Low-Power Low-Noise CMOS Analog and Mixed-Signal Design towards Epileptic Seizure Detection

About 50 million people worldwide suffer from epilepsy and one third of them have seizures that are refractory to medication. In the past few decades, deep brain stimulation (DBS) has been explored by researchers and physicians as a promising way to control and treat epileptic seizures. To make the DBS therapy more efficient and effective, the feedback loop for titrating therapy is required. It means the implantable DBS devices should be smart enough to sense the brain signals and then adjust the stimulation parameters adaptively. This research proposes a signal-sensing channel configurable to various neural applications, which is a vital part for a future closed-loop epileptic seizure stimulation system. This doctoral study has two main contributions, 1) a micropower low-noise neural front-end circuit, and 2) a low-power configurable neural recording system for both neural action-potential (AP) and fast-ripple (FR) signals. The neural front end consists of a preamplifier followed by a bandpass filter (BPF). This design focuses on improving the noise-power efficiency of the preamplifier and the power/pole merit of the BPF at ultra-low power consumption. In measurement, the preamplifier exhibits 39.6-dB DC gain, 0.8 Hz to 5.2 kHz of bandwidth (BW), 5.86-μVrms input-referred noise in AP mode, while showing 39.4-dB DC gain, 0.36 Hz to 1.3 kHz of BW, 3.07-μVrms noise in FR mode. The preamplifier achieves noise efficiency factor (NEF) of 2.93 and 3.09 for AP and FR modes, respectively. The preamplifier power consumption is 2.4 μW from 2.8 V for both modes. The 6th-order follow-the-leader feedback elliptic BPF passes FR signals and provides -110 dB/decade attenuation to out-of-band interferers. It consumes 2.1 μW from 2.8 V (or 0.35 μW/pole) and is one of the most power-efficient high-order active filters reported to date. The complete front-end circuit achieves a mid-band gain of 38.5 dB, a BW from 250 to 486 Hz, and a total input-referred noise of 2.48 μVrms while consuming 4.5 μW from the 2.8 V power supply. The front-end NEF achieved is 7.6. The power efficiency of the complete front-end is 0.75 μW/pole. The chip is implemented in a standard 0.6-μm CMOS process with a die area of 0.45 mm^2. The neural recording system incorporates the front-end circuit and a sigma-delta analog-to-digital converter (ADC). The ADC has scalable BW and power consumption for digitizing both AP and FR signals captured by the front end. Various design techniques are applied to the improvement of power and area efficiency for the ADC. At 77-dB dynamic range (DR), the ADC has a peak SNR and SNDR of 75.9 dB and 67 dB, respectively, while consuming 2.75-mW power in AP mode. It achieves 78-dB DR, 76.2-dB peak SNR, 73.2-dB peak SNDR, and 588-μW power consumption in FR mode. Both analog and digital power supply voltages are 2.8 V. The chip is fabricated in a standard 0.6-μm CMOS process. The die size is 11.25 mm^2. The proposed circuits can be extended to a multi-channel system, with the ADC shared by all channels, as the sensing part of a future closed-loop DBS system for the treatment of intractable epilepsy

LOW-VOLTAGE LOW-POWER ANALOG-TO-DIGITAL CONVERTERS

Ph.DDOCTOR OF PHILOSOPH

Interface Circuits for Microsensor Integrated Systems

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [Recent advances in sensing technologies, especially those for Microsensor Integrated Systems, have led to several new commercial applications. Among these, low voltage and low power circuit architectures have gained growing attention, being suitable for portable long battery life devices. The aim is to improve the performances of actual interface circuits and systems, both in terms of voltage mode and current mode, in order to overcome the potential problems due to technology scaling and different technology integrations. Related problems, especially those concerning parasitics, lead to a severe interface design attention, especially concerning the analog front-end and novel and smart architecture must be explored and tested, both at simulation and prototype level. Moreover, the growing demand for autonomous systems gets even harder the interface design due to the need of energy-aware cost-effective circuit interfaces integrating, where possible, energy harvesting solutions. The objective of this Special Issue is to explore the potential solutions to overcome actual limitations in sensor interface circuits and systems, especially those for low voltage and low power Microsensor Integrated Systems. The present Special Issue aims to present and highlight the advances and the latest novel and emergent results on this topic, showing best practices, implementations and applications. The Guest Editors invite to submit original research contributions dealing with sensor interfacing related to this specific topic. Additionally, application oriented and review papers are encouraged.

Low-voltage Low-power Switched-Capacitor ?S Modulator Design

Ph.DDOCTOR OF PHILOSOPH

Area- and Energy- Efficient Modular Circuit Architecture for 1,024-Channel Parallel Neural Recording Microsystem.

This research focuses to develop system architectures and associated electronic circuits for a next generation neuroscience research tool, a massive-parallel neural recording system capable of recording 1,024 channels simultaneously. Three interdependent prototypes have been developed to address major challenges in realization of the massive-parallel neural recording microsystems: minimization of energy and area consumption while preserving high quality in recordings. First, a modular 128-channel Δ-ΔΣ AFE using the spectrum shaping has been designed and fabricated to propose an area-and energy efficient solution for neural recording AFEs. The AFE achieved 4.84 fJ/C−s·mm2 figure of merit that is the smallest the area-energy product among the state-of-the-art multichannel neural recording systems. It also features power and area consumption of 3.05 µW and 0.05 mm2 per channel, respectively while exhibiting 63.3 dB signal-to-noise ratio with 3.02 µVrms input referred noise. Second, an on-chip mixed signal neural signal compressor was built to reduce the energy consumption in handling and transmission of the recorded data since this occupies a large portion of the total energy consumption as the number of parallel recording increases. The compressor reduces the data rates of two distinct groups of neural signals that are essential for neuroscience research: LFP and AP without loss of informative signals. As a result, the power consumptions for the data handling and transmissions of the LFP and AP were reduced to about 1/5.35 and 1/10.54 of the uncompressed cases, respectively. In the total data handling and transmission, the measured power consumption per channel is 11.98 µW that is about 1/9 of 107.5 µW without the compression. Third, a compact on-chip dc-to-dc converter with constant 1 MHz switching frequency has been developed to provide reliable power supplies and enhance energy delivery efficiency to the massive-parallel neural recording systems. The dc-to-dc converter has only predictable tones at the output and it exhibits > 80% power conversion efficiency at ultra-light loads, < 100 µW that is relevant power most of the multi-channel neural recording systems consume. The dc-to-dc converter occupies 0.375 mm2 of area which is less than 1/20 of the area the first prototype consumes (8.64 mm2).PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133244/1/sungyun_1.pd

Low-voltage low-power continuous-time delta-sigma modulator designs

Ph.DDOCTOR OF PHILOSOPH

A Continuous-Time Delta-Sigma Modulator for Ultra-Low-Power Radios

The increasing need of digital signal processing for telecommunication and multimedia applications, implemented in complementary metal-oxide semiconductor (CMOS) technology, creates the necessity for high-resolution analog-to-digital converters (ADCs). Based on the sampling frequency, ADCs are of two types: Nyquist-rate converters and oversampling converters. Oversampling converters are preferred for low-bandwidth applications such as audio and instrumentation because they provide inherently high resolution when coupled with proper noise shaping. This allows to push noise out of signal band, thus increasing the signal-to-noise ratio (SNR). Continuous time delta-sigma ADCs are becoming more popular than discrete-time ADCs primarily because of inherent anti-aliasing filtering, reduced settling time and low-power consumption. In this thesis, a 2nd-order 4-bits continuous-time (CT) delta-sigma modulator (DSM) for radio applications is designed. It employs a 2nd-order loop filter with a single operational amplifier. Implemented in a 65-nanometer CMOS technology, the modulator runs on a 0.8-V supply and achieves a SNR of 70dB over a 500-kHz signal bandwidth. The modulator operates with an oversampling ratio (OSR) of 16 and a sampling frequency of 16MHz. In the first chapter the principles of ΔΣ modulators are analysed, introducing the differences between discrete-time (DT) modulators and continuous-time (CT) modulators. In the next chapter the techniques to design a ΔΣ modulators for ultra-low-power radios are presented. The third chapter talks over the design of the operational amplifier, which appears inside the loop filter. In the fourth chapter the performance of the complete ΔΣ modulator, which employs a flash quantizer, is shown. Finally, in the last chapter, a performance analysis is carried out replacing the flash quantizer with an asynchronous SAR quantizer. The analysis shows that a further reduction of the quantizer power consumption of about 40% is possible. The conjunction of this replacement with the power-saving technique implemented in the loop filter appears relevant

Etude et conception de convertisseur analogique numérique large bande basé sur la modulation sigma delta

The work presented in this Ph.D. dissertation deals with the design of a wideband and accurate Analog-to-Digital Converter (ADC) able to digitize signals of different wireless communications standards. Thereby, itresponds to the Software Defined Radio concept (SDR). The purpose is reconfigurability by software andintegrability of the multistandard radio terminal. Oversampling (Sigma Delta) ADCs have been interestingcandidates in this context of multistandard SDR reception thanks to their high accuracy. Although they presentlimited operating bandwidth, it is possible to use them in a parallel architecture thus the bandwidth isextended. Therefore, we propose in this work the design and implementation of a parallel frequency banddecomposition ADC based on Discrete-time modulators in an SDR receiver handling E-GSM, UMTS andIEEE802.11a standard signals. The novelty of this proposed architecture is its programmability. Where,according to the selected standard digitization is made by activating only required branches are activated withspecified sub-bandwidths and sampling frequency. In addition the frequency division plan is non-uniform.After validation of the theoretical design by simulation, the overall baseband stage has been designed. Resultsof this study have led to a single passive 6th order Butterworth anti-aliasing filter (AAF) permitting theelimination of the automatic gain control circuit (AGC) which is an analog component. FBD architecturerequires digital processing able to recombine parallel branches outputs signals in order to reconstruct the finaloutput signal. An optimized design of this digital reconstruction signal stage has been proposed. Synthesis ofthe baseband stage has revealed modulators stability problems. To deal with this problem, a solution basedon non-unitary STF has been elaborated. Indeed, phase mismatches have been shown in the recombinedoutput signal and they have been corrected in the digital stage. Analytic study and system level design havebeen completed by an implementation of the parallel ADC digital reconstruction stage. Two design flows havebeen considered, one associated to the FPGA and another independent of the chosen target (standard VHDL).Proposed architecture has been validated using a VIRTEX6 FPGA Xilinx target. A dynamic range over 74 dB hasbeen measured for UMTS use case, which responds to the dynamic range required by this standard.Les travaux de recherche de cette thèse de doctorat s’inscrivent dans le cadre de la conception d’unconvertisseur analogique-numérique (ADC, Analog-to-Digital Converter) large bande et à haute résolution afinde numériser plusieurs standards de communications sans fil. Il répond ainsi au concept de la radio logiciellerestreinte (SDR, Software Defined Radio). L’objectif visé est la reconfigurabilité par logiciel et l’intégrabilité envue d’un système radio multistandard. Les ADCs à sur-échantillonnage de type sigma-delta () s’avèrent debons candidats dans ce contexte de réception SDR multistandard en raison de leur précision accrue. Bien queleur bande passante soit réduite, il est possible de les utiliser dans une architecture en parallèle permettantd’élargir la bande passante. Nous nous proposons alors dans cette thèse de dimensionner et d’implanter unADC parallèle à décomposition fréquentielle (FBD) basé sur des modulateurs à temps-discret pour unrécepteur SDR supportant les standards E-GSM, UMTS et IEEE802.11a. La nouveauté dans l’architectureproposée est qu’il est programmable, la numérisation d’un signal issu d’un standard donné se réalise enactivant seulement les branches concernées de l’architecture parallèle avec des sous-bandes defonctionnement et une fréquence d’échantillonnage spécifiée. De plus, le partage fréquentiel des sous-bandesest non uniforme. Après validation du dimensionnement théorique par simulation, l’étage en bande de base aété dimensionné. Cette étude conduit à la définition d’un filtre anti-repliement passif unique d’ordre 6 et detype Butterworth, permettant l’élimination du circuit de contrôle de gain automatique (AGC). L’architectureFBD requière un traitement numérique permettant de combiner les signaux à la sortie des branches enparallèle pour reconstruire le signal de sortie finale. Un dimensionnement optimisé de cet étage numérique àbase de démodulation a été proposé. La synthèse de l’étage en bande de base a montré des problèmes destabilité des modulateurs . Pour y remédier, une solution basée sur la modification de la fonction detransfert du signal (STF) afin de filtrer les signaux hors bande d’intérêt par branche a été élaborée. Unediscontinuité de phase a été également constatée dans le signal de sortie reconstruit. Une solution deraccordement de phase a été proposée. L’étude analytique et la conception niveau système ont étécomplétées par une implantation de la reconstruction numérique de l’ADC parallèle. Deux flots de conceptionont été considérés, un associé au FPGA et l’autre indépendant de la cible choisie (VHDL standard).L’architecture proposée a été validée sur un FPGA Xilinx de type VIRTEX6. Une dynamique de 74 dB a étémesurée pour le cas d’étude UMTS, ce qui est compatible avec celle requise du standard UMTS

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware