200 research outputs found

    A full-custom digital-signal-processing unit for real-time cortical blood flow monitoring

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    Master'sMASTER OF ENGINEERIN

    Ultra-low Power Circuits for Internet of Things (IOT)

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    Miniaturized sensor nodes offer an unprecedented opportunity for the semiconductor industry which led to a rapid development of the application space: the Internet of Things (IoT). IoT is a global infrastructure that interconnects physical and virtual things which have the potential to dramatically improve people's daily lives. One of key aspect that makes IoT special is that the internet is expanding into places that has been ever reachable as device form factor continue to decreases. Extremely small sensors can be placed on plants, animals, humans, and geologic features, and connected to the Internet. Several challenges, however, exist that could possibly slow the development of IoT. In this thesis, several circuit techniques as well as system level optimizations to meet the challenging power/energy requirement for the IoT design space are described. First, a fully-integrated temperature sensor for battery-operated, ultra-low power microsystems is presented. Sensor operation is based on temperature independent/dependent current sources that are used with oscillators and counters to generate a digital temperature code. Second, an ultra-low power oscillator designed for wake-up timers in compact wireless sensors is presented. The proposed topology separates the continuous comparator from the oscillation path and activates it only for short period when it is required. As a result, both low power tracking and generation of precise wake-up signal is made possible. Third, an 8-bit sub-ranging SAR ADC for biomedical applications is discussed that takes an advantage of signal characteristics. ADC uses a moving window and stores the previous MSBs voltage value on a series capacitor to achieve energy saving compared to a conventional approach while maintaining its accuracy. Finally, an ultra-low power acoustic sensing and object recognition microsystem that uses frequency domain feature extraction and classification is presented. By introducing ultra-low 8-bit SAR-ADC with 50fF input capacitance, power consumption of the frontend amplifier has been reduced to single digit nW-level. Also, serialized discrete Fourier transform (DFT) feature extraction is proposed in a digital back-end, replacing a high-power/area-consuming conventional FFT.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137157/1/seojeong_1.pd

    A Low-Power Silicon-Photomultiplier Readout ASIC for the CALICE Analog Hadronic Calorimeter

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    The future e + e − collider experiments, such as the international linear collider, provide precise measurements of the heavy bosons and serve as excellent tests of the underlying fundamental physics. To reconstruct these bosons with an unprecedented resolution from their multi-jet final states, a detector system employing the particle flow approach has been proposed, requesting calorimeters with imaging capabilities. The analog hadron calorimeter based on the SiPM-on-tile technology is one of the highly granular candidates of the imaging calorimeters. To achieve the compactness, the silicon-photomultiplier (SiPM) readout electronics require a low-power monolithic solution. This thesis presents the design of such an application-specific integrated circuit (ASIC) for the charge and timing readout of the SiPMs. The ASIC provides precise charge measurement over a large dynamic range with auto-triggering and local zero-suppression functionalities. The charge and timing information are digitized using channel-wise analog-to-digital and time-to-digital converters, providing a fully integrated solution for the SiPM readout. Dedicated to the analog hadron calorimeter, the power-pulsing technique is applied to the full chip to meet the stringent power consumption requirement. This work also initializes the commissioning of the calorimeter layer with the use of the designed ASIC. An automatic calibration procedure has been developed to optimized the configuration settings for the chip. The new calorimeter base unit with the designed ASIC has been produced and its functionality has been tested

    Front-ends para LiDAR baseados em ADC e TDC

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    Autonomous vehicles are a promising technology to save over a million lives each year that are lost in road accidents. However, bringing safe autonomous vehicles to market requires massive development, starting with vision sensors. LiDAR is a fundamental vision sensor for autonomous vehicles, as it enables high resolution 3D vision. However, automotive LiDAR is not yet a mature technology, and, also requires massive development in many aspects. This thesis aims to contribute to the maturity of LiDAR, focusing on sampling architectures for LiDAR front-ends. Two architectures were developed. The first is based on a pipelined ADC, available from an AD-FMCDAQ2-EBZ board. The ADC is synchronized with the emitted pulse and able to sample at 1 Gsample/s. The second architecture is based on a TDC that is directly implemented in an FPGA. It relies on a tapped delay line topology comprising 45 delay elements and on a mux-based decoder, resulting in a resolution of 50 ps. Preliminary test results show that both implementations operate correctly, and are both suitable for sampling short pulses typically used by LiDARs. When comparing both architectures, we conclude that an ADC consumes a significant amount of power, and uses many FPGA resources. However, it samples the LiDAR waveform without any loss of information, therefore enabling maximum range and precision. The TDC is just the opposite: it consumes little power, and uses less FPGA resources. However, it only captures one sample per pulse.Os veículos autónomos são uma tecnologia promissora para salvar mais de um milhão de vidas por ano, colhidas por acidentes rodoviários. Contudo, colocar veículos autónomos seguros no mercado requer inúmeros desenvolvimentos, a começar por sensores de visão. O LiDAR é um sensor de visão fundamental para veículos autónomos, pois permite uma visão 3D de alta resolução. Contudo, o LiDAR automotivo não é uma tecnologia madura, e portanto requer também desenvolvimento em vários aspectos. Esta dissertação visa contribuir para a maturidade do LiDAR, com foco em arquiteturas de amostragem para front-ends de LiDAR. Foram desenvolvidas duas arquiteturas. A primeira assenta numa ADC pipelined, por sua vez implementada numa placa de teste AD-FMCDAQ2-EBZ. A ADC opera em sincronismo com o pulso emitido, e permite capturar amostras a 1 Gsample/s. A segunda arquitetura assenta num TDC implementado diretamente numa FPGA. O TDC baseia-se numa topologia tapped delay line com 45 linhas de atraso, e num descodificador à base de multiplexers, permitindo uma resolução temporal de 50 ps. Resultados preliminares mostram que ambas as implementações operam corretamente, e são adequadas para amostrar pulsos curtos tipicamente associados a LiDAR. Em termos comparativos, a arquitectura com base numa ADC tem um consumo de potência considerável e requer uma quantidade significativa de recursos da FPGA. Contudo, esta permite amostrar a forma de onda de LiDAR sem nenhuma perda de informação, permitindo assim alcance e precisão máximos. A arquitectura com base num TDC é exatamente o oposto: tem um baixo consumo de potência e requer poucos recursos da FPGA. Contudo, permite capturar apenas uma amostra por pulso.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

    CMOS Sensors for Time-Resolved Active Imaging

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    In the past decades, time-resolved imaging such as fluorescence lifetime or time-of-flight depth imaging has been extensively explored in biomedical and industrial fields because of its non-invasive characterization of material properties and remote sensing capability. Many studies have shown its potential and effectiveness in applications such as cancer detection and tissue diagnoses from fluorescence lifetime imaging, and gesture/motion sensing and geometry sensing from time-of-flight imaging. Nonetheless, time-resolved imaging has not been widely adopted due to the high cost of the system and performance limits. The research presented in this thesis focuses on the implementation of low-cost real-time time-resolved imaging systems. Two image sensing schemes are proposed and implemented to address the major limitations. First, we propose a single-shot fluorescence lifetime image sensors for high speed and high accuracy imaging. To achieve high accuracy, previous approaches repeat the measurement for multiple sampling, resulting in long measurement time. On the other hand, the proposed method achieves both high speed and accuracy at the same time by employing a pixel-level processor that takes and compresses the multiple samples within a single measurement time. The pixels in the sensor take multiple samples from the fluorescent optical signal in sub-nanosecond resolution and compute the average photon arrival time of the optical signal. Thanks to the multiple sampling of the signal, the measurement is insensitive to the shape or the pulse-width of excitation, providing better accuracy and pixel uniformity than conventional rapid lifetime determination (RLD) methods. The proposed single-shot image sensor also improves the imaging speed by orders of magnitude compared to other conventional center-of-mass methods (CMM). Second, we propose a 3-D camera with a background light suppression scheme which is adaptable to various lighting conditions. Previous 3-D cameras are not operable in outdoor conditions because they suffer from measurement errors and saturation problems under high background light illumination. We propose a reconfigurable architecture with column-parallel discrete-time background light cancellation circuit. Implementing the processor at the column level allows an order of magnitude reduction in pixel size as compared to existing pixel-level processors. The column-level approach also provides reconfigurable operation modes for optimal performance in all lighting conditions. For example, the sensor can operate at the best frame-rate and resolution without the presence of background light. If the background light saturates the sensor or increases the shot noise, the sensor can adjust the resolution and frame-rate by pixel binning and superresolution techniques. This effectively enhances the well capacity of the pixel to compensate for the increase shot noise, and speeds up the frame processing to handle the excessive background light. A fabricated prototype sensor can suppress the background light more than 100-klx while achieving a very small pixel size of 5.9μm.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136950/1/eecho_1.pd

    CMOS SPAD-based image sensor for single photon counting and time of flight imaging

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    The facility to capture the arrival of a single photon, is the fundamental limit to the detection of quantised electromagnetic radiation. An image sensor capable of capturing a picture with this ultimate optical and temporal precision is the pinnacle of photo-sensing. The creation of high spatial resolution, single photon sensitive, and time-resolved image sensors in complementary metal oxide semiconductor (CMOS) technology offers numerous benefits in a wide field of applications. These CMOS devices will be suitable to replace high sensitivity charge-coupled device (CCD) technology (electron-multiplied or electron bombarded) with significantly lower cost and comparable performance in low light or high speed scenarios. For example, with temporal resolution in the order of nano and picoseconds, detailed three-dimensional (3D) pictures can be formed by measuring the time of flight (TOF) of a light pulse. High frame rate imaging of single photons can yield new capabilities in super-resolution microscopy. Also, the imaging of quantum effects such as the entanglement of photons may be realised. The goal of this research project is the development of such an image sensor by exploiting single photon avalanche diodes (SPAD) in advanced imaging-specific 130nm front side illuminated (FSI) CMOS technology. SPADs have three key combined advantages over other imaging technologies: single photon sensitivity, picosecond temporal resolution and the facility to be integrated in standard CMOS technology. Analogue techniques are employed to create an efficient and compact imager that is scalable to mega-pixel arrays. A SPAD-based image sensor is described with 320 by 240 pixels at a pitch of 8μm and an optical efficiency or fill-factor of 26.8%. Each pixel comprises a SPAD with a hybrid analogue counting and memory circuit that makes novel use of a low-power charge transfer amplifier. Global shutter single photon counting images are captured. These exhibit photon shot noise limited statistics with unprecedented low input-referred noise at an equivalent of 0.06 electrons. The CMOS image sensor (CIS) trends of shrinking pixels, increasing array sizes, decreasing read noise, fast readout and oversampled image formation are projected towards the formation of binary single photon imagers or quanta image sensors (QIS). In a binary digital image capture mode, the image sensor offers a look-ahead to the properties and performance of future QISs with 20,000 binary frames per second readout with a bit error rate of 1.7 x 10-3. The bit density, or cumulative binary intensity, against exposure performance of this image sensor is in the shape of the famous Hurter and Driffield densitometry curves of photographic film. Oversampled time-gated binary image capture is demonstrated, capturing 3D TOF images with 3.8cm precision in a 60cm range

    Miniature high dynamic range time-resolved CMOS SPAD image sensors

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    Since their integration in complementary metal oxide (CMOS) semiconductor technology in 2003, single photon avalanche diodes (SPADs) have inspired a new era of low cost high integration quantum-level image sensors. Their unique feature of discerning single photon detections, their ability to retain temporal information on every collected photon and their amenability to high speed image sensor architectures makes them prime candidates for low light and time-resolved applications. From the biomedical field of fluorescence lifetime imaging microscopy (FLIM) to extreme physical phenomena such as quantum entanglement, all the way to time of flight (ToF) consumer applications such as gesture recognition and more recently automotive light detection and ranging (LIDAR), huge steps in detector and sensor architectures have been made to address the design challenges of pixel sensitivity and functionality trade-off, scalability and handling of large data rates. The goal of this research is to explore the hypothesis that given the state of the art CMOS nodes and fabrication technologies, it is possible to design miniature SPAD image sensors for time-resolved applications with a small pixel pitch while maintaining both sensitivity and built -in functionality. Three key approaches are pursued to that purpose: leveraging the innate area reduction of logic gates and finer design rules of advanced CMOS nodes to balance the pixel’s fill factor and processing capability, smarter pixel designs with configurable functionality and novel system architectures that lift the processing burden off the pixel array and mediate data flow. Two pathfinder SPAD image sensors were designed and fabricated: a 96 × 40 planar front side illuminated (FSI) sensor with 66% fill factor at 8.25μm pixel pitch in an industrialised 40nm process and a 128 × 120 3D-stacked backside illuminated (BSI) sensor with 45% fill factor at 7.83μm pixel pitch. Both designs rely on a digital, configurable, 12-bit ripple counter pixel allowing for time-gated shot noise limited photon counting. The FSI sensor was operated as a quanta image sensor (QIS) achieving an extended dynamic range in excess of 100dB, utilising triple exposure windows and in-pixel data compression which reduces data rates by a factor of 3.75×. The stacked sensor is the first demonstration of a wafer scale SPAD imaging array with a 1-to-1 hybrid bond connection. Characterisation results of the detector and sensor performance are presented. Two other time-resolved 3D-stacked BSI SPAD image sensor architectures are proposed. The first is a fully integrated 5-wire interface system on chip (SoC), with built-in power management and off-focal plane data processing and storage for high dynamic range as well as autonomous video rate operation. Preliminary images and bring-up results of the fabricated 2mm² sensor are shown. The second is a highly configurable design capable of simultaneous multi-bit oversampled imaging and programmable region of interest (ROI) time correlated single photon counting (TCSPC) with on-chip histogram generation. The 6.48μm pitch array has been submitted for fabrication. In-depth design details of both architectures are discussed

    Ultra Low Power Circuits for Internet of Things and Deep Learning Accelerator Design with In-Memory Computing

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    Collecting data from environment and converting gathered data into information is the key idea of Internet of Things (IoT). Miniaturized sensing devices enable the idea for many applications including health monitoring, industrial sensing, and so on. Sensing devices typically have small form factor and thus, low battery capacity, but at the same time, require long life time for continuous monitoring and least frequent battery replacement. This thesis introduces three analog circuit design techniques featuring ultra-low power consumption for such requirements: (1) An ultra-low power resistor-less current reference circuit, (2) A 110nW resistive frequency locked on-chip oscillator as a timing reference, (3) A resonant current-mode wireless power receiver and battery charger for implantable systems. Raw data can be efficiently transformed into useful information using deep learning. However deep learning requires tremendous amount of computation by its nature, and thus, an energy efficient deep learning hardware is highly demanded to fully utilize this algorithm in various applications. This thesis also presents a pulse-width based computation concept which utilizes in-memory computing of SRAM.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144173/1/myungjun_1.pd

    Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

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    This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date
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