A synapse with low power consumption based on MoTe2/SnS2heterostructure.

The use of two-dimensional materials and van der Waals heterostructures holds great potential for improving the performance of memristors Here, we present SnS2/MoTe2heterostructure synaptic transistors. Benefiting from the ultra-low dark current of the heterojunction, the power consumption of the synapse is only 19pJ per switching under 0.1 V bias, comparable to that of biological synapses. The synaptic device based on the SnS2/MoTe2demonstrates various synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation. In particular, the synaptic weight of the excitatory postsynaptic current can reach 109.8%. In addition, the controllability of the long-term potentiation and long-term depression are discussed. The dynamic range (Gmax/Gmin) and the symmetricity values of the synaptic devices are approximately 16.22 and 6.37, and the non-linearity is 1.79. Our study provides the possibility for the application of 2D material synaptic devices in the field of low-power information storage.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Low-Power and Wireless Communication Research on Underground Displacement Three-Dimensional Monitoring System.

Underground displacement monitoring is a crucial means of preventing geological disasters. Compared to existing one-dimensional methods (measuring only horizontal or vertical displacement), the underground displacement three-dimensional measurement method and monitoring system proposed by the author's research team can more accurately reflect the internal movement of rock and soil mass, thereby improving the timeliness and accuracy of geological disaster prediction. To ensure the reliability and long-term operation of the underground displacement three-dimensional monitoring system, this article further introduces low-power design theory and Bluetooth wireless transmission technology into the system. By optimizing the power consumption of each sensing unit, the current during the sleep period of a single sensing unit is reduced to only 0.09 mA. Dynamic power management technology is employed to minimize power consumption during each detection cycle. By using Bluetooth wireless transmission technology, the original wired communication of the system is upgraded to a relay-type wireless network communication, effectively solving the problem of the entire sensing array's operation being affected when a single sensing unit is damaged. These optimized designs not only maintain monitoring accuracy (horizontal and vertical displacement errors not exceeding 1 mm) but also enable the monitoring system to operate stably for an extended period under harsh weather conditions.

Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications.

In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.

Self-Powered Long-Life Microsystem for Vibration Sensing and Target Recognition.

Microsystems play an important role in the Internet of Things (IoT). In many unattended IoT applications, microsystems with small size, lightweight, and long life are urgently needed to achieve covert, large-scale, and long-term distribution for target detection and recognition. This paper presents for the first time a low-power, long-life microsystem that integrates self-power supply, event wake-up, continuous vibration sensing, and target recognition. The microsystem is mainly used for unattended long-term target perception and recognition. A composite energy source of solar energy and battery is designed to achieve self-powering. The microsystem's sensing module, circuit module, signal processing module, and transceiver module are optimized to further realize the small size and low-power consumption. A low-computational recognition algorithm based on support vector machine learning is designed and ported into the microsystem. Taking the pedestrian, wheeled vehicle, and tracked vehicle as targets, the proposed microsystem of 15 cm3 and 35 g successfully realizes target recognitions both indoors and outdoors with an accuracy rate of over 84% and 65%, respectively. Self-powering of the microsystem is up to 22.7 mW under the midday sunlight, and 11 min self-powering can maintain 24 h operation of the microsystem in sleep mode.

A Design of 10-Bit Asynchronous SAR ADC with an On-Chip Bandgap Reference Voltage Generator.

A proposed prototype of a 10-bit 1 MS/s single-ended asynchronous Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) with an on-chip bandgap reference voltage generator is fabricated with 130 nm technology. To optimize the power consumption, static, and dynamic performance, several techniques have been proposed. A dual-path bootstrap switch was proposed to increase the linearity sampling. The Voltage Common Mode (VCM)-based Capacitive Digital-to-Analog Converter (CDAC) switching technique was implemented for the CDAC part to alleviate the switching energy problem of the capacitive DAC. The proposed architecture of the two-stage dynamic latch comparator provides high speed and low power consumption. Moreover, to achieve faster bit conversion with an efficient time sequence, asynchronous SAR logic with an internally generated clock is implemented, which avoids the requirement of a high-frequency external clock, as all conversions are carried out in a single clock cycle. The proposed error amplifier-based bandgap reference voltage generator provides a stable reference voltage to the ADC for practical implementation. The measurement results of the proposed SAR ADC, including an on-chip bandgap reference voltage generator, show an Effective Number of Bits (ENOB) of 9.49 bits and Signal-to-Noise and Distortion Ratio (SNDR) of 58.88 dB with 1.2 V of power supply while operating with a sampling rate of 1 MS/s.

Analog Sensor Interface for Field Mill Sensors in Atmospheric Applications.

An overview of the electric field mill sensor specifications in applications related to the measurement of the atmospheric electric field was conducted. The different design approaches of the field mill sensor interface are presented and analyzed, while the sensitivity-related parameters of a field mill are discussed. The design of a non-complex analog sensor interface that can be employed for the measurement of the electric field in both fair and foul weather conditions, such as thunderstorms, is implemented using discrete components for experimental validation and is optimized in an integrated version in terms of noise and power consumption. Advanced noise simulations are conducted in a 180 nm CMOS process (XH018 XFAB). The energy-autonomous operation of the sensor for extended periods of time is made feasible due to the low power consumption of the front-end circuitry (165 µW at 3 V) as well as the proposed intermittent style of operation of the motor. The total sensing system is low power, and its realization is simple and cost-effective, while also offering adequate sensitivity (45 mV/kV/m), making it comparable to the existing works.

Low power consumption photoelectric coupling perovskite memristor with adjustable threshold voltage.

Organic-inorganic halide perovskites (OHPs) have been proven to possess unique optical and electrical properties, and achieved more extensive application as excellent materials for memristors in recent years. Based on the traditional OHP-based memristors, the intermediate layer of the memristor was prepared using yttrium oxide (Y2O3)/OHP stacking structure in this manuscript. The potential barrier between Y2O3and perovskite is relatively high (ΔEC = 2.13 eV) which leads to comparatively low current of the memristor, thus the power consumption can be reduced. Besides, by changing the external light conditions, one can realize sharp or slow switch between high resistance state (HRS) and low resistance state (LRS), so as to meet the requirement of multilevel data storage, which indicates its promising application prospect in information storage and biological simulation. In addition, based on characteristics of photoelectric coupling, the Y2O3/OHP memristor can also achieve the advantage of adjustable threshold voltage. The transition of HRS and LRS can be realized by changing the illumination condition at any voltage, which means the set and reset voltage are not fixed, so that the memristor with adjustable threshold voltage can adapt to various working conditions.

Flexible fabric gas sensors based on reduced graphene-polyaniline nanocomposite for highly sensitive NH3detection at room temperature.

A flexible fabric gas sensor for the detection of sub-ppm-level NH3is reported in this paper. The reduced graphene oxide (rGO)-polyaniline (PANI) nanocomposite was successfully coated on cotton thread via anin situpolymerization technique. The morphology, microstructure and composition were analyzed by field-emission scanning electron microscope, x-ray diffraction, Fourier transform infrared spectroscopy and Raman spectroscopy. Furthermore, we have studied the responses of the rGO-PANI nanocomposite-based flexible sensors for the detection of NH3varying from 1-100 ppm, operated at 22 °C. At the optimized concentration of rGO, the response of these sensors increased by 4-5 times in comparison with the pristine rGO and PANI. These flexible sensors exhibited fast response, remarkable long-term stability, good selectivity and a low detection limit. The sensing mechanism for the high sensing performance has been thoroughly discussed and it is mainly due to the distinctive 1D fiber structure, the formation of a p-p heterojunction between the rGO nanosheets and PANI. The rGO-PANI composite-based fabric sensor with low power consumption is a potential flexible electronic device for the detection of NH3.

High performance and low power consumption resistive random access memory with Ag/Fe2O3/Pt structure.

Due to magnetic field tunability and the abundance of iron in the Earth's crust, iron oxide-based resistive random access memory (RRAM) is considered to be low cost and potential for multi-level storage. However, the relatively high operation voltage (>1 V) and small storage window (<100) limit its application. In this work, the devices with simple Ag/Fe2O3/Pt structure exhibit typical bipolar resistive switching with ultralow set voltage (Vset) of 0.16 V, ultralow reset voltage (Vreset) of -0.04 V, high OFF/ON resistance ratio of 103, excellent cycling endurance more than 104and good retention time longer than 104s. Each major parameter has about an order of magnitude improvement compared to the previous data. The devices demonstrate outstanding stable low power consumption quality. Based on the analysis of the experimental results, a percolation model of silver ion migration was established and confirmed that low operation voltage is attributed to the amorphous oxide layer with large porosity. During electrical testing, the compliance current (Ic) and maximum reset voltage (Vmax) can also affect the device performance. This discovery suggests Fe2O3memristor has significant potential for application and provides a new idea for the realization of high-performance low-power RRAM.

Acoustic-Field Beamforming for Low-Power Portable Ultrasound.

Portable ultrasound has been extensively used for diagnostic applications in health monitoring, emergency rooms, and ambulances. However, these handheld ultrasound systems may suffer from heat and battery issues attributed to the large power consumption of the transmitter. Additionally, the largest portion of the direct current (DC) power consumption can be attributed to the amplifier in the digital-to-analog converter (DAC) of the transmitter and to the analog-to-digital converter (ADC) of the receiver. Therefore, the number of transmit/receive channels in a portable ultrasound instrument is one of the crucial design factors regarding heat and battery related issues. To address these problems, we propose an acoustic-field beamforming (AFB) technique for low-power portable ultrasound systems with a single receive and five transmit channels. Finally, the simulation, experimental, and in vivo results verified the feasibility of this approach.

A 0.8 V, 5.3-5.9 GHz Sub-Sampling PLL with 196.5 fsrms Integrated Jitter and -251.6 dB FoM.

This paper proposes a hybrid dual path sub-sampling phase-locked loop (SSPLL), including a proportional path (P-path) and an integral path (I-path), with 0.8 V supply voltage. A differential master-slave sampling filter (MSSF), replacing the sub-sampling charge pump (SSCP), composed the P-path to avoid the degraded feature caused by the decreasing of the supply voltage. The I-path is built by a rail-to-rail SSCP to suppress the phase noise of the voltage-controlled oscillator (VCO) and avoid the trouble of locking at the non-zero phase offset (as in type-I PLL). The proposed design is implemented in a 40-nm CMOS process. The measured output frequency range is from 5.3 to 5.9 GHz with 196.5 fs root mean square (RMS) integrated jitter and -251.6 dB FoM.

On-CMOS Image Sensor Processing for Lane Detection.

This paper presents a CMOS image sensor (CIS) with built-in lane detection computing circuits for automotive applications. We propose on-CIS processing with an edge detection mask used in the readout circuit of the conventional CIS structure for high-speed lane detection. Furthermore, the edge detection mask can detect the edges of slanting lanes to improve accuracy. A prototype of the proposed CIS was fabricated using a 110 nm CIS process. It has an image resolution of 160 (H) × 120 (V) and a frame rate of 113, and it occupies an area of 5900 µm × 5240 µm. A comparison of its lane detection accuracy with that of existing edge detection algorithms shows that it achieves an acceptable accuracy. Moreover, the total power consumption of the proposed CIS is 9.7 mW at pixel, analog, and digital supply voltages of 3.3, 3.3, and 1.5 V, respectively.

An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused on analyzing mobile GPS data to accomplish this task. While this approach may guarantee high accuracy from the perspective of the data, it is considered inefficient since knowing the object's absolute geographic location is not required to accomplish this task. This work proposed the implementation of the unsupervised learning-based algorithm, namely convolutional autoencoder, to infer the co-location of people from a low-power consumption sensor data-magnetometer readings. The idea is that if the trained model can also reconstruct the other data with the structural similarity (SSIM) index being above 0.5, we can then conclude that the observed individuals were co-located. The evaluation of our system has indicated that the proposed approach could recognize the spatial co-location of people from magnetometer readings.

A Wearable Low-Power Sensing Platform for Environmental and Health Monitoring: The Convergence Project.

The low-power sensing platform proposed by the Convergence project is foreseen as a wireless, low-power and multifunctional wearable system empowered by energy-efficient technologies. This will allow meeting the strict demands of life-style and healthcare applications in terms of autonomy for quasi-continuous collection of data for early-detection strategies. The system is compatible with different kinds of sensors, able to monitor not only health indicators of individual person (physical activity, core body temperature and biomarkers) but also the environment with chemical composition of the ambient air (NOx, COx, NHx particles) returning meaningful information on his/her exposure to dangerous (safety) or pollutant agents. In this article, we introduce the specifications and the design of the low-power sensing platform and the different sensors developed in the project, with a particular focus on pollutant sensing capabilities and specifically on NO2 sensor based on graphene and CO sensor based on polyaniline ink.

A Low-Power 12-Bit 20 MS/s Asynchronously Controlled SAR ADC for WAVE ITS Sensor Based Applications.

A low power 12-bit, 20 MS/s asynchronously controlled successive approximation register (SAR) analog-to-digital converter (ADC) to be used in wireless access for vehicular environment (WAVE) intelligent transportation system (ITS) sensor based application is presented in this paper. To optimize the architecture with respect to power consumption and performance, several techniques are proposed. A switching method which employs the common mode charge recovery (CMCR) switching process is presented for capacitive digital-to-analog converter (CDAC) part to lower the switching energy. The switching technique proposed in our work consumes 56.3% less energy in comparison with conventional CMCR switching method. For high speed operation with low power consumption and to overcome the kick back issue in the comparator part, a mutated dynamic-latch comparator with cascode is implemented. In addition, to optimize the flexibility relating to the performance of logic part, an asynchronous topology is employed. The structure is fabricated in 65 nm CMOS process technology with an active area of 0.14 mm2. With a sampling frequency of 20 MS/s, the proposed architecture attains signal-to-noise distortion ratio (SNDR) of 65.44 dB at Nyquist frequency while consuming only 472.2 µW with 1 V power supply.

A High-Efficiency Driver Circuit for a Gas-Sensor Microheater Based on a Switch-Mode DC-to-DC Converter.

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).

An Adaptive Genetic Algorithm of Adjusting Sensor Acquisition Frequency.

Portable meteorological stations are widely applied in environment monitoring systems, but they are always limited in power-supplying due to no cable power, especially in long-term monitoring scenarios. Reducing power consumption by adjusting a suitable frequency of sensor acquisition is very important for wireless sensor nodes. The regularity of historical environment data from a monitoring system is analyzed, and then an optimization model of an adaptive genetic algorithm for environment monitoring data acquisition strategies is proposed to lessen sampling frequency. According to the historical characteristics, the algorithm dynamically changes the recent data acquisition frequency so as to collect data with a smaller acquisition frequency, which will reduce the energy consumption of the sensor. Experiment results in a practical environment show that the algorithm can greatly reduce the acquisition frequency, and can obtain the environment monitoring data changing curve with less error compared with the high-frequency acquisition of fixed frequency.

Electrically Transduced Gas Sensors Based on Semiconducting Metal Oxide Nanowires.

Semiconducting metal oxide-based nanowires (SMO-NWs) for gas sensors have been extensively studied for their extraordinary surface-to-volume ratio, high chemical and thermal stabilities, high sensitivity, and unique electronic, photonic and mechanical properties. In addition to improving the sensor response, vast developments have recently focused on the fundamental sensing mechanism, low power consumption, as well as novel applications. Herein, this review provides a state-of-art overview of electrically transduced gas sensors based on SMO-NWs. We first discuss the advanced synthesis and assembly techniques for high-quality SMO-NWs, the detailed sensor architectures, as well as the important gas-sensing performance. Relationships between the NWs structure and gas sensing performance are established by understanding general sensitization models related to size and shape, crystal defect, doped and loaded additive, and contact parameters. Moreover, major strategies for low-power gas sensors are proposed, including integrating NWs into microhotplates, self-heating operation, and designing room-temperature gas sensors. Emerging application areas of SMO-NWs-based gas sensors in disease diagnosis, environmental engineering, safety and security, flexible and wearable technology have also been studied. In the end, some insights into new challenges and future prospects for commercialization are highlighted.

Design of an Edge-Detection CMOS Image Sensor with Built-in Mask Circuits.

In this paper, we propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that has built-in mask circuits to selectively capture either edge-detection images or normal 8-bit images for low-power computer vision applications. To detect the edges of images in the CIS, neighboring column data are compared in in-column memories after column-parallel analog-to-digital conversion with the proposed mask. The proposed built-in mask circuits are implemented in the CIS without a complex image signal processer to obtain edge images with high speed and low power consumption. According to the measurement results, edge images were successfully obtained with a maximum frame rate of 60 fps. A prototype sensor with 1920 × 1440 resolution was fabricated with a 90-nm 1-poly 5-metal CIS process. The area of the 4-shared 4T-active pixel sensor was 1.4 × 1.4 µm2, and the chip size was 5.15 × 5.15 mm2. The total power consumption was 9.4 mW at 60 fps with supply voltages of 3.3 V (analog), 2.8 V (pixel), and 1.2 V (digital).