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

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

Fundamental Limit of Analog Multiplication in Linear Discriminant Classifier

In this thesis analog implementation of a machine learning algorithm, Linear Discriminant Analysis, is analyzed and shown how it performs on a classification problem. Analog machine learning has emerged as a promising field that provides advantages over its digital counterpart in power consumption, circuit area and scalability. Analog computation achieves its efficiency from the physics of device or circuit operation. This allows analog computation to operate on very low signal levels. However, low signal levels make itself vulnerable to noise. Excessive noise levels can render the machine learning system unstable and prone to making wrong decisions. To ensure reasonable accuracy of the system it is essential to understand how noise behaves and propagates along the system.A key component in analog implementation of the Linear Discriminant Analysis is the analog multiplier. A noise analysis is done for the multiplier to show how noise varies with multiplication factor. This also produces a relationship between signal to noise ratio and energy consumption that gives us a limit of accuracy obtained from the multiplier for a given energy consumption. Numerical analysis is provided to show that Linear Discriminant Analysis is well suited for the classification problem. The performance of a hardware implementation of the analog classifier in commercially available 130nm silicon process is also presented. With four feature input currents and three classes to classify the classifier consumes around 4nW of power. The testing process shows that the classifier is able to perform basic classification task in the presence of noise

Linearization of Time-encoded ADCs Architectures for Smart MEMS Sensors in Low Power CMOS Technology

Mención Internacional en el título de doctorIn the last few years, the development of mobile technologies and machine learning applications has increased the demand of MEMS-based digital microphones. Mobile devices have several microphones enabling noise canceling, acoustic beamforming and speech recognition. With the development of machine learning applications the interest to integrate sensors with neural networks has increased. This has driven the interest to develop digital microphones in nanometer CMOS nodes where the microphone analog-front end and digital processing, potentially including neural networks, is integrated on the same chip. Traditionally, analog-to-digital converters (ADCs) in digital microphones have been implemented using high order Sigma-Delta modulators. The most common technique to implement these high order Sigma-Selta modulators is switchedcapacitor CMOS circuits. Recently, to reduce power consumption and make them more suitable for tasks that require always-on operation, such as keyword recognition, switched-capacitor circuits have been improved using inverter-based operational amplifier integrators. Alternatively, switched-capacitor based Sigma- Delta modulators have been replaced by continuous time Sigma-Delta converters. Nevertheless, in both implementations the input signal is voltage encoded across the modulator, making the integration in smaller CMOS nodes more challenging due to the reduced voltage supply. An alternative technique consists on encoding the input signal on time (or frequency) instead of voltage. This is what time-encoded converters do. Lately, time-encoding converters have gained popularity as they are more suitable to nanometer CMOS nodes than Sigma-Delta converters. Among the ones that have drawn more interest we find voltage-controlled oscillator based ADCs (VCOADCs). VCO-ADCs can be implemented using CMOS inverter based ring oscillators (RO) and digital circuitry. They also show noise-shaping properties. This makes them a very interesting alternative for implementation of ADCs in nanometer CMOS nodes. Nevertheless, two main circuit impairments are present in VCO-ADCs, and both come from the oscillator non-idealities. The first of them is the oscillator phase noise, that reduces the resolution of the ADC. The second is the non-linear tuning curve of the oscillator, that results in harmonic distortion at medium to high input amplitudes. In this thesis we analyze the use of time encoding ADCs for MEMS microphones with special focus on ring oscillator based ADCs (RO-ADCs). Firstly, we study the use of a dual-slope based SAR noise shaped quantizer (SAR-NSQ) in sigma-delta loops. This quantizer adds and extra level of noise-shaping to the modulator, improving the resolution. The quantizer is explained, and equations for the noise transfer function (NTF) of a third order sigma-delta using a second order filter and the NSQ are presented. Secondly, we move our attention to the topic of RO-ADCs. We present a high dynamic range MEMS microphone 130nm CMOS chip based on an open-loop VCO-ADC. This dissertation shows the implementation of the analog front-end that includes the oscillator and the MEMS interface, with a focus on achieving low power consumption with low noise and a high dynamic range. The digital circuitry is left to be explained by the coauthor of the chip in his dissertation. The chip achieves a 80dBA peak SNDR and 108dB dynamic range with a THD of 1.5% at 128 dBSPL with a power consumption of 438μW. After that, we analyze the use of a frequency-dependent-resistor (FDR) to implement an unsampled feedback loop around the oscillator. The objective is to reduce distortion. Additionally phase noise mitigation is achieved. A first topology including an operational amplifier to increase the loop gain is analyzed. The design is silicon proven in a 130 nm CMOS chip that achieves a 84 dBA peak SNDR with an analog power consumption of 600μW. A second topology without the operational amplifier is also analyzed. Two chips are designed with this topology. The first chip in 130 nm CMOS is a full VCO-ADC including the frequencyto- digital converter (F2D). This chip achieves a peak SNDR of 76.6 dBA with a power consumption of 482μW. The second chip includes only the oscillator and is implemented in 55nm CMOS. The peak SNDR is 78.15 dBA and the analog power consumption is 153μW. To finish this thesis, two circuits that use an FDR with a ring oscillator are presented. The first is a capacity-to-digital converter (CDC). The second is a filter made with an FDR and an oscillator intended for voice activity detection tasks (VAD).En los últimos años, el desarrollo de las tecnologías móviles y las aplicaciones de machine-learning han aumentado la demanda de micrófonos digitales basados en MEMS. Los dipositivos móviles tienen varios micrófonos que permiten la cancelación de ruido, el beamforming o conformación de haces y el reconocimiento de voz. Con el desarrollo de aplicaciones de aprendizaje automático, el interés por integrar sensores con redes neuronales ha aumentado. Esto ha impulsado el interés por desarrollar micrófonos digitales en nodos CMOS nanométricos donde el front-end analógico y el procesamiento digital del micrófono, que puede incluir redes neuronales, está integrado en el mismo chip. Tradicionalmente, los convertidores analógicos-digitales (ADC) en micrófonos digitales han sido implementados utilizando moduladores Sigma-Delta de orden elevado. La técnica más común para implementar estos moduladores Sigma- Delta es el uso de circuitos CMOS de capacidades conmutadas. Recientemente, para reducir el consumo de potencia y hacerlos más adecuados para las tareas que requieren una operación continua, como el reconocimiento de palabras clave, los convertidores Sigma-Delta de capacidades conmutadas has sido mejorados con el uso de integradores implementados con amplificadores operacionales basados en inversores CMOS. Alternativamente, los Sigma-Delta de capacidades conmutadas han sido reemplazados por moduladores en tiempo continuo. No obstante, en ambas implementaciones, la señal de entrada es codificada en voltaje durante el proceso de conversión, lo que hace que la integración en nodos CMOS más pequeños sea complicada debido a la menor tensión de alimentación. Una técnica alternativa consiste en codificar la señal de entrada en tiempo (o frecuencia) en lugar de tensión. Esto es lo que hacen los convertidores de codificación temporal. Recientemente, los convertidores de codificación temporal han ganado popularidad ya que son más adecuados para nodos CMOS nanométricos que los convertidores Sigma-Delta. Entre los que más interés han despertado encontramos los ADCs basados en osciladores controlados por tensión (VCO-ADC). Los VCO-ADC se pueden implementar usando osciladores en anillo (RO) implementados con inversores CMOS y circuitos digitales. Esta familia de convertidores también tiene conformado de ruido. Esto los convierte en una alternativa muy interesante para la implementación de convertidores en nodos CMOS nanométricos. Sin embargo, dos problemas principales están presentes en este tipo de ADCs debidos ambos a las no idealidades del oscilador. El primero de los problemas es la presencia de ruido de fase en el oscilador, lo que reduce la resolución del ADC. El segundo es la curva de conversion voltaje-frecuencia no lineal del oscilador, lo que causa distorsión a amplitudes medias y altas. En esta tesis analizamos el uso de ADCs de codificación temporal para micrófonos MEMS, con especial interés en ADCS basados en osciladores de anillo (RO-ADC). En primer lugar, estudiamos el uso de un cuantificador SAR con conformado de ruido (SAR-NSQ) en moduladores Sigma-Delta. Este cuantificador agrega un orden adicional de conformado de ruido al modulador, mejorando la resolución. En este documento se explica el cuantificador y obtienen las ecuaciones para la función de transferencia de ruido (NTF) de un sigma-delta de tercer orden usando un filtro de segundo orden y el NSQ. En segundo lugar, dirigimos nuestra atención al tema de los RO-ADC. Presentamos el chip de un micrófono MEMS de alto rango dinámico en CMOS de 130 nm basado en un VCO-ADC de bucle abierto. En esta tesis se explica la implementación del front-end analógico que incluye el oscilador y la interfaz con el MEMS. Esta implementación se ha llevado a cabo con el objetivo de lograr un bajo consumo de potencia, un bajo nivel de ruido y un alto rango dinámico. La descripción del back-end digital se deja para la tesis del couator del chip. La SNDR de pico del chip es de 80dBA y el rango dinámico de 108dB con una THD de 1,5% a 128 dBSPL y un consumo de potencia de 438μW. Finalmente, se analiza el uso de una resistencia dependiente de frecuencia (FDR) para implementar un bucle de realimentación no muestreado alrededor del oscilador. El objetivo es reducir la distorsión. Además, también se logra la mitigación del ruido de fase del oscilador. Se analyza una primera topologia de realimentación incluyendo un amplificador operacional para incrementar la ganancia de bucle. Este diseño se prueba en silicio en un chip CMOS de 130nm que logra un pico de SNDR de 84 dBA con un consumo de potencia de 600μW en la parte analógica. Seguidamente, se analiza una segunda topología sin el amplificador operacional. Se fabrican y miden dos chips diseñados con esta topologia. El primero de ellos en CMOS de 130 nm es un VCO-ADC completo que incluye el convertidor de frecuencia a digital (F2D). Este chip alcanza un pico SNDR de 76,6 dBA con un consumo de potencia de 482μW. El segundo incluye solo el oscilador y está implementado en CMOS de 55nm. El pico SNDR es 78.15 dBA y el el consumo de potencia analógica es de 153μW. Para cerrar esta tesis, se presentan dos circuitos que usan la FDR con un oscilador en anillo. El primero es un convertidor de capacidad a digital (CDC). El segundo es un filtro realizado con una FDR y un oscilador, enfocado a tareas de detección de voz (VAD).Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Antonio Jesús Torralba Silgado.- Secretaria: María Luisa López Vallejo.- Vocal: Pieter Rombout

Plasmonic color filter array, high performance analog to digital converter architectures and novel circuit techniques

Part I: Plasmonic color filters can be manufactured at lower cost since they can be fabricated in single lithographic process step as compared to Fabry-Perot based filters. In addition, they have narrow passband making resolving sharp features in sample spectrum possible. Due to these benefits, in this thesis, Plasmonic color filters are investigated as alternative to conventional color filters and their feasibility for spectroscopy demonstrated through reconstruction of 6 sample spectra by using a set of 20 color filters. The error in reconstructed sample spectra is less than 0.137 root mean squared error across all samples. Part II: A novel 12-bit pipelined successive approximation analog to digital converter is investigated for high speed data conversion. The design was implemented in TSMC 65nm process to demonstrate the feasibility of the architecture. Furthermore, a high dynamic range audio delta sigma modulator using pseudo-pseudo differential topology was investigated and feasibility simulated using TSMC 65nm process. In addition, various novel systems and circuit techniques including efficient calibration of feedback digital to analog converters, new boosted switch and push-pull source follower circuits were investigated to improve upon existing circuit topologies