Contribución al modelado y diseño de moduladores sigma-delta en tiempo continuo de baja relación de sobremuestreo y bajo consumo de potencia

Continuous-Time Sigma-Delta modulators are often employed as analog-to-digital converters. These modulators are an attractive approach to implement high-speed converters in VLSI systems because they have low sensitivity to circuit imperfections compared to other solutions. This work is a contribution to the analysis, modelling and design of high-speed Continuous-Time Sigma-Delta modulators. The resolution and the stability of these modulators are limited by two main factors, excess-loop delay and sampling uncertainty. Both factors, among others, have been carefully analysed and modelled. A new design methodology is also proposed. It can be used to get an optimum high-speed Continuous-Time Sigma-Delta modulator in terms of dynamic range, stability and sensitivity to sampling uncertainty. Based on the proposed design methodology, a software tool that covers the main steps has been developed. The methodology has been proved by using the tool in designing a 30 Megabits-per-second Continuous-Time Sigma-Delta modulator with 11-bits of dynamic range. The modulator has been integrated in a 0.13-µm CMOS technology and it has a measured peak SNR of 62.5dB

Design of RF/IF analog to digital converters for software radio communication receivers

Software radio architecture can support multiple standards by performing analogto- digital (A/D) conversion of the radio frequency (RF) signals and running reconfigurable software programs on the backend digital signal processor (DSP). A slight variation of this architecture is the software defined radio architecture in which the A/D conversion is performed on intermediate frequency (IF) signals after a single down conversion. The first part of this research deals with the design and implementation of a fourth order continuous time bandpass sigma-delta (CT BP) C based on LC filters for direct RF digitization at 950 MHz with a clock frequency of 3.8 GHz. A new ADC architecture is proposed which uses only non-return to zero feedback digital to analog converter pulses to mitigate problems associated with clock jitter. The architecture also has full control over tuning of the coefficients of the noise transfer function for obtaining the best signal to noise ratio (SNR) performance. The operation of the architecture is examined in detail and extra design parameters are introduced to ensure robust operation of the ADC. Measurement results of the ADC, implemented in IBM 0.25 µm SiGe BiCMOS technology, show SNR of 63 dB and 59 dB in signal bandwidths of 200 kHz and 1 MHz, respectively, around 950 MHz while consuming 75 mW of power from ± 1.25 V supply. The second part of this research deals with the design of a fourth order CT BP ADC based on gm-C integrators with an automatic digital tuning scheme for IF digitization at 125 MHz and a clock frequency of 500 MHz. A linearized CMOS OTA architecture combines both cross coupling and source degeneration in order to obtain good IM3 performance. A system level digital tuning scheme is proposed to tune the ADC performance over process, voltage and temperature variations. The output bit stream of the ADC is captured using an external DSP, where a software tuning algorithm tunes the ADC parameters for best SNR performance. The IF ADC was designed in TSMC 0.35 µm CMOS technology and it consumes 152 mW of power from ± 1.65 V supply

Design of sigma-delta modulators for analog-to-digital conversion intensively using passive circuits

This thesis presents the analysis, design implementation and experimental evaluation of passiveactive discrete-time and continuous-time Sigma-Delta (ΣΔ) modulators (ΣΔMs) analog-todigital converters (ADCs). Two prototype circuits were manufactured. The first one, a discrete-time 2nd-order ΣΔM, was designed in a 130 nm CMOS technology. This prototype confirmed the validity of the ultra incomplete settling (UIS) concept used for implementing the passive integrators. This circuit, clocked at 100 MHz and consuming 298 μW, achieves DR/SNR/SNDR of 78.2/73.9/72.8 dB, respectively, for a signal bandwidth of 300 kHz. This results in a Walden FoMW of 139.3 fJ/conv.-step and Schreier FoMS of 168 dB. The final prototype circuit is a highly area and power efficient ΣΔM using a combination of a cascaded topology, a continuous-time RC loop filter and switched-capacitor feedback paths. The modulator requires only two low gain stages that are based on differential pairs. A systematic design methodology based on genetic algorithm, was used, which allowed decreasing the circuit’s sensitivity to the circuit components’ variations. This continuous-time, 2-1 MASH ΣΔM has been designed in a 65 nm CMOS technology and it occupies an area of just 0.027 mm2. Measurement results show that this modulator achieves a peak SNR/SNDR of 76/72.2 dB and DR of 77dB for an input signal bandwidth of 10 MHz, while dissipating 1.57 mW from a 1 V power supply voltage. The ΣΔM achieves a Walden FoMW of 23.6 fJ/level and a Schreier FoMS of 175 dB. The innovations proposed in this circuit result, both, in the reduction of the power consumption and of the chip size. To the best of the author’s knowledge the circuit achieves the lowest Walden FOMW for ΣΔMs operating at signal bandwidth from 5 MHz to 50 MHz reported to date

Contribution to the design of continuous -time Sigma - Delta Modulators based on time delay elements

The research carried out in this thesis is focused in the development of a new class of data converters for digital radio. There are two main architectures for communication receivers which perform a digital demodulation. One of them is based on analog demodulation to the base band and digitization of the I/Q components. Another option is to digitize the band pass signal at the output of the IF stage using a bandpass Sigma-Delta modulator. Bandpass Sigma- Delta modulators can be implemented with discrete-time circuits, using switched capacitors or continuous-time circuits. The main innovation introduced in this work is the use of passive transmission lines in the loop filter of a bandpass continuous-time Sigma-Delta modulator instead of the conventional solution with gm-C or LC resonators. As long as transmission lines are used as replacement of a LC resonator in RF technology, it seems compelling that transmission lines could improve bandpass continuous-time Sigma-Delta modulators. The analysis of a Sigma- Delta modulator using distributed resonators has led to a completely new family of Sigma- Delta modulators which possess properties inherited both from continuous-time and discretetime Sigma-Delta modulators. In this thesis we present the basic theory and the practical design trade-offs of this new family of Sigma-Delta modulators. Three demonstration chips have been implemented to validate the theoretical developments. The first two are a proof of concept of the application of transmission lines to build lowpass and bandpass modulators. The third chip summarizes all the contributions of the thesis. It consists of a transmission line Sigma-Delta modulator which combines subsampling techniques, a mismatch insensitive circuitry and a quadrature architecture to implement the IF to digital stage of a receiver

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device

A Low Jitter Wideband Fractional-N Subsampling Phase Locked Loop (SSPLL)

Frequency synthesizers have become a crucial building block in the evolution of modern communication systems and consumer electronics. The spectral purity performance of frequency synthesizers limits the achievable data-rate and presents a noise-power tradeoff. For communication standards such as LTE where the channel spacing is a few kHz, the synthesizers must provide high frequencies with sufficiently wide frequency tuning range and fine frequency resolutions. Such stringent performance must be met with a limited power and small chip area. In this thesis a wideband fractional-N frequency synthesizer based on a subsampling phase locked loop (SSPLL) is presented. The proposed synthesizer which has a frequency resolution less than 100Hz employs a digital fractional controller (DFC) and a 10-bit digital-to-time converter (DTC) to delay the rising edges of the reference clock to achieve fractional phase lock. For fast convergence of the delay calibration, a novel two-step delay correlation loop (DCL) is employed. Furthermore, to provide optimum settling and jitter performance, the loop transfer characteristics during frequency acquisition and phase-lock are decoupled using a dual input loop filter (DILF). The fractional-N sub-sampling PLL (FNSSPLL) is implemented in a TSMC 40nm CMOS technology and occupies a total active area of 0.41mm^2. The PLL operates over frequency range of 2.8 GHz to 4.3 GHz (42% tuning range) while consuming 9.18mW from a 1.1V supply. The integrated jitter performance is better than 390 fs across all fractional frequency channel. The worst case fractional spur of -48.3 dBc occurs at a 650 kHz offset for a 3.75GHz fractional channel. The in-band phase noise measured at a 200 kHz offset is -112.5 dBc/Hz

Architectural Alternatives to Implement High-Performance Delta-Sigma Modulators

RÉSUMÉ Le besoin d’appareils portatifs, de téléphones intelligents et de systèmes microélectroniques implantables médicaux s’accroît remarquablement. Cependant, l’optimisation de l’alimentation de tous ces appareils électroniques portables est l’un des principaux défis en raison du manque de piles à grande capacité utilisées pour les alimenter. C’est un fait bien établi que le convertisseur analogique-numérique (CAN) est l’un des blocs les plus critiques de ces appareils et qu’il doit convertir efficacement les signaux analogiques au monde numérique pour effectuer un post-traitement tel que l’extraction de caractéristiques. Parmi les différents types de CAN, les modulateurs Delta Sigma (��M) ont été utilisés dans ces appareils en raison des fonctionnalités alléchantes qu’ils offrent. En raison du suréchantillonnage et pour éloigner le bruit de la bande d’intérêt, un CAN haute résolution peut être obtenu avec les architectures ��. Il offre également un compromis entre la fréquence d’échantillonnage et la résolution, tout en offrant une architecture programmable pour réaliser un CAN flexible. Ces CAN peuvent être implémentés avec des blocs analogiques de faible précision. De plus, ils peuvent être efficacement optimisés au niveau de l’architecture et circuits correspondants. Cette dernière caractéristique a été une motivation pour proposer différentes architectures au fil des ans. Cette thèse contribue à ce sujet en explorant de nouvelles architectures pour optimiser la structure ��M en termes de résolution, de consommation d’énergie et de surface de silicium. Des soucis particuliers doivent également être pris en compte pour faciliter la mise en œuvre du ��M. D’autre part, les nouveaux procédés CMOS de conception et fabrication apportent des améliorations remarquables en termes de vitesse, de taille et de consommation d’énergie lors de la mise en œuvre de circuits numériques. Une telle mise à l’échelle agressive des procédés, rend la conception de blocs analogiques tel que un amplificateur de transconductance opérationnel (OTA), difficile. Par conséquent, des soins spéciaux sont également pris en compte dans cette thèse pour surmonter les problèmes énumérés. Ayant mentionné ci-dessus que cette thèse est principalement composée de deux parties principales. La première concerne les nouvelles architectures implémentées en mode de tension et la seconde partie contient une nouvelle architecture réalisée en mode hybride tension et temps.----------ABSTRACT The need for hand-held devices, smart-phones and medical implantable microelectronic sys-tems, is remarkably growing up. However, keeping all these electronic devices power optimized is one of the main challenges due to the lack of long life-time batteries utilized to power them up. It is a well-established fact that analog-to-digital converter (ADC) is one of the most critical building blocks of such devices and it needs to efficiently convert analog signals to the digital world to perform post processing such as channelizing, feature extraction, etc. Among various type of ADCs, Delta Sigma Modulators (��Ms) have been widely used in those devices due to the tempting features they offer. In fact, due to oversampling and noise-shaping technique a high-resolution ADC can be achieved with �� architectures. It also offers a compromise between sampling frequency and resolution while providing a highly-programmable approach to realize an ADC. Moreover, such ADCs can be implemented with low-precision analog blocks. Last but not the least, they are capable of being effectively power optimized at both architectural and circuit levels. The latter has been a motivation to proposed different architectures over the years.This thesis contributes to this topic by exploring new architectures to effectively optimize the ��M structure in terms of resolution, power consumption and chip area. Special cares must also be taken into account to ease the implementation of the ��M. On the other hand, advanced node CMOS processes bring remarkable improvements in terms of speed, size and power consumption while implementing digital circuits. Such an aggressive process scaling, however, make the design of analog blocks, e.g. operational transconductance amplifiers (OTAs), cumbersome. Therefore, special cares are also taken into account in this thesis to overcome the mentioned issues. Having had above mentioned discussion, this thesis is mainly split in two main categories. First category addresses new architectures implemented in a pure voltage domain and the second category contains new architecture realized in a hybrid voltage and time domain. In doing so, the thesis first focuses on a switched-capacitor implementation of a ��M while presenting an architectural solution to overcome the limitations of the previous approaches. This limitations include a power hungry adder in a conventional feed-forward topology as well as power hungry OTAs

High Performance Integrated Circuit Blocks for High-IF Wideband Receivers

Due to the demand for high‐performance radio frequency (RF) integrated circuit design in the past years, a system‐on‐chip (SoC) that enables integration of analog and digital parts on the same die has become the trend of the microelectronics industry. As a result, a major requirement of the next generation of wireless devices is to support multiple standards in the same chip‐set. This would enable a single device to support multiple peripheral applications and services. Based on the aforementioned, the traditional superheterodyne front‐end architecture is not suitable for such applications as it would require a complete receiver for each standard to be supported. A more attractive alternative is the highintermediate frequency (IF) radio architecture. In this case the signal is digitalized at an intermediate frequency such as 200MHz. As a consequence, the baseband operations, such as down‐conversion and channel filtering, become more power and area efficient in the digital domain. Such architecture releases the specifications for most of the front‐end building blocks, but the linearity and dynamic range of the ADC become the bottlenecks in this system. The requirements of large bandwidth, high frequency and enough resolution make such ADC very difficult to realize. Many ADC architectures were analyzed and Continuous‐Time Bandpass Sigma‐Delta (CT‐BP‐ΣΔ) architecture was found to be the most suitable solution in the high‐IF receiver architecture since they combine oversampling and noise shaping to get fairly high resolution in a limited bandwidth. A major issue in continuous‐time networks is the lack of accuracy due to powervoltage‐ temperature (PVT) tolerances that lead to over 20% pole variations compared to their discrete‐time counterparts. An optimally tuned BP ΣΔ ADC requires correcting for center frequency deviations, excess loop delay, and DAC coefficients. Due to these undesirable effects, a calibration algorithm is necessary to compensate for these variations in order to achieve high SNR requirements as technology shrinks. In this work, a novel linearization technique for a Wideband Low‐Noise Amplifier (LNA) targeted for a frequency range of 3‐7GHz is presented. Post‐layout simulations show NF of 6.3dB, peak S21 of 6.1dB, and peak IIP3 of 21.3dBm, respectively. The power consumption of the LNA is 5.8mA from 2V. Secondly, the design of a CMOS 6th order CT BP‐ΣΔ modulator running at 800 MHz for High‐IF conversion of 10MHz bandwidth signals at 200 MHz is presented. A novel transconductance amplifier has been developed to achieve high linearity and high dynamic range at high frequencies. A 2‐bit quantizer with offset cancellation is alsopresented. The sixth‐order modulator is implemented using 0.18 um TSMC standard analog CMOS technology. Post‐layout simulations in cadence demonstrate that the modulator achieves a SNDR of 78 dB (~13 bit) performance over a 14MHz bandwidth. The modulator’s static power consumption is 107mW from a supply power of ± 0.9V. Finally, a calibration technique for the optimization of the Noise Transfer Function CT BP ΣΔ modulators is presented. The proposed technique employs two test tones applied at the input of the quantizer to evaluate the noise transfer function of the ADC, using the capabilities of the Digital Signal Processing (DSP) platform usually available in mixed‐mode systems. Once the ADC output bit stream is captured, necessary information to generate the control signals to tune the ADC parameters for best Signal‐to‐Quantization Noise Ratio (SQNR) performance is extracted via Least‐ Mean Squared (LMS) software‐based algorithm. Since the two tones are located outside the band of interest, the proposed global calibration approach can be used online with no significant effect on the in‐band content

Design and implementation of generalized topologies of time-interleaved variable bandpass Σ−Δ modulators

In this thesis, novel analog-to-digital and digital-to-analog generalized time-interleaved variable bandpass sigma-delta modulators are designed, analysed, evaluated and implemented that are suitable for high performance data conversion for a broad-spectrum of applications. These generalized time-interleaved variable bandpass sigma-delta modulators can perform noise-shaping for any centre frequency from DC to Nyquist. The proposed topologies are well-suited for Butterworth, Chebyshev, inverse-Chebyshev and elliptical filters, where designers have the flexibility of specifying the centre frequency, bandwidth as well as the passband and stopband attenuation parameters. The application of the time-interleaving approach, in combination with these bandpass loop-filters, not only overcomes the limitations that are associated with conventional and mid-band resonator-based bandpass sigma-delta modulators, but also offers an elegant means to increase the conversion bandwidth, thereby relaxing the need to use faster or higher-order sigma-delta modulators. A step-by-step design technique has been developed for the design of time-interleaved variable bandpass sigma-delta modulators. Using this technique, an assortment of lower- and higher-order single- and multi-path generalized A/D variable bandpass sigma-delta modulators were designed, evaluated and compared in terms of their signal-to-noise ratios, hardware complexity, stability, tonality and sensitivity for ideal and non-ideal topologies. Extensive behavioural-level simulations verified that one of the proposed topologies not only used fewer coefficients but also exhibited greater robustness to non-idealties. Furthermore, second-, fourth- and sixth-order single- and multi-path digital variable bandpass digital sigma-delta modulators are designed using this technique. The mathematical modelling and evaluation of tones caused by the finite wordlengths of these digital multi-path sigmadelta modulators, when excited by sinusoidal input signals, are also derived from first principles and verified using simulation and experimental results. The fourth-order digital variable-band sigma-delta modulator topologies are implemented in VHDL and synthesized on Xilinx® SpartanTM-3 Development Kit using fixed-point arithmetic. Circuit outputs were taken via RS232 connection provided on the FPGA board and evaluated using MATLAB routines developed by the author. These routines included the decimation process as well. The experiments undertaken by the author further validated the design methodology presented in the work. In addition, a novel tunable and reconfigurable second-order variable bandpass sigma-delta modulator has been designed and evaluated at the behavioural-level. This topology offers a flexible set of choices for designers and can operate either in single- or dual-mode enabling multi-band implementations on a single digital variable bandpass sigma-delta modulator. This work is also supported by a novel user-friendly design and evaluation tool that has been developed in MATLAB/Simulink that can speed-up the design, evaluation and comparison of analog and digital single-stage and time-interleaved variable bandpass sigma-delta modulators. This tool enables the user to specify the conversion type, topology, loop-filter type, path number and oversampling ratio