A built-in self-test technique for high speed analog-to-digital converters

Fundação para a Ciência e a Tecnologia (FCT) - PhD grant (SFRH/BD/62568/2009

Design and debugging of multi-step analog to digital converters

With the fast advancement of CMOS fabrication technology, more and more signal-processing functions are implemented in the digital domain for a lower cost, lower power consumption, higher yield, and higher re-configurability. The trend of increasing integration level for integrated circuits has forced the A/D converter interface to reside on the same silicon in complex mixed-signal ICs containing mostly digital blocks for DSP and control. However, specifications of the converters in various applications emphasize high dynamic range and low spurious spectral performance. It is nontrivial to achieve this level of linearity in a monolithic environment where post-fabrication component trimming or calibration is cumbersome to implement for certain applications or/and for cost and manufacturability reasons. Additionally, as CMOS integrated circuits are accomplishing unprecedented integration levels, potential problems associated with device scaling – the short-channel effects – are also looming large as technology strides into the deep-submicron regime. The A/D conversion process involves sampling the applied analog input signal and quantizing it to its digital representation by comparing it to reference voltages before further signal processing in subsequent digital systems. Depending on how these functions are combined, different A/D converter architectures can be implemented with different requirements on each function. Practical realizations show the trend that to a first order, converter power is directly proportional to sampling rate. However, power dissipation required becomes nonlinear as the speed capabilities of a process technology are pushed to the limit. Pipeline and two-step/multi-step converters tend to be the most efficient at achieving a given resolution and sampling rate specification. This thesis is in a sense unique work as it covers the whole spectrum of design, test, debugging and calibration of multi-step A/D converters; it incorporates development of circuit techniques and algorithms to enhance the resolution and attainable sample rate of an A/D converter and to enhance testing and debugging potential to detect errors dynamically, to isolate and confine faults, and to recover and compensate for the errors continuously. The power proficiency for high resolution of multi-step converter by combining parallelism and calibration and exploiting low-voltage circuit techniques is demonstrated with a 1.8 V, 12-bit, 80 MS/s, 100 mW analog to-digital converter fabricated in five-metal layers 0.18-µm CMOS process. Lower power supply voltages significantly reduce noise margins and increase variations in process, device and design parameters. Consequently, it is steadily more difficult to control the fabrication process precisely enough to maintain uniformity. Microscopic particles present in the manufacturing environment and slight variations in the parameters of manufacturing steps can all lead to the geometrical and electrical properties of an IC to deviate from those generated at the end of the design process. Those defects can cause various types of malfunctioning, depending on the IC topology and the nature of the defect. To relive the burden placed on IC design and manufacturing originated with ever-increasing costs associated with testing and debugging of complex mixed-signal electronic systems, several circuit techniques and algorithms are developed and incorporated in proposed ATPG, DfT and BIST methodologies. Process variation cannot be solved by improving manufacturing tolerances; variability must be reduced by new device technology or managed by design in order for scaling to continue. Similarly, within-die performance variation also imposes new challenges for test methods. With the use of dedicated sensors, which exploit knowledge of the circuit structure and the specific defect mechanisms, the method described in this thesis facilitates early and fast identification of excessive process parameter variation effects. The expectation-maximization algorithm makes the estimation problem more tractable and also yields good estimates of the parameters for small sample sizes. To allow the test guidance with the information obtained through monitoring process variations implemented adjusted support vector machine classifier simultaneously minimize the empirical classification error and maximize the geometric margin. On a positive note, the use of digital enhancing calibration techniques reduces the need for expensive technologies with special fabrication steps. Indeed, the extra cost of digital processing is normally affordable as the use of submicron mixed signal technologies allows for efficient usage of silicon area even for relatively complex algorithms. Employed adaptive filtering algorithm for error estimation offers the small number of operations per iteration and does not require correlation function calculation nor matrix inversions. The presented foreground calibration algorithm does not need any dedicated test signal and does not require a part of the conversion time. It works continuously and with every signal applied to the A/D converter. The feasibility of the method for on-line and off-line debugging and calibration has been verified by experimental measurements from the silicon prototype fabricated in standard single poly, six metal 0.09-µm CMOS process

Alternative Methods for Non-Linearity Estimation in High-Resolution Analog-to-Digital Converters

The evaluation of the linearity performance of a high resolution Analog-to- Digital Converter (ADC) by the Standard Histogram method is an outstanding challenge due to the requirement of high purity of the input signal and the high number of output data that must be acquired to obtain an acceptable accuracy on the estimation. These requirements become major application drawbacks when the measures have to be performed multiple times within long test flows and for many parts, and under an industrial environment that seeks to reduce costs and lead times as is the case in the New Space sector. This thesis introduces two alternative methods that succeed in relaxing the two previous requirements for the estimation of the Integral Nonlinearity (INL) parameter in ADCs. The methods have been evaluated by estimating the Integral Non-Linearity pattern by simulation using realistic high-resolution ADC models and experimentally by applying them to real high performance ADCs. First, the challenge of applying the Standard Histogram method for the evaluation of static parameters in high resolution ADCs and how the drawbacks are accentuated in the New Space industry is analysed, being a highly expensive method for an industrial environment where cost and lead time reduction is demanded. Several alternative methods to the Standard Histogram for estimating Integral Nonlinearity in high resolution ADCs are reviewed and studied. As the number of existing works in the literature is very large and addressing all of them is a challenge in itself, only those most relevant to the development of this thesis have been included. Methods based on spectral processing to reduce the number of data acquired for the linearity test and methods based on a double histogram to be able to use generators that do not meet the the purity requirement against the ADC to be tested are further analysed. Two novel contributions are presented in this work for the estimation of the Integral Nonlinearity in ADCs, as possible alternatives to the Standard Histogram method. The first method, referred to as SSA (Simple Spectral Approach), seeks to reduce the number of output data that need to be acquired and focuses on INL estimation using an algorithm based on processing the spectrum of the output signal when a sinusoidal input stimulus is used. This type of approach requires a much smaller number of samples than the Standard Histogram method, although the estimation accuracy will depend on how smooth or abrupt the ADC nonlinearity pattern is. In general, this algorithm cannot be used to perform a calibration of the ADC nonlinearity error, but it can be applied to find out between which limits it lies and what its approximate shape is. The second method, named SDH (Simplified Double Histogram)aims to estimate the Non-Linearity of the ADC using a poor linearity generator. The approach uses two histograms constructed from the two set of output data in response to two identical input signals except for a dc offset between them. Using a simple adder model, an extended approach named ESDH (Extended Simplified Double Histogram) addresses and corrects for possible time drifts during the two data acquisitions, so that it can be successfully applied in a non-stationary test environment. According to the experimental results obtained, the proposed algorithm achieves high estimation accuracy. Both contributions have been successfully tested in high-resolution ADCs with both simulated and real laboratory experiments, the latter using a commercial ADC with 14-bit resolution and 65Msps sampling rate (AD6644 from Analog Devices).La medida de la característica de linealidad de un convertidor analógicodigital (ADC) de alta resolución mediante el método estándar del Histograma constituye un gran desafío debido los requisitos de alta pureza de la señal de entrada y del elevado número de datos de salida que deben adquirirse para obtener una precisión aceptable en la estimación. Estos requisitos encuentran importantes inconvenientes para su aplicación cuando las medidas deben realizarse dentro de largos flujos de pruebas, múltiples veces y en un gran número de piezas, y todo bajo un entorno industrial que busca reducir costes y plazos de entrega como es el caso del sector del Nuevo Espacio. Esta tesis introduce dos métodos alternativos que consiguen relajar los dos requisitos anteriores para la estimación de los parámetros de no linealidad en los ADCs. Los métodos se han evaluado estimando el patrón de No Linealidad Integral (INL) mediante simulación utilizando modelos realistas de ADC de alta resolución y experimentalmente aplicándolos en ADCs reales. Inicialmente se analiza el reto que supone la aplicación del método estándar del Histograma para la evaluación de los parámetros estáticos en ADCs de alta resolución y cómo sus inconvenientes se acentúan en la industria del Nuevo Espacio, siendo un método altamente costoso para un entorno industrial donde se exige la reducción de costes y plazos de entrega. Se estudian métodos alternativos al Histograma estándar para la estimación de la No Linealidad Integral en ADCs de alta resolución. Como el número de trabajos es muy amplio y abordarlos todos es ya en sí un desafío, se han incluido aquellos más relevantes para el desarrollo de esta tesis. Se analizan especialmente los métodos basados en el procesamiento espectral para reducir el número de datos que necesitan ser adquiridos y los métodos basados en un doble histograma para poder utilizar generadores que no cumplen el requisito de precisión frente al ADC a medir. En este trabajo se presentan dos novedosas aportaciones para la estimación de la No Linealidad Integral en ADCs, como posibles alternativas al método estándar del Histograma. El primer método, denominado SSA (Simple Spectral Approach), busca reducir el número de datos de salida que es necesario adquirir y se centra en la estimación de la INL mediante un algoritmo basado en el procesamiento del espectro de la señal de salida cuando se utiliza un estímulo de entrada sinusoidal. Este tipo de enfoque requiere un número mucho menor de muestras que el método estándar del Histograma, aunque la precisión de la estimación dependerá de lo suave o abrupto que sea el patrón de no-linealidad del ADC a medir. En general, este algoritmo no puede utilizarse para realizar una calibración del error de no linealidad del ADC, pero puede aplicarse para averiguar entre qué límites se encuentra y cuál es su forma aproximada. El segundo método, denominado SDH (Simplified Double Histogram) tiene como objetivo estimar la no linealidad del ADC utilizando un generador de baja pureza. El algoritmo utiliza dos histogramas, construidos a partir de dos conjuntos de datos de salida en respuesta a dos señales de entrada idénticas, excepto por un desplazamiento constante entre ellas. Utilizando un modelo simple de sumador, un enfoque ampliado denominado ESDH (Extended Simplified Double Histogram) aborda y corrige las posibles derivas temporales durante las dos adquisiciones de datos, de modo que puede aplicarse con éxito en un entorno de prueba no estacionario. De acuerdo con los resultados experimentales obtenidos, el algoritmo propuesto alcanza una alta precisión de estimación. Ambas contribuciones han sido probadas en ADCs de alta resolución con experimentos tanto simulados como reales en laboratorio, estos últimos utilizando un ADC comercial con una resolución de 14 bits y una tasa de muestreo de 65Msps (AD6644 de Analog Devices)

Robust low power CMOS methodologies for ISFETs instrumentation

I have developed a robust design methodology in a 0.18 [Mu]m commercial CMOS process to circumvent the performance issues of the integrated Ions Sensitive Field Effect Transistor (ISFET) for pH detection. In circuit design, I have developed frequency domain signal processing, which transforms pH information into a frequency modulated signal. The frequency modulated signal is subsequently digitized and encoded into a bit-stream of data. The architecture of the instrumentation system consists of a) A novel front-end averaging amplifier to interface an array of ISFETs for converting pH into a voltage signal, b) A high linear voltage controlled oscillator for converting the voltage signal into a frequency modulated signal, and c) Digital gates for digitizing and differentiating the frequency modulated signal into an output bit-stream. The output bit stream is indistinguishable to a 1st order sigma delta modulation, whose noise floor is shaped by +20dB/decade. The fabricated instrumentation system has a dimension of 1565 [Mu] m 1565 [Mu] m. The chip responds linearly to the pH in a chemical solution and produces a digital output, with up to an 8-bit accuracy. Most importantly, the fabricated chips do not need any post-CMOS processing for neutralizing any trapped-charged effect, which can modulate on-chip ISFETs’ threshold voltages into atypical values. As compared to other ISFET-related works in the literature, the instrumentation system proposed in this thesis can cope with the mismatched ISFETs on chip for analogue-to-digital conversions. The design methodology is thus very accurate and robust for chemical sensing

Fast, accurate power measurement and optimization for microprocessor platforms

Power and energy consumption have become important for all computers, but the tools used to measure and optimize power on physical hardware lag far behind performance focused tools. Existing measurement apparata have low analog bandwidth, do not explicitly correlate power data with processor activity, and are not explained in sufficient detail to quantify uncertainty in their data. We present the design, implementation, and application of Jouler’s Loupe, a measurement device that overcomes these obstacles and enables a new generation of fast, fundamentally sound energy-efficiency-focused tools. We demonstrate substantial opportunity for energy-focused software optimizations on a mobile CPU core

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Resource-Constrained Acquisition Circuits for Next Generation Neural Interfaces

The development of neural interfaces allowing the acquisition of signals from the cortex of the brain has seen an increasing amount of interest both in academic research as well as in the commercial space due to their ability to aid people with various medical conditions, such as spinal cord injuries, as well as their potential to allow more seamless interactions between people and machines. While it has already been demonstrated that neural implants can allow tetraplegic patients to control robotic arms, thus to an extent returning some motoric function, the current state of the art often involves the use of heavy table-top instruments connected by wires passing through the patient’s skull, thus making the applications impractical and chronically infeasible. Those limitations are leading to the development of the next generation of neural interfaces that will overcome those issues by being minimal in size and completely wireless, thus paving a way to the possibility of their chronic application. Their development however faces several challenges in numerous aspects of engineering due to constraints presented by their minimal size, amount of power available as well as the materials that can be utilised. The aim of this work is to explore some of those challenges and investigate novel circuit techniques that would allow the implementation of acquisition analogue front-ends under the presented constraints. This is facilitated by first giving an overview of the problematic of recording electrodes and their electrical characterisation in terms of their impedance profile and added noise that can be used to guide the design of analogue front-ends. Continuous time (CT) acquisition is then investigated as a promising signal digitisation technique alternative to more conventional methods in terms of its suitability. This is complemented by a description of practical implementations of a CT analogue-to-digital converter (ADC) including a novel technique of clockless stochastic chopping aimed at the suppression of flicker noise that commonly affects the acquisition of low-frequency signals. A compact design is presented, implementing a 450 nW, 5.5 bit ENOB CT ADC, occupying an area of 0.0288 mm2 in a 0.18 μm CMOS technology, making this the smallest presented design in literature to the best of our knowledge. As completely wireless neural implants rely on power delivered through wireless links, their supply voltage is often subject to large high frequency variations as well voltage uncertainty making it necessary to design reference circuits and voltage regulators providing stable reference voltage and supply in the constrained space afforded to them. This results in numerous challenges that are explored and a design of a practical implementation of a reference circuit and voltage regulator is presented. Two designs in a 0.35 μm CMOS technology are presented, showing respectively a measured PSRR of ≈60 dB and ≈53 dB at DC and a worst-case PSRR of ≈42 dB and ≈33 dB with a less than 1% standard deviation in the output reference voltage of 1.2 V while consuming a power of ≈7 μW. Finally, ΣΔ modulators are investigated for their suitability in neural signal acquisition chains, their properties explained and a practical implementation of a ΣΔ DC-coupled neural acquisition circuit presented. This implements a 10-kHz, 40 dB SNDR ΣΔ analogue front-end implemented in a 0.18 μm CMOS technology occupying a compact area of 0.044 μm2 per channel while consuming 31.1 μW per channel.Open Acces

Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle