growing widely in the market after the the incident of COVID-19 and other respiratory diseases. [ABSTRACT FROM AUTHOR]

In this brief, we analyzed the output ripple in a capacitively-coupled chopper-based instrumentation amplifier (CCIA). Our analysis found that the shape and amplitude of the output ripple of the CCIA are determined by five design parameters, namely amplifier offset, transconductance, chopping frequency, Miller compensation capacitance, and the nulling resistor. With different design specifications, the CCIA ripple appears in four different shapes, including triangular ripple, RC-settling ripple, square ripple, and step-change ripple. These findings are verified by the silicon results. As the cause of each ripple shape varies, this analysis serves as a guideline for designing practical chopper-based amplifiers and their corresponding ripple reduction techniques. [ABSTRACT FROM AUTHOR]

In this article, we report a wafer-level packaged Pirani vacuum gauge using the proprietary InvenSense CMOS MEMS technology. The micro Pirani vacuum gauge features three serpentine-shaped molybdenum thermistors on the suspended silicon-on-insulator (SOI) bridges, while the wiring gap of each serpentine-shaped silicon microbridge is 1.6 ${ {\mu }}\text{m}$. For the vacuum range of $5\times 10^{-{4}}$ –760 Torr, the CMOS MEMS Pirani gauge configured with a constant temperature interface circuit achieves a sensitivity of 0.414 V/Torr in a very fine vacuum regime, while its heating power is less than 21.3 mW. Moreover, the measured output of the micro Pirani gauge shows good agreement with a semi-empirical model, while the model predicts that the proposed Pirani gauge can measure a vacuum pressure as low as $2.6\times 10^{-{4}}$ Torr. The performance achieved by this Pirani vacuum gauge combined with its high level of integration makes it a promising Internet of Things (IoT) sensing node for vacuum monitoring in the industry. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) presents many hazardous traits such as corrosive, explosive, toxic, and flammable. It is slightly denser than air, and a mixture of H2S and air can be volatile. Therefore, a reliable and robust measurement system is required to effectively detect and quantify H2S in many applications, such as oil and gas industries. There are several methods available in the literature to quantify H2S in fuel gases; however, only a few are available in case of air samples. Furthermore, array-based sensors are more reliable in the detection of volatile organic compounds (VOCs); however, sensor arrays are more expensive and challenging to produce. To overcome the limitations of producing physical sensor arrays, this article proposes a concept of virtual sensing that enables to augment a single sensing platform into a virtual array, thus, increasing the detection accuracy at no extra cost of producing a large physical sensors array. The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H2S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H2S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H2S; and 5) the proposed model is a highly sensitive and reliable H2S gas sensing scheme with the ability to detect the gas instantaneously. The proposed gas quantification model has the advantages of being low cost, easy to implement, and fast operation compared with the analytical solutions. Furthermore, it is extensively tested and validated using real gas data. [ABSTRACT FROM AUTHOR]

In-liquid wave attenuation is the major factor that limits the resolution of acoustic wave biosensors. Existing acoustic wave devices rely on shear mode operation to reduce the mechanical motion transferred to the liquid. However, the energy leakage still exists through acoustic radiation. In this article, we combined phononic metasurface based on local mechanical resonators with shear horizontal surface acoustic wave (SH-SAW) to address the above issue. The shear-horizontal phononic metasurface (SH-PM) resonator is made of periodic high-aspect-ratio electrode (HARE). It produces a slow phononic wave that cannot propagate in water due to its low velocity, which suppresses the acoustic radiation. Furthermore, as the particle motion of the HARE is polarized in shear mode, the SH-PM also reduces the mechanical motion transfer to water. The minimization of the total acoustic energy leakage in the SH-PM results in high quality factor (Q) resonance in water. Besides, the increased surface-to-volume ratio of the HAREs also enhances the mass sensitivity of the device. Experimental results show that the SH-PM resonator achieved an in-liquid Q factor of 230 and a normalized mass sensitivity of $- 192.4~{\textit {cm}}^{2}/{g}$. The high in-liquid Q factor together with the high mass sensitivity makes the SH-PM a promising candidate for high-resolution gravimetric detection of biomolecules. [ABSTRACT FROM AUTHOR]

Both bacterial viability and concentration are significant metrics for bacterial detection. Existing miniaturized and cost-effective single-mode sensor, pH or optical, can only be skilled at detecting single information viability or concentration. This paper presents an inverter-based CMOS ion-sensitive-field-effect-transistor (ISFET) sensor array, featuring bacterial pH detection which is an indicator of viability. The proposed design realizes pH detection using the native passivation layer of CMOS process as a sensing layer and configuring an inverter-based front-end as a capacitive feedback amplifier. This sensor array is assisted by temperature sensing and optical detection which reveals bacterial concentration. The optical detection is enabled using the leakage current of a reset switch as a response to a light source. While in reset mode, the inverter-based amplifier works as a temperature sensor that could help to reduce temperature influences on pH and optical detection. All the functionalities are realized using one single inverter-based amplifier, resulting in a compact pixel structure and largely relaxed design complexity for the sensor system. Fabricated in 0.18 μm standard CMOS process, the proposed CMOS sensor array system achieves an amplified pH sensitivity of 221 mV/pH, an improved sensor resolution of 0.03 pH through systematic noise optimization, a linear optical response, and a maximum temperature error of 0.69° C. The sensing capabilities of the proposed design are demonstrated through on-chip Escherichia coli (E. coli) detection. This study may be extended to a rapid and cost-effective platform that renders multiple information of bacterial samples. [ABSTRACT FROM AUTHOR]

With the emergence of new sensing technologies, soft substrate-based displacement sensing has gained popularity in the last decade. Nevertheless, performance degradation due to noise from unexpected vibrations of the substrate can affect the sensor’s accuracy and lead to poor measurements. Few studies have focused on noise reduction to improve the accuracy of the sensing data. Based on mathematical modeling, this paper compares the two most representative filtering algorithms based on minimum mean square error (MMSE), namely recursive least squares (RLS) and Kalman algorithms for their predictive ability, accuracy, and reliability. Based on the modeling and comparison, we propose a Kalman filtering algorithm to suppress the vibration noise in the displacement-based dosage sensor. The filter-processed data from the sensor’s measurement mainly contains the actual resistance change upon mechanical displacement of the sensor substrate and the undesired noise from its vibration. With the filtering algorithm, the noise can be reduced from the sensor’s measurement, and an accurate correlation between resistance change and displacement of the sensor can be established. This would provide a straightforward sensing signal that reflects the displacement, and in turn, achieves precise dosage monitoring in drug delivery devices, which can be used as a test-bed application for the sensor. [ABSTRACT FROM AUTHOR]

Long term sensors drift is a challenging problem to solve for instruments like an Electronic Nose System (ENS). These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. Moreover, the response of an electronic nose system depends on the variable response of the sensors and a delay is expected in reaching a steady state by the sensors. In this paper, these two problems of ‘sensors long term drift’ and ‘delayed response’ are solved simultaneously to propose a robust and fast electronic nose system, with following merits: (i) only initial transient state features are used in the proposed system without waiting for the sensors to reach a steady state, (ii) a modified boxplot approach is used to handle noisy/drifted data points as a preprocessing step before the classification setup, (iii) a heuristic tree classification approach with optimized transient features is proposed, (iv) the proposed approach only relies on adapted ML methods contrary to the traditional approaches like system recalibration or sensors replacement for handling sensors drift, and (v) the proposed ML model does not require any target domain data and uses only the source domain data for learning the classifier, opposed to the other ML solutions available in the existing literature. The proposed method is tested using a large scale gas sensors drift benchmark dataset available freely on UCI Machine Learning repository and is found better than the existing state-of-the art approaches with an overall accuracy of 87.34%. [ABSTRACT FROM AUTHOR]