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Special Issue "Advanced Interface Circuits for Sensor Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 17741

Special Issue Editors

Dr. Pak Kwong Chan
E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Interests: sensor interface ICs; smart sensor systems; low-energy low-noise circuit design; sensor power management ICs; PVT-insensitive circuits and systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Holden King-Ho Li
E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Interests: MEMS sensors; MOEMS sensors; CMOS-MEMS integration; sensor reliability and long term performance; sensor design for manufacturability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors are widely used in healthcare, automobiles, consumer electronics, industrial applications, and so forth. With that demand, comes an expectation of high quality and intelligent sensor circuits. In an era of digital technology, digital-aware or software-aware sensor circuits becomes the design agenda. With the emergence of artificial intelligence (AI) Internet-of-Things (AI-IoT) as one of the driving forces, this has posed further design criteria and constraints to the interface circuits that aim to enhance the detection capability through the assistance of AI. It turns out that the interface circuits, which may be coupled with intelligent peripheral support circuits, are to provide advanced features to meet high-performance-aware specifications or high-adaptability, etc. and, therefore, realize smart functions through smart program control. These design concerns impose design challenges and cause a paradigm shift in the design approach to designers or researchers in the emerging field. This Special Issue will publish papers that explore the potential solutions to tackle the stated issues. Potential topics will not be limited to the following:

  • High-performance interface circuits
  • AI-IoT sensor circuit design and techniques
  • Smart power management circuits for sensors
  • Sensor circuits with configurable architectures
  • Intelligent peripheral circuits for sensor circuits
  • Software for sensor circuits
  • Digital-assisted sensor circuits
  • Data communication for sensor circuits
Prof. Dr. Pak Kwong Chan
Prof. Dr. Holden King-Ho Li
Guest Editors

Manuscript Submission Information

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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. Sensors is an international peer-reviewed open access semimonthly 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 2400 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

  • readout circuits
  • interface circuit techniques
  • AI-IoT
  • power management
  • system architectures
  • sensor peripheral circuits
  • data communication
  • digital-assisted design
  • software-assisted design

Published Papers (11 papers)

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Research

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Article
Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator
Sensors 2022, 22(5), 1957; https://doi.org/10.3390/s22051957 - 02 Mar 2022
Cited by 1 | Viewed by 1144
Abstract
Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust [...] Read more.
Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
A CMOS PSR Enhancer with 87.3 mV PVT-Insensitive Dropout Voltage for Sensor Circuits
Sensors 2021, 21(23), 7856; https://doi.org/10.3390/s21237856 - 25 Nov 2021
Viewed by 1033
Abstract
A new power supply rejection (PSR) based enhancer with small and stable dropout voltage is presented in this work. It is implemented using TSMC-40 nm process technology and powered by 1.2 V supply voltage. A number of circuit techniques are proposed in this [...] Read more.
A new power supply rejection (PSR) based enhancer with small and stable dropout voltage is presented in this work. It is implemented using TSMC-40 nm process technology and powered by 1.2 V supply voltage. A number of circuit techniques are proposed in this work. These include the temperature compensation for Level-Shifted Flipped Voltage Follower (LSFVF) and the Complementary-To-Absolute Temperature (CTAT) current reference. The typical output voltage and dropout voltage of the enhancer is 1.1127 V and 87.3 mV, respectively. The Monte-Carlo simulation of this output voltage yields a mean T.C. of 29.4 ppm/°C from −20 °C and 80 °C. Besides, the dropout voltage has been verified with good immunity against Process, Temperature and Process (PVT) variation through the worst-case simulation. Consuming only 4.75 μA, the circuit can drive load up to 500 μA to yield additional PSR improvement of 36 dB and 20 dB of PSR at 1 Hz and 1 MHz, respectively for the sensor circuit of interest. This is demonstrated through the application of an enhancer on the instrumentation Differential Difference Amplifier (DDA) for sensing floating bridge sensor signal. The comparative Monte-Carlo simulation results on a respective DDA circuit have revealed that the process sensitivity of output voltage of this work has achieved 14 times reduction in transient metrics with respect to that of the conventional counterpart over the operation temperature range in typical operation condition. Due to simplicity without voltage reference and operational amplifier(s), low power and small consumption of supply voltage headroom, the proposed work is very useful for supply noise sensitive analog or sensor circuit applications. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport
Sensors 2021, 21(17), 5711; https://doi.org/10.3390/s21175711 - 25 Aug 2021
Cited by 2 | Viewed by 1296
Abstract
Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors [...] Read more.
Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
A 3.0 Gsymbol/s/lane MIPI C-PHY Receiver with Adaptive Level-Dependent Equalizer for Mobile CMOS Image Sensor
Sensors 2021, 21(15), 5197; https://doi.org/10.3390/s21155197 - 31 Jul 2021
Viewed by 1952
Abstract
A 3.0 Gsymbol/s/lane receiver is proposed herein to acquire near-grounded high-speed signals for the mobile industry processor interface (MIPI) C-PHY version 1.1 specification used for CMOS image sensor interfaces. Adaptive level-dependent equalization is also proposed to improve the signal integrity of the high-speed [...] Read more.
A 3.0 Gsymbol/s/lane receiver is proposed herein to acquire near-grounded high-speed signals for the mobile industry processor interface (MIPI) C-PHY version 1.1 specification used for CMOS image sensor interfaces. Adaptive level-dependent equalization is also proposed to improve the signal integrity of the high-speed receivers receiving three-level signals. The proposed adaptive level-dependent equalizer (ALDE) is optimized by adjusting the duty cycle ratio of the clock recovered from the received data to 50%. A pre-determined data pattern transmitted from a MIPI C-PHY transmitter is established to perform the adaptive level-dependent equalization. The proposed MIPI C-PHY receiver with three data lanes is implemented using a 65 nm CMOS process with a 1.2 V supply voltage. The power consumption and area of each lane are 4.9 mW/Gsymbol/s/lane and 0.097 mm2, respectively. The proposed ALDE improves the peak-to-peak time jitter of 12 ps and 34 ps, respectively, for the received data and the recovered clock at a symbol rate of 3 Gsymbol/s/lane. Additionally, the duty cycle ratio of the recovered clock is improved from 42.8% to 48.3%. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Communication
A Soft-Error-Tolerant SAR ADC with Dual-Capacitor Sample-and-Hold Control for Sensor Systems
Sensors 2021, 21(14), 4768; https://doi.org/10.3390/s21144768 - 13 Jul 2021
Cited by 4 | Viewed by 1446
Abstract
For a reliable and stable sensor system, it is essential to precisely measure various sensor signals, such as electromagnetic field, pressure, and temperature. The measured analog signal is converted into digital bits through the sensor readout system. However, in extreme radiation environments, such [...] Read more.
For a reliable and stable sensor system, it is essential to precisely measure various sensor signals, such as electromagnetic field, pressure, and temperature. The measured analog signal is converted into digital bits through the sensor readout system. However, in extreme radiation environments, such as in space, during flights, and in nuclear fusion reactors, the performance of the analog-to-digital converter (ADC) constituting the sensor readout system can be degraded due to soft errors caused by radiation effects, leading to system malfunction. This paper proposes a soft-error-tolerant successive-approximation-register (SAR) ADC using dual-capacitor sample-and-hold (S/H) control, which has robust characteristics against total ionizing dose (TID) and single event effects (SEE). The proposed ADC was fabricated using 65-nm CMOS process, and its soft-error-tolerant performance was measured in radiation environments. Additionally, the proposed circuit techniques were verified by utilizing a radiation simulator CAD tool. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Communication
Design of a Microwave Power Detection System in the 5G-Communication Frequency Band
Sensors 2021, 21(8), 2674; https://doi.org/10.3390/s21082674 - 10 Apr 2021
Viewed by 1086
Abstract
At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power [...] Read more.
At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
Two-Capacitor Direct Interface Circuit for Resistive Sensor Measurements
Sensors 2021, 21(4), 1524; https://doi.org/10.3390/s21041524 - 22 Feb 2021
Cited by 3 | Viewed by 1224
Abstract
Direct interface circuits (DICs) avoid the need for signal conditioning circuits and analog-to-digital converters (ADCs) to obtain digital measurements of resistive sensors using only a few passive elements. However, such simple hardware can lead to quantization errors when measuring small resistance values as [...] Read more.
Direct interface circuits (DICs) avoid the need for signal conditioning circuits and analog-to-digital converters (ADCs) to obtain digital measurements of resistive sensors using only a few passive elements. However, such simple hardware can lead to quantization errors when measuring small resistance values as well as high measurement times and uncertainties for high resistances. Different solutions to some of these problems have been presented in the literature over recent years, although the increased uncertainty in measurements at higher resistance values is a problem that has remained unaddressed. This article presents an economical hardware solution that only requires an extra capacitor to reduce this problem. The circuit is implemented with a field-programmable gate array (FPGA) as a programmable digital device. The new proposal significantly reduces the uncertainty in the time measurements. As a result, the high resistance errors decreased by up to 90%. The circuit requires three capacitor discharge cycles, as is needed in a classic DIC. Therefore, the time to estimate resistance increases slightly, between 2.7% and 4.6%. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
A Self-Powered Hybrid SSHI Circuit with a Wide Operation Range for Piezoelectric Energy Harvesting
Sensors 2021, 21(2), 615; https://doi.org/10.3390/s21020615 - 17 Jan 2021
Cited by 5 | Viewed by 2706
Abstract
This paper presents a piezoelectric (PE) energy harvesting circuit, which integrates a Synchronized Switch Harvesting on Inductor (SSHI) circuit and a diode bridge rectifier. A typical SSHI circuit cannot transfer the power from a PE cantilever into the load when the rectified voltage [...] Read more.
This paper presents a piezoelectric (PE) energy harvesting circuit, which integrates a Synchronized Switch Harvesting on Inductor (SSHI) circuit and a diode bridge rectifier. A typical SSHI circuit cannot transfer the power from a PE cantilever into the load when the rectified voltage is higher than a certain voltage. The proposed circuit addresses this problem. It uses the two resonant loops for flipping the capacitor voltage and energy transfer in each half cycle. One resonant loop is typically used for the parallel SSHI scheme, and the other for the series SSHI scheme. The hybrid SSHI circuit using the two resonant loops enables the proposed circuit’s output voltage to no longer be limited. The circuit is self-powered and has the capability of starting without the help of an external battery. Eleven simple discrete components prototyped the circuit. The experimental results show that, compared with the full-bridge (FB) circuit, the amount of power harvested from a PE cantilever and the Voltage Range of Interest (VRI) of the proposed circuit is increased by 2.9 times and by 4.4 times, respectively. A power conversion efficiency of 83.2% is achieved. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Article
Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks
Sensors 2020, 20(15), 4222; https://doi.org/10.3390/s20154222 - 29 Jul 2020
Cited by 1 | Viewed by 1924
Abstract
Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 μm/±2.5 [...] Read more.
Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 μm/±2.5 V complementary metal oxide semiconductor (CMOS) process, which are comprised of digital-to-analog converter (DAC)-based multipliers and phase shifters. The results from the measurement convinces us that our implementation attains the correct function and good performance. Furthermore, we propose the multi-layer perceptron (MLP) by utilizing analog perceptron where the structure and neurons as well as weights can be flexibly configured. The example given is to design a 2-3-4 MLP circuit with rectified linear unit (ReLU) activation, which consists of 2 input neurons, 3 hidden neurons, and 4 output neurons. Its experimental case shows that the simulated performance achieves a power dissipation of 200 mW, a range of working frequency from 0 to 1 MHz, and an error ratio within 12.7%. Finally, to demonstrate the feasibility and effectiveness of our analog perceptron for configuring a MLP, seven more analog-based MLPs designed with the same approach are used to analyze the simulation results with respect to various specifications, in which two cases are used to compare to their digital counterparts with the same structures. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Letter
A Real-Time Thermal Monitoring System Intended for Embedded Sensors Interfaces
Sensors 2020, 20(19), 5657; https://doi.org/10.3390/s20195657 - 03 Oct 2020
Cited by 5 | Viewed by 1422
Abstract
This paper proposes a real-time thermal monitoring method using embedded integrated sensor interfaces dedicated to industrial integrated system applications. Industrial sensor interfaces are complex systems that involve analog and mixed signals, where several parameters can influence their performance. These include the presence of [...] Read more.
This paper proposes a real-time thermal monitoring method using embedded integrated sensor interfaces dedicated to industrial integrated system applications. Industrial sensor interfaces are complex systems that involve analog and mixed signals, where several parameters can influence their performance. These include the presence of heat sources near sensitive integrated circuits, and various heat transfer phenomena need to be considered. This creates a need for real-time thermal monitoring and management. Indeed, the control of transient temperature gradients or temperature differential variations as well as the prediction of possible induced thermal shocks and stress at early design phases of advanced integrated circuits and systems are essential. This paper addresses the growing requirements of microelectronics applications in several areas that experience fast variations in high-power density and thermal gradient differences caused by the implementation of different systems on the same chip, such as the new-generation 5G circuits. To mitigate adverse thermal effects, a real-time prediction algorithm is proposed and validated using the MCUXpresso tool applied to a Freescale embedded sensor board to monitor and predict its temperature profile in real time by programming the embedded sensor into the FRDM-KL26Z board. Based on discrete temperature measurements, the embedded system is used to predict, in advance, overheating situations in the embedded integrated circuit (IC). These results confirm the peak detection capability of the proposed algorithm that satisfactorily predicts thermal peaks in the FRDM-KL26Z board as modeled with a finite element thermal analysis tool (the Numerical Integrated elements for System Analysis (NISA) tool), to gauge the level of local thermomechanical stresses that may be induced. In this paper, the FPGA implementation and comparison measurements are also presented. This work provides a solution to the thermal stresses and local system overheating that have been a major concern for integrated sensor interface designers when designing integrated circuits in various high-performance technologies or harsh environments. Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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Brief Report
A High-Efficiency Driver Circuit for a Gas-Sensor Microheater Based on a Switch-Mode DC-to-DC Converter
Sensors 2020, 20(18), 5367; https://doi.org/10.3390/s20185367 - 19 Sep 2020
Cited by 1 | Viewed by 1397
Abstract
Low power consumption is one of the critical factors for successful Internet of Things (IoT) applications. In such applications, gas sensors have become a main source of power consumption because energy conversion efficiency of the microheater is relative over a wide range of [...] Read more.
Low power consumption is one of the critical factors for successful Internet of Things (IoT) applications. In such applications, gas sensors have become a main source of power consumption because energy conversion efficiency of the microheater is relative over a wide range of operating temperatures. To improve the energy-conversion efficiency of gas-sensor microheaters, this paper proposes integrated switch-mode DC-to-DC power converter technology which we compare with traditional driving methods such as pulse-width modulation and the linear mode. The results indicate that energy conversion efficiency with this proposed method remains over 90% from 150 °C to 400 °C when using a 3.0, 4.2 and 5.0 V power supply. Energy-conversion efficiency increases by 1–74% compared with results obtained using the traditional driving methods, and the sensing film still detects alcohol and toluene at 200 °C and 280 °C, respectively, with high energy conversion efficiency. These results show that the proposed method is useful and should be further developed to drive gas-sensor microheaters, and then integrated into the circuits of the complementary metal-oxide-semiconductor micro electro mechanical systems (CMOS-MEMS). Full article
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
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