Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces

Dopamiinin hapettumisen lukija-anturirajapinta 65 nm CMOS teknologialla

Sensing and monitoring of neural activities within the central nervous system has become a fast-growing area of research due to the need to understand more about how neurons communicate. Several neurological disorders such as Parkinson’s disease, Schizophrenia, Alzeihmers and Epilepsy have been reported to be associated with imbalance in the concentration of neurotransmitters such as glutamate and dopamine [1] - [5]. Hence, this thesis proposes a solution for the measurement of dopamine concentration in the brain during neural communication. The proposed design of the dopamine oxidation readout sensor interface is based on a mixed-signal front-end architecture for minimizing noise and high resolution of detected current signals. The analog front-end is designed for acquisition and amplification of current signals resulting from oxidation and reduction at the biosensor electrodes in the brain. The digital signal processing (DSP) block is used for discretization of detected dopamine oxidation and reduction current signals that can be further processed by an external system. The results from the simulation of the proposed design show that the readout circuit has a current resolution of 100 pA and can detect minimum dopamine concentration of 10 μMol based on measured data from novel diamond-like carbon electrodes [6]. Higher dopamine concentration can be detected from the sensor interface due to its support for a wide current range of 1.2 μA(±600 nA). The digital code representation of the detected dopamine has a resolution of 14.3-bits with RMS conversion error of 0.18 LSB which results in an SNR of 88 dB at full current range input. However, the attained ENOB is 8-bits due to the effect of nonlinearity in the oscillator based ADC. Nonetheless, the achieved resolution of the readout circuit provides good sensitivity of released dopamine in the brain which is useful for further understanding of neurotransmitters and fostering research into improved treatments of related neurodegenerative diseases.Keskushermoston aktiivisuuden havainnointi ja tarkkailu on muodostunut tärkeäksi tutkimusalaksi, sillä tarve ymmärtää neuronien viestintää on kasvanut. Monien hermostollisten sairauksien kuten Parkinsonin taudin, skitsofrenian, Alzheimerin taudin ja epilepsian on huomattu aiheuttavan muutoksia välittäjäaineiden, kuten glutamaatin ja dopamiinin, pitoisuuksissa [1] - [5]. Aiheeseen liittyen tässä työssä esitetään ratkaisu dopamiinipitoisuuden mittaamiseksi aivoista. Esitetty dopamiinipitoisuuden lukijapiiri perustuu sekamuotoiseen etupäärakenteeseen, jolla saavutetaan matala kohinataso ja hyvä tarkkuus signaalien ilmaisemisessa. Suunniteltu analoginen etupää kykenee lukemaan ja vahvistamaan dopamiinipitoisuuden muutosten aiheuttamia virran muutoksia aivoihin asennetuista elektrodeista. Digitaalisen signaalinkäsittelyn avulla voidaan havaita dopamiinin hapettumis-ja pelkistymisvirtasignaalit, ja välittää ne edelleen ulkoisen järjestelmän muokattavaksi. Simulaatiotulokset osoittavat, että suunniteltu piiri saavuttaa 100 pA virran erottelukyvyn. Simuloinnin perustuessa hiilipohjaisiin dopamiinielektrodeihin piiri voi havaita 10 μMol dopamiinipitoisuuden [6]. Myös suurempia dopamiinipitoisuuksia voidaan havaita, sillä etupäärajapinta tukee 1.2 μA(±600 nA) virta-aluetta. Digitaalinen esitysmuoto tukee 14.3 bitin esitystarkkuutta 0.18 bitin RMS virheellä saavuttaen 88 dB dynaamisen virta-alueen. Saavutettu ENOB (tehollinen bittimäärä) on kuitenkin 8 bittiä oskillaattoripohjaisen ADC:n (analogia-digitaalimuuntimen) epälineaarisuuden takia. Saavutettu tarkkuus tuottaa hyvän herkkyyden dopamiinin havaitsemiseksi ja hyödyttää siten välittäjäainetutkimusta ja uusien hoitomuotojen kehittämistä hermostollisiin sairauksiin

Digital-based analog processing in nanoscale CMOS ICs for IoT applications

The Internet-of-Things (IoT) concept has been opening up a variety of applications, such as urban and environmental monitoring, smart health, surveillance, and home automation. Most of these IoT applications require more and more power/area efficient Complemen tary Metal–Oxide–Semiconductor (CMOS) systems and faster prototypes (lower time-to market), demanding special modifications in the current IoT design system bottleneck: the analog/RF interfaces. Specially after the 2000s, it is evident that there have been significant improvements in CMOS digital circuits when compared to analog building blocks. Digital circuits have been taking advantage of CMOS technology scaling in terms of speed, power consump tion, and cost, while the techniques running behind the analog signal processing are still lagging. To decrease this historical gap, there has been an increasing trend in finding alternative IC design strategies to implement typical analog functions exploiting Digital in-Concept Design Methodologies (DCDM). This idea of re-thinking analog functions in digital terms has shown that Analog ICs blocks can also avail of the feature-size shrinking and energy efficiency of new technologies. This thesis deals with the development of DCDM, demonstrating its compatibility for Ultra-Low-Voltage (ULV) and Power (ULP) IoT applications. This work proves this state ment through the proposing of new digital-based analog blocks, such as an Operational Transconductance Amplifiers (OTAs) and an ac-coupled Bio-signal Amplifier (BioAmp). As an initial contribution, for the first time, a silicon demonstration of an embryonic Digital-Based OTA (DB-OTA) published in 2013 is exhibited. The fabricated DB-OTA test chip occupies a compact area of 1,426 µm2 , operating at supply voltages (VDD) down to 300 mV, consuming only 590 pW while driving a capacitive load of 80pF. With a Total Harmonic Distortion (THD) lower than 5% for a 100mV input signal swing, its measured small-signal figure of merit (FOMS) and large-signal figure of merit (FOML) are 2,101 V −1 and 1,070, respectively. To the best of this thesis author’s knowledge, this measured power is the lowest reported to date in OTA literature, and its figures of merit are the best in sub-500mV OTAs reported to date. As the second step, mainly due to the robustness limitation of previous DB-OTA, a novel calibration-free digital-based topology is proposed, named here as Digital OTA (DIG OTA). A 180-nm DIGOTA test chip is also developed exhibiting an area below the 1000 µm2 wall, 2.4nW power under 150pF load, and a minimum VDD of 0.25 V. The proposed DIGOTA is more digital-like compared with DB-OTA since no pseudo-resistor is needed. As the last contribution, the previously proposed DIGOTA is then used as a building block to demonstrate the operation principle of power-efficient ULV and ultra-low area (ULA) fully-differential, digital-based Operational Transconductance Amplifier (OTA), suitable for microscale biosensing applications (BioDIGOTA) such as extreme low area Body Dust. Measured results in 180nm CMOS confirm that the proposed BioDIGOTA can work with a supply voltage down to 400 mV, consuming only 95 nW. The BioDIGOTA layout occupies only 0.022 mm2 of total silicon area, lowering the area by 3.22X times compared to the current state of the art while keeping reasonable system performance, such as 7.6 Noise Efficiency Factor (NEF) with 1.25 µVRMS input-referred noise over a 10 Hz bandwidth, 1.8% of THD, 62 dB of the common-mode rejection ratio (CMRR) and 55 dB of power supply rejection ratio (PSRR). After reviewing the current DCDM trend and all proposed silicon demonstrations, the thesis concludes that, despite the current analog design strategies involved during the analog block development

Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

Analog Compressive Sensing for Multi-Channel Neural Recording: Modeling and Circuit Level Implementation

RÉSUMÉ Dans cette thèse, nous présentons la conception d’un implant d’enregistrement neuronal multicanaux avec un échantillonnage compressé mis en oeuvre avec un procédé de fabrication CMOS à 65 nm. La réduction de la technologie a˙ecte à la baisse les paramètres des amplificateurs neuronaux couplés en AC, comme la fréquence de coupure basse, en raison de l’e˙et de canal court des transistors MOS. Nous analysons la fréquence de coupure basse et nous constatons que l’origine de ce problème, dans les technologies avancées, est la diminution de l’impédance d’entrée de l’amplificateur opérationnel de transconductance (OTA) en raison de la fuite d’oxyde de grille à l’entrée des OTA. Nous proposons deux solutions pour réduire la fréquence de coupure basse sans augmenter la valeur des condensateurs de rétroaction de l’étage d’entrée. La première solution est appelée rétroaction positive croisée et la deuxième solution utilise des PMOS à oxyde épais dans la paire de l’entrée di˙érentielle de l’OTA. Il est à noter que pour compresser le signal neuronal, nous utilisons le CS dans le domaine analogique. Pour la réalisation, un intégrateur à capacité commutée est requis. Les paramètres non idéaux de l’OTA utilisé dans cet intégrateur, tels que le gain fini, la bande passante, la vitesse de balayage et le changement rapide de la sortie. Toutes ces imperfections induisent des erreurs et réduisent le rapport signal sur bruit (SNR) total. Nous avons simulé ces imperfections sur Matlab et Simulink pour définir les spécifications de l’OTA requis. Aussi, pour concevoir les circuits analogiques correspondant aux interfaces neuronales requises, tels qu’un amplificateur neuronal, une référence de tension compacte et à faible consommation d’énergie est requise. Nous avons proposé une référence de tension de faible consommation d’énergie sans utiliser le transistor bipolaire parasite de la technologie CMOS pour diminuer la surface de silicium requise. Finalement, nous avons complété l’encodeur de CS et un convertisseur analogique-numérique à approximation successive (SAR ADC) requis pour la chaine d’enregistrement des signaux neuronaux dans ce projet.----------ABSTRACT In this thesis we present the design of a multi-channel neural recording implant with analog compressive sensing (CS) in 65 nm process. Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cuto˙ frequency due to the short-channel e˙ects of MOS transistors. We analyze the low-cuto˙ frequency and find that the main reason of this problem in advanced technologies is decreasing the input resistance of the operational transconductance amplifier (OTA) due to the gate oxide static current leakage in the input of the OTA. In advanced technologies, the gate oxide is thin and some electrons can penetrate to the channel and cause DC current leakage. We proposed two solutions to reduce the low-cuto˙ frequency without increasing the value of the feedback capacitors of the front-end neural amplifier. The first solution is called cross-coupled positive feedback, and the second solution is utilizing thick-oxide PMOS transistors in the input di˙erential pair of the OTA. Compress the neural signal, we utilized the CS method in analog domain. For its implementation, a switched-capacitor integrator is required. Non-ideal specifications of OTA of CS integrator such as finite gain, bandwidth, slew rate and output swing induce error and reduce the total signal to noise ratio (SNR). We simulated these non-idealities in Matlab and Simulink and extracted the specification of the required OTA. Also, to design analog circuits such as neural amplifier a low power and compact voltage reference is required. We implemented a low-power band-gap reference without utilizing parasitic bipolar transis-tor to decrease the silicon area. At the end, we completed the CS encoder and successive approximation architecture analog-to-digital converter (SAR ADC)

700mV low power low noise implantable neural recording system design

This dissertation presents the work for design and implementation of a low power, low noise neural recording system consisting of Bandpass Amplifier and Pipelined Analog to Digital Converter (ADC) for recording neural signal activities. A low power, low noise two stage neural amplifier for use in an intelligent Radio-Frequency Identification (RFID) based on folded cascode Operational Transconductance Amplifier (OTA) is utilized to amplify the neural signals. The optimization of the number of amplifier stages is discussed to achieve the minimum power and area consumption. The amplifier power supply is 0.7V. The midband gain of amplifier is 58.4dB with a 3dB bandwidth from 0.71 to 8.26 kHz. Measured input-referred noise and total power consumption are 20.7 μVrms and 1.90 μW respectively. The measured result shows that the optimizing the number of stages can achieve lower power consumption and demonstrates the neural amplifier's suitability for instu neutral activity recording. The advantage of power consumption of Pipelined ADC over Successive Approximation Register (SAR) ADC and Delta-Sigma ADC is discussed. An 8 bit fully differential (FD) Pipeline ADC for use in a smart RFID is presented in this dissertation. The Multiplying Digital to Analog Converter (MDAC) utilizes a novel offset cancellation technique robust to device leakage to reduce the input drift voltage. Simulation results of static and dynamic performance show this low power Pipeline ADC is suitable for multi-channel neural recording applications. The performance of all proposed building blocks is verified through test chips fabricated in IBM 180nm CMOS process. Both bench-top and real animal test results demonstrate the system's capability of recording neural signals for neural spike detection

Network Electrophysiology Sensor-On-A- Chip

Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG) bio-potential signals are commonly recorded in clinical practice. Typically, patients are connected to a bulky and mains-powered instrument, which reduces their mobility and creates discomfort. This limits the acquisition time, prevents the continuous monitoring of patients, and can affect the diagnosis of illness. Therefore, there is a great demand for low-power, small-size, and ambulatory bio-potential signal acquisition systems. Recent work on instrumentation amplifier design for bio-potential signals can be broadly classified as using one or both of two popular techniques: In the first, an AC-coupled signal path with a MOS-Bipolar pseudo resistor is used to obtain a low-frequency cutoff that passes the signal of interest while rejecting large dc offsets. In the second, a chopper stabilization technique is designed to reduce 1/f noise at low frequencies. However, both of these existing techniques lack control of low-frequency cutoff. This thesis presents the design of a mixed- signal integrated circuit (IC) prototype to provide complete, programmable analog signal conditioning and analog-to-digital conversion of an electrophysiologic signal. A front-end amplifier is designed with low input referred noise of 1 uVrms, and common mode rejection ratio 102 dB. A novel second order sigma-delta analog- to-digital converter (ADC) with a feedback integrator from the sigma-delta output is presented to program the low-frequency cutoff, and to enable wide input common mode range of ¡Ãƒâ€œ0.3 V. The overall system is implemented in Jazz Semiconductor 0.18 um CMOS technology with power consumption 5.8 mW from ¡Ãƒâ€œ0.9V power supplies

Modulated Backscatter for Low-Power High-Bandwidth Communication

<p>This thesis re-examines the physical layer of a communication link in order to increase the energy efficiency of a remote device or sensor. Backscatter modulation allows a remote device to wirelessly telemeter information without operating a traditional transceiver. Instead, a backscatter device leverages a carrier transmitted by an access point or base station.</p><p>A low-power multi-state vector backscatter modulation technique is presented where quadrature amplitude modulation (QAM) signalling is generated without running a traditional transceiver. Backscatter QAM allows for significant power savings compared to traditional wireless communication schemes. For example, a device presented in this thesis that implements 16-QAM backscatter modulation is capable of streaming data at 96 Mbps with a radio communication efficiency of 15.5 pJ/bit. This is over 100x lower energy per bit than WiFi (IEEE 802.11).</p><p>This work could lead to a new class of high-bandwidth sensors or implantables with power consumption far lower than traditional radios.</p>Dissertatio

A Low-Power BFSK/OOK Transmitter for Wireless Sensors

In recent years, significant improvements in semiconductor technology have allowed consistent development of wireless chipsets in terms of functionality and form factor. This has opened up a broad range of applications for implantable wireless sensors and telemetry devices in multiple categories, such as military, industrial, and medical uses. The nature of these applications often requires the wireless sensors to be low-weight and energy-efficient to achieve long battery life. Among the various functions of these sensors, the communication block, used to transmit the gathered data, is typically the most power-hungry block. In typical wireless sensor networks, transmission range is below 10 meters and required radiated power is below 1 milliwatt. In such cases, power consumption of the frequency-synthesis circuits prior to the power amplifier of the transmitter becomes significant. Reducing this power consumption is currently the focus of various research endeavors. A popular method of achieving this goal is using a direct-modulation transmitter where the generated carrier is directly modulated with baseband data using simple modulation schemes. Among the different variations of direct-modulation transmitters, transmitters using unlocked digitally-controlled oscillators and transmitters with injection or resonator-locked oscillators are widely investigated because of their simple structure. These transmitters can achieve low-power and stable operation either with the help of recalibration or by sacrificing tuning capability. In contrast, phase-locked-loop-based (PLL) transmitters are less researched. The PLL uses a feedback loop to lock the carrier to a reference frequency with a programmable ratio and thus achieves good frequency stability and convenient tunability. This work focuses on PLL-based transmitters. The initial goal of this work is to reduce the power consumption of the oscillator and frequency divider, the two most power-consuming blocks in a PLL. Novel topologies for these two blocks are proposed which achieve ultra-low-power operation. Along with measured performance, mathematical analysis to derive rule-of-thumb design approaches are presented. Finally, the full transmitter is implemented using these blocks in a 130 nanometer CMOS process and is successfully tested for low-power operation