226 research outputs found

    10-Bit 200 kHz/8-Channel Incremental ADC for Biosensor Applications

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    Design and Implementation of a Low‐Power Wireless Respiration Monitoring Sensor

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    Wireless devices for monitoring of respiration activities can play a major role in advancing modern home-based health care applications. Existing methods for respiration monitoring require special algorithms and high precision filters to eliminate noise and other motion artifacts. These necessitate additional power consuming circuitry for further signal conditioning. This dissertation is particularly focused on a novel approach of respiration monitoring based on a PVDF-based pyroelectric transducer. Low-power, low-noise, and fully integrated charge amplifiers are designed to serve as the front-end amplifier of the sensor to efficiently convert the charge generated by the transducer into a proportional voltage signal. To transmit the respiration data wirelessly, a lowpower transmitter design is crucial. This energy constraint motivates the exploration of the design of a duty-cycled transmitter, where the radio is designed to be turned off most of the time and turned on only for a short duration of time. Due to its inherent duty-cycled nature, impulse radio ultra-wideband (IR-UWB) transmitter is an ideal candidate for the implementation of a duty-cycled radio. To achieve better energy efficiency and longer battery lifetime a low-power low-complexity OOK (on-off keying) based impulse radio ultra-wideband (IR-UWB) transmitter is designed and implemented using standard CMOS process. Initial simulation and test results exhibit a promising advancement towards the development of an energy-efficient wireless sensor for monitoring of respiration activities

    Low-Noise Energy-Efficient Sensor Interface Circuits

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    Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd

    Continuous-Time ΣΔ ADC with Implicit Variable Gain Amplifier for CMOS Image Sensor

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    This paper presents a column-parallel continuous-time sigma delta (CTSD) ADC for mega-pixel resolution CMOS image sensor (CIS). The sigma delta modulator is implemented with a 2nd order resistor/capacitor-based loop filter. The first integrator uses a conventional operational transconductance amplifier (OTA), for the concern of a high power noise rejection. The second integrator is realized with a single-ended inverter-based amplifier, instead of a standard OTA. As a result, the power consumption is reduced, without sacrificing the noise performance. Moreover, the variable gain amplifier in the traditional column-parallel read-out circuit is merged into the front-end of the CTSD modulator. By programming the input resistance, the amplitude range of the input current can be tuned with 8 scales, which is equivalent to a traditional 2-bit preamplification function without consuming extra power and chip area. The test chip prototype is fabricated using 0.18 m CMOS process and the measurement result shows an ADC power consumption lower than 63.5 W under 1.4 V power supply and 50 MHz clock frequency

    DESIGN AND ACCOMPLISHMENT OF SLEEP CONVENTION LOGIC WITH HIGH ACCURACY

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    Sigma-Delta (∑-∆) analog to digital converters are well known for its use in high accuracy wireless communication applications. It is alternative for low power, high resolution (greater than 12 bits) converters, which can be ultimately integrated on digital signal Processor ICs. In this work Over Sampling concept is used to address the problem of power dissipation and noise in ADCs. In this paper a Second order Sigma-Delta Modulator is implemented using CMOS 0.13μm technology using a ±1.2 V power supply. This brief presents a second-order incremental delta–sigma analog-to-digital converter (ADC) for CMOS image sensors (CISs). The ADC that employs a cascade of integrators with a feed forward architecture uses only one operational transconductance amplifier (OTA) by sharing the OTA between the first and second stages of the modulator. Further power and area savings are achieved by using a self-biasing amplifier and the proposed level-shifting technology, which allows active signal summation at the quantize input node without using an additional OTA

    Smart Sensor Networks For Sensor-Neural Interface

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    One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface. In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system. The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption

    Analogue-to-digital conversion and image enhancement using neuron-mos technology

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    This thesis describes the development of two novel circuits that use a newly developed technology, that of neuron-MOS, for the purposes of analogue-to-digital conversion and image enhancement. Neuron-MOS has the potential to reduce both the complexity and number of transistors required for analogue and digital circuits. A reduced area, low transistor-count- analogue-to-digital converter that is suitable for inclusion in a massively parallel array of identical image processing elements is developed. Supporting the function of the array some fundamental image enhancement operations, such as edge enhancement, are examined exploiting the unique features of neuron-MOS technology

    Arrayable Voltage-Controlled Ring-Oscillator for Direct Time-of-Flight Image Sensors

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    Direct time-of-flight (d-ToF) estimation with high frame rate requires the incorporation of a time-to-digital converter (TDC) at pixel level. A feasible approach to a compact implementation of the TDC is to use the multiple phases of a voltage-controlled ring-oscillator (VCRO) for the finest bits. The VCRO becomes central in determining the performance parameters of a d-ToF image sensor. In this paper, we are covering the modeling, design, and measurement of a CMOS pseudo-differential VCRO. The oscillation frequency, the jitter due to mismatches and noise and the power consumption are analytically evaluated. This design has been incorporated into a 64x64-pixel array. It has been fabricated in a 0.18 mu m standard CMOS technology. Occupation area is 28x29 mu m(2) and power consumption is 1.17 mW at 850 MHz. The measured gain of the VCRO is of 477 MHz/V with a frequency tuning range of 53%. Moreover, it features a linearity of 99.4% over a wide range of control frequencies, namely, from 400 to 850 MHz. The phase noise is of -102 dBc/Hz at 2 MHz offset frequency from 850 MHz. The influence of these parameters in the performance of the TDC has been measured. The minimum time bin of the TDC is 147 ps with a rms DNL/INL of 0.13/1.7LSB.Office of Naval Research (USA) N000141410355Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía P12-TIC 233
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