Design of a Programmable Passive SoC for Biomedical Applications Using RFID ISO 15693/NFC5 Interface

Low power, low cost inductively powered passive biotelemetry system involving fully customized RFID/NFC interface base SoC has gained popularity in the last decades. However, most of the SoCs developed are application specific and lacks either on-chip computational or sensor readout capability. In this paper, we present design details of a programmable passive SoC in compliance with ISO 15693/NFC5 standard for biomedical applications. The integrated system consists of a 32-bit microcontroller, a sensor readout circuit, a 12-bit SAR type ADC, 16 kB RAM, 16 kB ROM and other digital peripherals. The design is implemented in a 0.18 μ m CMOS technology and used a die area of 1.52 mm × 3.24 mm. The simulated maximum power consumption of the analog block is 592 μ W. The number of external components required by the SoC is limited to an external memory device, sensors, antenna and some passive components. The external memory device contains the application specific firmware. Based on the application, the firmware can be modified accordingly. The SoC design is suitable for medical implants to measure physiological parameters like temperature, pressure or ECG. As an application example, the authors have proposed a bioimplant to measure arterial blood pressure for patients suffering from Peripheral Artery Disease (PAD)

A 36 µW 1.1 mm2 reconfigurable analog front-end for cardiovascular and respiratory signals recording

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents a 1.2 V 36 µW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates 1/f noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.Peer ReviewedPostprint (author's final draft

A Novel Power-Efficient Wireless Multi-channel Recording System for the Telemonitoring of Electroencephalography (EEG)

This research introduces the development of a novel EEG recording system that is modular, batteryless, and wireless (untethered) with the supporting theoretical foundation in wireless communications and related design elements and circuitry. Its modular construct overcomes the EEG scaling problem and makes it easier for reconfiguring the hardware design in terms of the number and placement of electrodes and type of standard EEG system contemplated for use. In this development, portability, lightweight, and applicability to other clinical applications that rely on EEG data are sought. Due to printer tolerance, the 3D printed cap consists of 61 electrode placements. This recording capacity can however extend from 21 (as in the international 10-20 systems) up to 61 EEG channels at sample rates ranging from 250 to 1000 Hz and the transfer of the raw EEG signal using a standard allocated frequency as a data carrier. The main objectives of this dissertation are to (1) eliminate the need for heavy mounted batteries, (2) overcome the requirement for bulky power systems, and (3) avoid the use of data cables to untether the EEG system from the subject for a more practical and less restrictive setting. Unpredictability and temporal variations of the EEG input make developing a battery-free and cable-free EEG reading device challenging. Professional high-quality and high-resolution analog front ends are required to capture non-stationary EEG signals at microvolt levels. The primary components of the proposed setup are the wireless power transmission unit, which consists of a power amplifier, highly efficient resonant-inductive link, rectification, regulation, and power management units, as well as the analog front end, which consists of an analog to digital converter, pre-amplification unit, filtering unit, host microprocessor, and the wireless communication unit. These must all be compatible with the rest of the system and must use the least amount of power possible while minimizing the presence of noise and the attenuation of the recorded signal A highly efficient resonant-inductive coupling link is developed to decrease power transmission dissipation. Magnetized materials were utilized to steer electromagnetic flux and decrease route and medium loss while transmitting the required energy with low dissipation. Signal pre-amplification is handled by the front-end active electrodes. Standard bio-amplifier design approaches are combined to accomplish this purpose, and a thorough investigation of the optimum ADC, microcontroller, and transceiver units has been carried out. We can minimize overall system weight and power consumption by employing battery-less and cable-free EEG readout system designs, consequently giving patients more comfort and freedom of movement. Similarly, the solutions are designed to match the performance of medical-grade equipment. The captured electrical impulses using the proposed setup can be stored for various uses, including classification, prediction, 3D source localization, and for monitoring and diagnosing different brain disorders. All the proposed designs and supporting mathematical derivations were validated through empirical and software-simulated experiments. Many of the proposed designs, including the 3D head cap, the wireless power transmission unit, and the pre-amplification unit, are already fabricated, and the schematic circuits and simulation results were based on Spice, Altium, and high-frequency structure simulator (HFSS) software. The fully integrated head cap to be fabricated would require embedding the active electrodes into the 3D headset and applying current technological advances to miniaturize some of the design elements developed in this dissertation

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.

A Smart IoT Node using a Hybrid Edge-Computing Strategy for Environmental Multiparameter Sensing

The Internet of Things (IoT) has been growing at an immense pace over the last few years and there are no predictions of slowing down anytime soon, but most importantly, not only has it been growing in size but it has also been growing in capabilities, performance and diversity. Diversity is incredibly important but also fracturing, in this context. As IoT sensor nodes get more performant and diverse, their adaptability and reconfigurability ends up being lost in the search for ultimate performance. As a way to unify these individual single purpose sensor nodes, a need and an opportunity present themselves to develop a singular multi-parameter, multi-sensor IoT node, that can make use of the latest reconfigurable technology to adapt itself to the requirements of each type of sensor, while maintaining the very high performance and precision of dedicated sensor nodes. This dissertation work will thus focus on developing an architecture and building a prototype circuit board for a multi-sensor, reconfigurable IoT node based on a state-ofthe- art System-on-Chip (SoC) with extremely high resolution measurement capabilities, which can interface with virtually any type of existing sensor. This architecture and prototype are intended to serve as a stepping stone in the path to develop a capable IoT node which can interface with a wider range of sensor and have a higher precision than what is currently availableA Internet of Things (IoT) tem vindo a crescer a passos largos ao longo dos últimos anos, e não apresenta quaisquer sinais de abrandar o seu crescimento num futuro próximo. No entanto, não só tem vindo a crescer em tamanho, mas também nas suas capacidades, performance e diversidade. Enquanto que a diversidade é extremamente importante, neste contexto, é também fraturante. À medida que os nós IoT melhoram em performance e diversidade, a sua adaptabilidade e reconfigurabiliade acaba por ficar em segundo plano na procura do pico de performance. Com o objetivo de unificar estes nós de sensores com propósitos singulares, apresentase uma oportunidade e uma necessidade de desenvolver um único nó IoT capaz de fazer interface com uma multiplicidade de sensores distintos, usando tecnologia de ponta reconfigurável para se adaptar às necessidades de cada tipo de sensor, mantendo ainda assim a alta performance e precisão de nós de sensor dedicados. A presente dissertação irá então focar-se no desenvolvimento de uma arquitetura de sistema e criação de um protótipo em placa de circuito impresso referente a um nó IoT multisensor reconfigurável, baseado num SoC de última geração com capacidades de medição extremamente elevadas, que consiga fazer interface com qualquer tipo de sensor existente. Esta arquitetura de sistema e protótipo são desenvolvidos com a intenção de servirem como ponto de partida para o desenvolvimento de um nó IoT de interface com sensores, que tenha a capacidade de medir qualquer tipo de sensor com uma precisão superior àquilo que está atualmente disponível

PROCESS AWARE ANALOG-CENTRIC SINGLE LEAD ECG ACQUISITION AND CLASSIFICATION CMOS FRONTEND

The primary objective of this research work is the development of a low power single-lead ECG analog front-end (AFE) architecture which includes acquisition, digitization, process aware efficient gain and frequency control mechanism and a low complexity classifier for the detecting asystole, extreme bardycardia and tachycardia. Recent research on ECG recording systems focuses on the design of a compact single-lead wearable/portable devices with ultra-low-power consumption and in-built hardware for diagnosis and prognosis. Since, the amplitude of the ECG signal varies from hundreds of µV to a few mV, and has a bandwidth of DC to 250 Hz, conventional front-ends use an instrument amplifier followed by a programmable gain amplifier (PGA) to amplify the input ECG signal appropriately. This work presents an mixed signal ECG fronted with an ultra-low power two-stage capacitive-coupled signal conditioning circuit (or an AFE), providing programmable amplification along with tunable 2nd order high pass and lowpass filter characteristics. In the contemporary state-of-the-art ECG recording systems, the gain of the amplifier is controlled by external digital control pins which are in turn dynamically controlled through a DSP. Therefore, an efficient automatic gain control mechanism with minimal area overhead and consuming power in the order of nano watts only. The AGC turns the subsequent ADC on only after output of the PGA (or input of the ADC) reaches a level for which the ADC achieves maximum signal-to-noise-ratio (SNR), hence saving considerable startup power and avoiding the use of DSP. Further, in any practical filter design, the low pass cut-off frequency is prone to deviate from its nominal value across process and temperature variations. Therefore, post-fabrication calibration is essential, before the signal is fed to an ADC, to minimize this deviation, prevent signal degradation due to aliasing of higher frequencies into the bandwidth for classification of ECG signals, to switch to low resolution processing, hence saving power and enhances battery lifetime. Another short-coming noticed in the literature published so far is that the classification algorithm is implemented in digital domain, which turns out to be a power hungry approach. Moreover, Although analog domain implementations of QRS complexes detection schemes have been reported, they employ an external micro-controller to determine the threshold voltage. In this regard, finally a power-efficient low complexity CMOS fully analog classifier architecture and a heart rate estimator is added to the above scheme. It reduces the overall system power consumption by reducing the computational burden on the DSP. The complete proposed scheme consists of (i) an ultra-low power QRS complex detection circuit using an autonomous dynamic threshold voltage, hence discarding the need of any external microcontroller/DSP and calibration (ii) a power efficient analog classifier for the detection of three critical alarm types viz. asystole, extreme bradycardia and tachycardia. Additionally, a heart rate estimator that provides the number of QRS complexes within a period of one minute for cardiac rhythm (CR) and heart rate variability (HRV) analysis. The complete proposed architecture is implemented in UMC 0.18 µm CMOS technology with 1.8 V supply. The functionality of each of the individual blocks are successfully validated using postextraction process corner simulations and through real ECG test signals taken from the PhysioNet database. The capacitive feedback amplifier, Σ∆ ADC, AGC and the AFT are fabricated, and the measurement results are discussed here. The analog classification scheme is successfully validated using embed NXP LPC1768 board, discrete peak detector prototype and FPGA software interfac

An Energy Efficient Power Converter for Zero Power Wearable Devices

Early diagnosis of Alzheimer's and epilepsy requires monitoring a subject's development of symptoms through electroencephalography (EEG) signals over long periods. Wearable devices enable convenient monitoring of biosignals, unlike complex and costly hospital equipment. The key to achieving a fit and forgettable wearable device is to increase its operating cycle and decrease its size and weight. Instead of batteries, which limit the life cycle of electronic devices and set their form factor, body heat and environmental light can power wearable devices through energy-scavenging technologies. The harvester transducers should be tailored according to on the application and the sensor placement. This leaves a wide variety of transducers with an extensive range of impedances and voltages. To realize an autonomous wearable device, the power converter energy harvester, has to be very efficient and maintain its efficiency despite potential transducer replacement or variations in environmental conditions. This thesis presents a detailed design of an efficient integrated power converter for use in an autonomous wearable device. The design is based on the examination of both power losses and power transfer in the power converter. The efficiency bound of the converter is derived from the specifications of its transducer. The tuning ranges for the reconfigurable parameters are extracted to keep the converter efficient with variations in the transducer specifications. With the efficient design and the manual tuning of the reconfigurable parameters, the converter can work optimally with different types of transducers, and keeps its efficiency in the conversion of low voltages from the harvesters. Measurements of the designed converter demonstrate an efficiency of higher than 50% and 70% with two different transducers having an open-circuit voltage as low as 20 mV and 100 mV, respectively. The power converter should be able to reconfigure itself without manual tunings to keep its efficiency despite changes in the harvesters' specifications. The second portion of this dissertation addresses this issue with a proposed design methodology to implement a control section. The control section adjusts the converter's reconfigurable parameters by examining the power transfer and loss and through concurrent closed loops. The concurrent loops working together raise a serious concern regarding stability. The system is designed and analyzed in the time domain with the state-space averaging (SSA) model to address the stability issue. The ultra-low-power control section obtained from the SSA model estimates the power and loss with a reasonable accuracy, and adjusts the timings in a stable manner. The entire control section consumes only 30 nW dynamic power at 10 kHz. The control section tunes the converter's speed or its working frequency depending on the available power. The frequency clocks the entire architecture, which is designed asynchronously; therefore, the power consumption of the system depends on the power available from the transducer. The system is implemented using 0.18 µm CMOS technology. For an input as low as 7 mV, the converter is not only functional but also has an efficiency of more than 40%. The efficiency can reach 70% with an input voltage of 50 mV. The system operates in a range of just a few of millivolts to half a volt with ample efficiencies. It can work at an optimal point with different transducers and environmental conditions

Low-Power Reconfigurable Sensing Circuitry for the Internet-of-Things Paradigm

With ubiquitous wireless communication via Wi-Fi and nascent 5th Generation mobile communications, more devices -- both smart and traditionally dumb -- will be interconnected than ever before. This burgeoning trend is referred to as the Internet-of-Things. These new sensing opportunities place a larger burden on the underlying circuitry that must operate on finite battery power and/or within energy-constrained environments. New developments of low-power reconfigurable analog sensing platforms like field-programmable analog arrays (FPAAs) present an attractive sensing solution by processing data in the analog domain while staying flexible in design. This work addresses some of the contemporary challenges of low-power wireless sensing via traditional application-specific sensing and with FPAAs. A large emphasis is placed on furthering the development of FPAAs by making them more accessible to designers without a strong integrated-circuit background -- much like FPGAs have done for digital designers

A highly digital, reconfigurable and voltage scalable SAR ADC

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-112).Micropower sensor networks have a broad range of applications which include military surveillance, environmental monitoring, chemical detection and more recently, medical monitoring systems. Each node of the sensor network requires energy efficient circuits powered off small batteries or harvested energy. In such systems, a single reconfigurable analog-to-digital converter (ADC) is needed to digitize a wide range of signals with varying bandwidth and resolution requirements. This thesis describes the design of an ADC whose power scales exponentially with resolution and linearly with frequency to maximize the system lifetime. The proposed ADC has reconfigurable resolution from 5 to 10-bits and a scalable sample rate from 0 to 1-MS/s. The successive approximation register (SAR) architecture was chosen for its highly digital nature which enables low voltage operation. The supply voltage can be scaled from 1V down to 0.4V such that the ADC maintains a constant energy efficiency across all modes of operation when normalized with respect to sample rate and resolution. A capacitive digital-to -analog converter (DAC) in a split capacitor topology with a sub-DAC is used to minimize the DAC power and area. Top plate switches are used to decouple the MSB capacitors as resolution is scaled to avoid parasitic loading of the DAC. The DAC capacitors are laid out in a common-centroid configuration with edge effects minimized at each resolution mode to improve matching. A fully dynamic latched comparator is used to avoid static bias currents.(cont.) Power gating of the digital logic is used to reduce leakage power at low sample rates. Reconfigurability between single-ended or differential modes enables a power versus performance trade-off. Lastly, programmable sampling duration and internal bootstrapping is used to maintain sampling linearity at low voltages. The ADC has been submitted for fabrication in a low power 65nm digital CMOS process and simulation results are presented.by Marcus Yip.S.M

A Low-Power, Reconfigurable, Pipelined ADC with Automatic Adaptation for Implantable Bioimpedance Applications

Biomedical monitoring systems that observe various physiological parameters or electrochemical reactions typically cannot expect signals with fixed amplitude or frequency as signal properties can vary greatly even among similar biosignals. Furthermore, advancements in biomedical research have resulted in more elaborate biosignal monitoring schemes which allow the continuous acquisition of important patient information. Conventional ADCs with a fixed resolution and sampling rate are not able to adapt to signals with a wide range of variation. As a result, reconfigurable analog-to-digital converters (ADC) have become increasingly more attractive for implantable biosensor systems. These converters are able to change their operable resolution, sampling rate, or both in order convert changing signals with increased power efficiency. Traditionally, biomedical sensing applications were limited to low frequencies. Therefore, much of the research on ADCs for biomedical applications focused on minimizing power consumption with smaller bias currents resulting in low sampling rates. However, recently bioimpedance monitoring has become more popular because of its healthcare possibilities. Bioimpedance monitoring involves injecting an AC current into a biosample and measuring the corresponding voltage drop. The frequency of the injected current greatly affects the amplitude and phase of the voltage drop as biological tissue is comprised of resistive and capacitive elements. For this reason, a full spectrum of measurements from 100 Hz to 10-100 MHz is required to gain a full understanding of the impedance. For this type of implantable biomedical application, the typical low power, low sampling rate analog-to-digital converter is insufficient. A different optimization of power and performance must be achieved. Since SAR ADC power consumption scales heavily with sampling rate, the converters that sample fast enough to be attractive for bioimpedance monitoring do not have a figure-of-merit that is comparable to the slower converters. Therefore, an auto-adapting, reconfigurable pipelined analog-to-digital converter is proposed. The converter can operate with either 8 or 10 bits of resolution and with a sampling rate of 0.1 or 20 MS/s. Additionally, the resolution and sampling rate are automatically determined by the converter itself based on the input signal. This way, power efficiency is increased for input signals of varying frequency and amplitude