Smart Systems Integration for Autonomous Wireless Communications

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Mina Danesh

Smart Systems Integration

for

Autonomous

Wireles s Communications

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S

MART

S

YSTEMS

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NTEGRATION

FOR

A

UTONOMOUS

W

IRELESS

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OMMUNICATIONS

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Smart Systems Integration for Autonomous Wireless Communications

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op dinsdag 8 mei 2012 om 12:30 uur

door

Mina DANESH

Electrical Engineer

Master of Applied Sciences, University of Toronto, Toronto, Canada Geboren te Montréal, Canada

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Dit proefschrift is goedgekeurd door de promotor: Prof. dr. J. R. Long

Samenstelling promotiecommissie: Rector Magnificus, voorzitter

Prof. dr. J. R. Long, Technische Universiteit Delft, promotor Prof. dr. P.M. Sarro, Technische Universiteit Delft

Prof. dr. M. Zeman, Technische Universiteit Delft Prof. dr.ir. B. Nauta, Universiteit Twente

Prof. dr. J.R. Mosig, École Polytechnique Fédérale de Lausanne Prof. Dr.-Ing. H. Schumacher, Universität Ulm

Prof. Dr.-Ing. D. Manteuffel, Universität Kiel

All rights reserved. No part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without any prior permission of the copyright owner.

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To anyone who perseveres in pursuing her or his dream(s)…

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Abstract

Integration of sensors and wireless transceivers for system networking aims at emerging applications that are highly integrated, self-powered, and low cost. These applications include: health monitoring and body-area networks (BAN), environment and industrial process monitoring, surveillance, security, in-vehicle sensor networks, building automation, and personal-area networks (PAN). These typically rely on short-range communication (≤ 100 m) at low data rates (< 250 kbps). The deployment of such wireless systems relies on efficient power management schemes to prolong lifetime, thus eliminating the need for batteries as a limited primary source of energy.

Autonomous wireless transmitter sensor nodes that harvest solar energy outdoors and light energy indoors via a solar cell or photovoltaic module are developed. Photovoltaic (PV) antennas are used as both DC power source and radio-frequency (RF) radiator or receptor. Sharing the area consumed by the primary DC power source with the antenna reduces the size and overall cost of the transceiver, resulting in a smarter integrated wireless system. PV antenna and DC power management circuit design guidelines are provided. With average power consumptions of just a few µWs for the electronics, energy scavenging is feasible in the system design. The overall package consists of a temperature sensor, a supercapacitor or a rechargeable battery, DC power management, digital signal processing, analog, RF circuitry, and a PV antenna.

The transmitter RF front-end is designed for ultra wideband (UWB) communications to take advantage of the UWB low-power transmit power requirements (37 µW radiated power within a 500 MHz bandwidth) and ease design specifications. It consists of a free-running voltage-controlled oscillator (VCO). A single-ended ring-VCO and a differential

LC-VCO are designed in a 90 nm CMOS process for the 3.1-5.1 GHz (lower) and the

8.5-10.5 GHz (upper) UWB band, respectively. These VCOs are co-integrated with antennas, thus requiring no external matching components while achieving low power DC consumption (1 and 3 mW for the ring and LC-VCO, respectively).

Monopole, dipole, and loop antennas based on amorphous silicon photovoltaic 2 x 2 cm2_{cells are designed for 3.1 to 10.6 GHz UWB communications. The harvested}

light energy is either stored in a supercapacitor (outdoor applications) or a rechargeable battery (indoor applications) to ensure system autonomy. These antenna prototypes are implemented in wireless sensor nodes.

A 3-5 GHz UWB wireless sensor node uses a single solar cell to generate up to 20 mW-peak power outdoors. The solar cell behaves as a broadband monopole antenna in the 3-10 GHz range and is integrated directly to the ring-VCO. The sensor node consumes an average power of 10 µW when transmitting 1 kbps every minute using on-off keying (OOK) 500 MHz bandwidth FM data bursts. Storing 2.85 J of harvested energy on a supercapacitor from the solar cell in a single charge cycle ensures more than 2 days of packet data transmission.

The differential LC-VCO is modulated with a frequency-shift keying (FSK) subcarrier signal. It is integrated to either a PV dipole antenna which uses two solar cells suitable for outdoor applications, and charges a 120 mF supercapacitor, or a flexible PV loop antenna

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Abstract

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which has several cells in series used in an indoor environment, and charges a 12 µAh thin film battery. The sensor nodes transmit data packets every 8.5 s at 10 kbps with average power consumptions on the order of 15 µW. System autonomy in darkness last for 9 h 30 min and 2 h 30 min for the dipole and loop antenna systems, respectively. The FM-UWB modulated signals have a bandwidth of about 600 MHz and comply with the federal communications commission (FCC) mask. Moreover, these sensor nodes are light weight (< 10g), suitable for portable and wearable applications.

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List of Abbreviations and

Symbols

Abbreviations

3D Three-dimensional a-Si Amorphous Silicon

aSi:H Hydrogenated Amorphous Silicon c-Si Crystalline Silicon

mc-Si Monocrystalline Silicon ADC Analog-to-Digital Converter Ag Silver

AGC Automatic Gain Amplifier Al Aluminium

AM Air Mass

AM Amplitude Modulation BER Bit-Error Rate

BFSK Binary Frequency Shift Keying BLE Bluetooth Low Energy

BOM Bill of Material

CP Charge Pump

CEPT European Conference of Postal and Telecommunications Administrations DAC Digital-to-Analog Converter

DFM Design for Manufacturability

CMOS Complementary Metal Oxide Semiconductor COTS Commercial Off-The-Shelf

CPW Coplanar Waveguide DDS Direct Digital Synthesis DSP Digital Signal Processor DVS Dynamic Voltage Scaling EC European Commission

EIRP Equivalent Isotropically Radiated Power EMI Electromagnetic Interference or Immunity EBG Electromagnetic Bandgap

FB Feedback

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List of Abbreviations and Symbols

viii

FLL Frequency-Locked Loop FM Frequency Modulation FPGA Field Programmable Gate Array FSK Frequency Shift Keying FSS Frequency Selective Surface GaAs Gallium Arsenide GSM Groupe Spécial Mobile LC Inductor Capacitor IC Integrated Circuit ICO Current Controlled Oscillator

IEEE Institute of Electrical and Electronics Engineers

IoT Internet of Things

IP Internet Protocol

IR Impulse Radio

ISM Industrial, Scientific and Medical

ITRS International Technology Roadmap for Semiconductors LDR Low Data Rate

LOS Line-of-Sight LPF Low-Pass Filter MAC Medium Access Control MCU Microcontroller Unit MDR Medium Data Rate

MEMS Micro-Electro Mechanical Systems MIM Metal-Insulator-Metal

NLOS Non-Line-of-Sight

NMOS N-channel Metal Oxide Semiconductor NREL National Renewable Energy Laboratory OFDM Orthogonal Frequency-Division Multiplexing OOK On-Off Keying

PCB Printed Circuit Board PFD Phase Frequency Detector

PL Path Loss

PLL Phase-Locked Loop PM Phase Modulation

PMOS P-channel Metal Oxide Semiconductor PPM Pulse Position Modulation

PSK Phase-Shift Keying PV Photovoltaic

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ix PWM Pulse Width Modulation

QoS Quality of Service R2R Roll-to-roll RF Radio-Frequency

RFIC Radio-Frequency Integrated Circuit RFID Radio-Frequency Identification RTLS Real-Time Localization SC Supercapacitor SI Signal Integrity Si Silicon

SiP System-in-Package SnO2 Tin Oxide

SNR Signal-to-Noise Ratio SoP System-on-Package SoC System-on-Chip

SRAM Static Random-Access Memory TSV Through Silicon Via

TWV Through-Wafer Via

VCO Voltage-Controlled Oscillator

UMTS Universal Mobile Telecommunications System UWB Ultra-Wideband

WBAN Wireless Body Area Network

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network WPAN Wireless Personal Area Network WSN Wireless Sensor Network

Symbols

η Efficiency

BW Bandwidth

c Speed of light

Cgb MOS gate-to-body capacitance

Cgd MOS gate-to-drain capacitance

Cgs MOS gate-to-source capacitance

Cox MOS transistor oxide capacitance

Cv Varactor capacitance

d Distance from transmitter

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x

Eavg Average Energy per bit

fc Center frequency

fmax Transistor maximum unity gain frequency (power gain cut-off frequency)

fT Transistor transition frequency

Fm Fade margin

gm Small-signal transistor transconductance

ID MOS drain current

IL Insertion Loss or Implementation Loss

Isc Photovoltaic cell short circuit current

k Transformer coupling factor

M Mutual inductance

n Transformer turns ratio

NF Noise Figure

PL Path Loss

Pn Noise power

PoB Power bandwidth ratio

Psleep Power consumption during sleep mode

Q Quality factor

R Data Rate or Throughput

Ri Input resistance

Ro Output load resistance

Rp Parallel tank resistance

SNR Signal-to-noise ratio

Ton Total time transmitter is operational

Tsm Time between two transmissions or sleep time

VDD Supply voltage

Voc Photovoltaic cell open circuit voltage

VT Tuning voltage

Vth Threshold voltage

ω Angular frequency

Z Characteristic impedance of transmission line

Zi, Zin Input impedance

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Chapter 1 Introduction

« Il est bien plus beau de savoir quelque chose de tout que de savoir tout d’une chose. »

« It is much better to know something of everything than to know everything of one thing. » Blaise Pascal, Mathematician and physicist (1623-1662)

This chapter introduces the topic of smart systems integration and its applications. Autonomous smart systems scavenge energy from the environment to power circuitry using energy harvesting technologies. More specifically in this work, light energy provides the DC power to a wireless sensor node, but the physical characteristics of a photovoltaic cell are exploited such that this device operates as a radio-frequency (RF) antenna simultaneously, thus saving system surface area and cost. This results in a higher integrated package. The concept is applied to ultrawideband (3.1 to 10.6 GHz) communications for which transmitter nodes exploit various design strategies for different applications. This work sets the foundations of autonomous wireless smart systems integration using photovoltaic antennas for light energy harvesting applied to the realm of the Internet of

Things.

1.1 Smart Systems Integration

Smart systems are described as integrated systems which are able to: 1) sense and diagnose a situation and describe it, 2) mutually address and identify each other, 3) predict and decide, 4) operate in a discreet, ubiquitous and quasi-invisible manner, 5) utilize properties of materials, components or processes in an innovative way to achieve greater performance and new functionalities 6) interface, interact, and communicate with the environment and with other smart systems, and 7) perform multiple tasks and assist the user in different activities [1.1].

In other words, a smart system is defined as an integration of different functionalities, consisting of electronic devices such as sensors, actuators, signal processing, data transmission and reception, power supply, and flexible display, which present a high degree of miniaturization at a reasonable cost (< $10i_{). The entire package bridges the gap between}

micro-, nano- or bio-electronics.

Fig. 1.1 illustrates the various functions associated with a smart system, such as energy, intelligence, communication, integration, interoperability, and manufacturing. Energy may be provided through energy harvesting or a low-capacity battery. Energy efficiency is achieved through system (i.e., hardware and software implementations) and circuit design techniques to minimize energy wastage and prolong the operational lifetime

i_{There are no published cost targets. The cost value of much less than $10 is highly desirable by the customer,}

as mentioned in talks. The cost is determined depending on the commercial application and production in volume quantities.

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Chapter 1 – Introduction

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of the system. Energy storage devices (e.g., capacitors and rechargeable batteries) are needed to provide energy when the primary or other sources of energy cannot supply enough power to the circuitry. To increase energy efficiency, low DC circuit active (< 50 mW) and average power (< 100 µW) consumptions are required. This is achieved at the level of system architecture design and applied circuit techniques.

Intelligence implies context awareness capabilities using multifunctional sensors and actuators, integration of memory and processing power, localization in real-time, and adaptive networks using standard or proprietary protocol algorithms. Intelligence is defined by the system architecture and design, by software algorithms and the associated hardware. It can also enable energy efficient systems.

The communication interface includes antenna integration, smart antennas (i.e., adaptive array antennas and beamforming using signal processing), tunable or adaptive radio front-ends, modulation schemes, link reliability, new materials and alternate technologies to implement all functions and blocks in the radio transceiver chain.

Integration consists of system packaging and interconnecting between different system blocks (see Section 1.1.3). Interoperability involves communication between multiple smart systems and ad-hoc communication networks (i.e., a self-configuring or spontaneous network). Manufacturing comprises system assembly, testing, and production. Smart systems are commonly designed for high volume production. Simplicity and ease of assembly helps to reduce total costs.

1.1.1 The Internet of Things

Wireless smart systems were initially envisioned as components of dust networks which aim to make electronic devices disappear into user environments, defining the new paradigm of ambient intelligence [1.2]. The goal is to improve people’s quality of life by creating the desired atmosphere and functionality through intelligent, personalized, interconnected systems and services, wherever they are and whenever they want [1.3], [1.4]. The concept builds on the early ideas of ubiquitous and pervasive computing which anticipates electronic devices are the embedded parts of fine-grained distributed networks [1.5].

This lead to the vision of the Internet of Things (IoT) where smart systems in terms of functionality, technology and application fields will belong to a common communication environment that can bridge the real and virtual worlds by using wireless connectivity [1.6].

Figure 1.1 Smart systems integration. Energy Processing/ intelligence Communication interface Integration Interoperability Manufacturing Smart system

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3 By assigning internet protocol (IP) addresses to objects such as sensors, radio-frequency identification (RFID) tags, short-range wireless communications, and real-time localization, these objects can communicate with each other across the internet in any industrial, commercial, and domestic environment. IoT describes a number of technologies and research disciplines that enable the internet to reach out into the real world of physical objects, such as wireless smart sensor systems [1.7]-[1.9]. This new dimension defines that from anytime, any place connectivity for anyone, there will be connectivity to anything (see

Fig. 1.2(a)) [1.6]. This is extended to the Internet of Nano-Things for objects at the nano-scale. A healthcare application is illustrated in Fig. 1.2(b) [1.10].

The trends in communication and computing for the deployment of wireless systems are illustrated in Fig. 1.3. For mW node devices, the trend is towards increased efficiency and multi-mode, multi-standard capabilities (e.g., GSM, UMTS, Bluetooth, IEEE 802.11). Since the last decade, a relative new area is arising in the microwatt area, aiming for low data rate and low power communication, achieving total autonomy [1.11]. In 2000, PicoRadios were envisioned to support ad-hoc ultra-low power wireless networking.

PicoNodes were defined as small, lightweight, low-cost network elements which are smaller than 1 cm3_{, weigh less than 100 g, dissipate less than 100 µW, support data rates of}

less than 100 kbps, and cost substantially less than 1$. The ultra-low power consumption enables self-powered nodes using energy extracted from the environment, an approach called energy-scavenging or harvesting, eliminating frequent battery replacement. These would be applied to dense wireless networks, such as for intelligent buildings, containing hundreds to thousands of nodes [1.12].

Figure 1.3 Communication trends [1.11].

Data throughput (bps)

Power

Auto_nom ous s_yste

ms

Figure 1.2 Internet of Things (a) new dimension [1.6] and (b) intrabody nanonetworks for healthcare applications

[1.10].

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1.1.2 Applications

Wireless smart systems have many potential applications for both outdoors and indoors, as illustrated in Fig. 1.4 [1.1], [1.12], [1.13]-[1.23].

These applications include:

1) Medical and healthcare monitoring, such as patient monitoring, diagnostics, drug administration in hospitals, and wireless body area-networks (WBAN).

A wireless pressure and temperature sensing device for continuous intraocular pressure monitoring for glaucoma prevention and treatment is demonstrated in [1.24]. The system comprises a system-on-chip (SoC) and an on-board pressure sensor, which is powered via a wireless signal at 2.4 GHz. Its power consumption is 2.3 µW at a supply voltage of 1.5 V. In [1.25], the system consists of two series-connected 1 mm2_{solar cells fabricated in a}

0.18 µm process, a processor core operating at 73 kHz, a 2.4 GHz transmitter with an on-chip antenna, and a 1 µAh rechargeable battery. Its total volume is 8.75 mm3_{and consumes}

7.7 µW in active state. A WBAN with 2.4 GHz transceivers is described in [1.26], which communicate using electronic textiles. The system uses remote charging of a supercapacitor via a transmitter which has a battery. It operates at 10 Mbps with a voltage supply of 0.9 V and consumes 110 µW continuously.

2) Safety and security, such as smart homes which provide security, identification, and anti-counterfeiting, video surveillance, and wildfire monitoring.

3) Environment and infrastructure monitoring, such as temperature and humidity control in office buildings, factories, and greenhouses, soil moisture monitoring in agricultural fields, control and guidance in automatic manufacturing, structural and seismic monitoring of bridges, structural monitoring of aircrafts, building energy management by controlling artificial lighting, temperature, carbon dioxide level, relative humidity, and the positioning of external shading devices.

For building monitoring and control, 2.4 GHz ZigBee wireless sensor nodes are built using off-the-shelf components which include temperature, humidity, acceleration, and motion sensors. They operate autonomously with credit card size solar panels which generate 150 µW indoors to provide energy to a rechargeable battery and supercapacitor [1.27]. (See Chapter 5 for power management devices.)

Environmental wireless sensor networks (WSN), such as for cattle, rainforest ecosystem, and lake water quality monitoring, is explored in [1.16]. The wireless transceiver is made of off-the-shelf components with stackable boards and its energy is

Figure 1.4 Wireless smart systems applications. Wireless smart systems Medical & Healthcare Safety & Security Aeronautics Automotive Real-time localization Environment & infrastructure monitoring Identification

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5 provided by solar panels and a set of rechargeable batteries. The system operates at 915 MHz and covers a range up to a few km.

4) Identification (RFID) for information collection on objects, animals, and people, such as for warehouse inventory.

5) Real-time localization (RTLS) to track and trace objects, animals, and people. 6) Aeronautics, such as inside and outside aircrafts, and satellites.

7) Automotive, such as tire pressure monitoring.

A tire pressure monitoring system is achieved using a three-dimensional (3D) SoC chip-to-wafer technique applying through silicon vias (TSVs) and gold stud bump bonding. It has a FSK transceiver operating at 2.1 or 2.45 GHz at a data rate of 50 kbps. The complete system includes two 8 mAh coin cell batteries, weighs less than 5 g and its packaged volume is 1 cm3_[_1.28_{], [}_1.29_].

For building energy management, wireless smart systems enable a considerable percentage of energy savings and improve human comfort levels, resulting in the potential for a 15-20% savings in total energy usage [1.30]. The ease of deployment of a WSN would replace most of the current “wired” sensor systems [1.31].

In addition to the aforementioned list, smart systems are also integrated into military products and portable electronic devices such as mobile handsets.

1.1.3 Systems package integration

There are four approaches to smart systems integration for embedding the different physical blocks, as illustrated in Figs. 1.5(a)-(d). A system-on-chip (SoC) is a complete system on one chip, including all RF, analog, digital blocks, and on-chip antenna. A multichip module (MCM) consists of components placed on the same plane, which are interconnected together using the substrate. A system-in-package (SiP) brings MCM to a higher level of integration by employing stacked chips or packages for reduced form factors. System-on-package (SoP) optimizes functions between ICs and packages with the substrate’s embedded passive devices, such as decoupling capacitors, resistors, inductors, filters, and opto-electronic waveguides, to achieve a higher miniaturization by component integration. SoPs are envisioned at the nano scale[1.32], [1.33]. Although MCM, SiP, and SoP are different, these are commonly regrouped as SiPs.

The packaging substrate remains the most expensive component in advanced packaging becoming the cost limiting factor of the system, covering at least one-third of the total cost. System integration refers to its capabilities to interact with the outside world and the users, allowing for the non-digital functionalities (e.g., RF communication, power

Figure 1.5 Systems hardware integration for (a) SoC, (b) MCM, (c) SiP, and (d) SoP [1.32].

SoC

RFIC Opto-e Digital

RFIC _DRAM storage Flash SRAM Imaging Opto-electronics DSP Microprocessor Analog Antenna MCM Substrate IC Package Packaged IC IC stack SiP Substrate

RFIC Opto-e Digital

Substrate SiP

Decoupling capsR, L, C filters

Opto-waveguides SoP (a) (b) (c) (d)

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control, passive components, sensors, actuators) to migrate from the system board level into a particular package-level (SiP) or chip-level (SoC) implementation [1.34].

SoCs for consumer electronics need to offer realistic prices (<< $5), short time-to-market (< 6 months), low power consumption (< 50 mW active power), and high integration density (< 10 mm2_{). For low volume production, SiP solutions are mainly used}

[1.35]. 3D SoCs may be realized using non-conventional processes, such as thin film technology, to decrease interconnect delay and increase functionality [1.36]. In SoC, noise coupling due to digital and analog circuits, such as substrate ground line and supply, is addressed using techniques (e.g., substrate guard rings) to reduce crosstalk and apply noise isolation schemes [1.37].

In Fig. 1.5(c), SiP may be in the form of IC and package stacking using flip-chip connections, IC stacking using through substrate vias, or package IC stacking using wirebonds to connect to the substrate. All other devices, such as passive components, antennas, micro-electro-mechanical systems (MEMS), and optical components, are placed on the substrate. In 3D ICs, RF, analog, digital, and memory wafers are processed separately, then brought together in an integrated vertical stack [1.38], also named wafer-level packaging or wafer-scale 3D ICs using through silicon vias (TSVs) or through-wafer vias (TWVs) [1.39].

Electromagnetic interference (EMI) between different blocks is addressed by applying signal integrity (SI) techniques [1.40]. EMI is caused by signal-return path loop and switching current in the power/ground line, coupling paths or unwanted antennas in the substrate. SI is achieved with intensive electromagnetic simulations and near-field measurements to isolate and suppress the resonance of the DC power bus in a substrate and maintain the return current path.

Parasitic coupling between an integrated antenna and RF circuits is eliminated by applying shielding practices and proper placement within the package, such as changing the 3D package topology. On-chip antenna performance is hindered by the semiconductor substrate (e.g., silicon or GaAs) poor dielectric properties, such as a high dielectric constant and high losses in a conventional process (e.g., silicon resistivity of 1-10 Ω·cm). Moreover, using additional on-chip surface area for the antenna raises the overall IC cost, specifically for latest nanometer CMOS processes. Using small antennas results in a trade-off in antenna radiation efficiency (i.e., gain) for surface area. SoP allows degrees of freedom in substrate material choice and for placement. A SoC, however, is a system built essentially on a thin active layer on top of a semiconductor substrate, such that the antenna has a planar structure. Hence, substrate and surface coupling are introduced because of the semiconductor high dielectric constant and the potential build-up of substrate charges [1.41]. To suppress such noise coupling, signal integrity techniques such as metamaterial, electromagnetic bandgap (EBG), or frequency-selective surface (FSS) structures may be added within the substrate [1.42]-[1.44]. On-chip antennas are considered for medical applications, such as for intraocular pressure monitoring, where system size is an important factor traded for antenna performance degradation [1.25].

It is envisioned that integration of a multiplicity of technologies rather than simply silicon technology could realize new integrated systems. Bringing together microelectronics and photon-based sciences with that of nanochemistry and biotechnology will achieve intelligent systems-on-chip (iSoCs), enabling innovative products [1.45].

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7 Presently, for wireless sensor nodes, the most economical solution is likely to be a hybrid of integrated and assembled parts in a SiP [1.46]. Hence, this is the approach taken in this thesis.

1.2 Energy Harvesting for Wireless Communications

The anticipated market for wireless sensor networks (WSN) market demands a “deploy and forget” solution, requiring the elimination of a battery replacement maintenance cycle. Energy harvesting technology could lead to this possibility of self-sustaining “infinite” lifetime sensor nodes, or at least the prolongation of the time span between battery replacements. Micro-fuel cells are currently under investigation to replace batteries as they create electricity and heat from chemical energy with almost no loss [1.47].

The primary energy source for mobile and portable wireless systems is the battery. Batteries must be recharged regularly or disposed of and replaced by a new one. Considering the DC power consumed by an electronic system and ageing, batteries have a limited lifespan, ranging from minutes to years depending on the application. In some cases, replacing or recharging the battery is not feasible because someone must physically perform the task, hence adding a maintenance cost. For applications where the system lifetime is defined in years, energy harvesting (or scavenging) from the environment is desired as a supplement or replacement of the battery.

Energy harvesting is used in a wide range of applications, as listed in Section 1.1.2. enOcean is one of the first companies to apply energy harvesting for indoor building automation, such as temperature control of rooms, using photovoltaic cells for light energy, a mechanical switch or button pressure for motion energy, and thermal gradient converter element for thermal energy [1.31].

Energy harvesting technologies are given in Fig. 1.6 which include sun or artificial light, wind and airflow, heat or thermal gradient, radio-frequency electromagnetic radiation either from a generated source or from the environment, such as RF signals from a cellular base station, pressure, such as from a button switch, human generated power, such as from body heat, heart beat, motion, and blood glucose variation, vibration, mainly from transport vehicles (e.g., aircrafts, trains, trucks, and cars) or motors present on machinery, and plants which generate a potential difference in living tissue [1.48]-[1.55].

Figure 1.6 Energy harvesting technologies. Human power Vibration Light / sun Heat Pressure Wind Air flow Energy harvester RF radiation Plants

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8

A comparison of various energy harvesting power sources is given in Table 1.1, where the harvested power density is an approximate number as it depends on the source and the energy conversion efficiency of the element. The highest power is generated by solar cells in bright sunlight conditions. However, with artificial light sources, the power decreases by a factor of 1000, and only a few µW/cm2_{is generated, comparable with other sources of}

harvesting, such as vibration, thermal gradient, air flow, and pressure. Although indoor light energy has a relatively small power density, it is a significantly more mature technology. Moreover, indoor light energy is the most common energy source in most office and residential environments.

Solar, light, and vibration are energy harvesting elements commonly found in the environment. Fig. 1.7 compares their power densities with batteries. Leakage current becomes dominant for some battery chemistries for longer lifetimes. For lifetimes of 5 years or more, a battery cannot provide the same level of power that energy harvesting devices can provide [1.50]. However, energy harvesters may not be operational continuously. Sunlight is an intermittent source and thus an energy storage device with a high enough capacity must be used. This intermittency introduces design constraints on the smart system, as highlighted in Chapter 5 of this thesis.

Figure 1.7 Power density vs. lifetime for batteries, solar cell, and vibration based power [1.50].

Lifetime (years) P ower de nsit y (µ W/ cm 3 ) 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 1000 100 10 1 0 Office lighting Sun TABLE1.1

COMPARISON OF VARIOUS ENERGY HARVESTING POWER SOURCES [1.50],[1.51]. Power source Harvested Power

Solar (outdoor) (solar irradiance of 100 mW/cm2_{) 5-25}_mW/cm2

Light (indoor) (light source of 500 lx) 5-25 µW/cm2

Vibration 100-300 µW/cm3

Thermo-electric (ΔT=5o_{C) 40}_µW/cm2

Human power (motion) 300 µW/cm3

Air flow* 380 µW/cm3

Pressure variation* 17 µW/cm3

RF radiation 0.1-1 µW/cm3

Acoustic noise (75 dB, 100 dB) 0.003, 0.96 µW/cm3

*: Assumes an air velocity of 5 m/s and 5 percent conversion efficiency.

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1.3 Wireless Sensor Networks

A wireless sensor network (WSN) is composed of a large number of sensor nodes that are densely deployed in a random location. These low-cost ($1 target), low-power (µW average power range), multifunctional sensor nodes are small in size (cm3_{range) and}

communicate within short distances (≤ 100 m). Sensor nodes use their processing abilities to carry out simple computations locally and transmit only the required and partially processed data.

1.3.1 Wireless technologies for WSN

Table 1.2 summarizes the main wireless technologies and IEEE standards for WSN and wireless body-area networks (WBAN), using ZigBee [1.56], Bluetooth and Bluetooth Low Energy (BLE or Bluetooth 4.0) [1.57], wireless local-area networks (WLANs), and ultra-wideband (UWB) [1.58] standards. Some companies have developed their own proprietary wireless communication schemes, such as enOcean, which operates in the 868 MHz ISM European band and 315 MHz band worldwide [1.31]. ANT is a proprietary sensor network technology which features a lightweight protocol stack (i.e., compact transmission encoding to minimize memory resources), ultra-low power consumption (5 µA average current), and a data rate of 1 Mbps. It operates in the 2.4 GHz ISM band. The ANT+ interoperable system brings wireless connectivity to hundreds of available sport, fitness and health products [1.59].

Currently, the most widely used radio standard is IEEE 802.15.4 (Zigbee), for which the medium access control (MAC) layer functionalities are defined [1.61]. Zigbee can suffer from interference with WLAN transmissions. The ZigBee Alliance has been working on products for smart energy for home, building and industrial automation.

For connecting low power peripheral devices on the human body, e.g., watches, health-related monitors and sports sensors, BLE technology has the potential to be widely employed, due to its association with Bluetooth as well as lower cost and lower power consumption by using duty cycling to reduce the average power consumption to a few microWatts.

The IEEE 802.15.6 task group was formed in November 2007 to develop a standard for WBANs. The targeted data rate for WBANs is 10 Mbps [1.62]. Low data rate UWB is one of the potential candidates to overcome the bandwidth limitations of current narrowband systems and to improve the power consumption and size. The standard intends to use future generation electronics in close proximity to, or inside the human body. However, a time frame for product commercialization that incorporates this standard remains unknown.

TABLE1.2

WIRELESS TECHNOLOGIES USED FOR SENSOR NETWORKS [1.60]. ZigBee

IEEE 802.15.4 Bluetooth, BLE IEEE 802.15.1* IEEE 802.11b/g WLAN IEEE 802.15.6* UWB

Frequency band 868/915 MHz, 2.4 GHz 2.4 GHz 2.4 GHz 3.1-10.6 GHz

Bandwidth/channel 5 MHz 1 MHz 20 MHz 500 MHz-7.5 GHz

Data rate 20, 40, 250 kbps < 3 Mbps* _{1 Mbps} ≥ 11 Mbps _{850 kbps-20 Mbps*}< 1 Mbps (LDR)

Transmit power 0 dBm 4, 20 dBm* 24 dBm -41.3 dBm/MHz

Max. Range 75 m, 10 m 10, 100m*, 50 m 100 m 2 m*

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According to the Federal Communications Commission (FCC), UWB refers to any radio technology having a transmission bandwidth exceeding 500 MHz or 20% of the arithmetic center frequency. The FCC also permits license-free use of the 3.1-10.6 GHz band for UWB communication at an equivalent isotropically radiated power (EIRP) limit of -41.3 dBm/MHz [1.58]. In Europe, UWB is restricted to the 6-8.5 GHz frequency band [1.63]. This leads to the suitability of UWB applications in short-range and indoor environments, and in environments sensitive to RF emissions due to large frequency spreading of the signal [1.64]. UWB does not interfere with other wireless medical devices when used in a medical environment due its low-power transmission. Moreover, UWB technology offers the capability of precise localization within the cm range [1.65].

The IEEE 802.15.4a Task Group main interest is to provide data communications, high precision ranging and location capability (≤ 1 m), throughput in the range of Mbps, and power consumption of a few mWs [1.66]. UWB is a strong candidate for a potential alternative physical layer to the 802.15.4 standard for low-power, low-data-rate wireless networks. The UWB physical layer (PHY) is specified to operate at frequencies from 3.1 to 5 GHz, 6 to 10 GHz, and below 960 MHz with mandatory data rate of 851 kbps and optional data rates of 110 kbps, 6.81 Mbps, and 27.24 Mbps [1.67]. An amendment of this standard drafts an alternate PHY specification based on impulse radio (IR-UWB) signaling, as described in the next section.

1.3.2 UWB signaling

There are two main classes of UWB signals. Impulse-radio UWB (IR-UWB) uses short, low-duty cycle, baseband generated electrical impulses which are carrierless since it does not use any carrier to up-convert the signal towards radio-frequencies (GHz range). For transmitting, the difficulty lies in the control of the exact shape of the impulse and its center frequency and in synchronizing at the receiver. Frequency-modulation UWB (FM-UWB) possesses better spectral accuracy, which makes the compliance of the generated signal to regulations much easier.

An IR-UWB using the time-domain approach [1.68] relies upon a sequence of short-duration pulses as the information carrier. The spectrum of such a pulse sequence (usually Gaussian) has a single broad main lobe with slow spectral roll-off. An impulse radio may be able to provide communication at a high data rate (> 100 Mbps), but this comes at the expense of circuit complexity and power consumption [1.69]. A 5 mW wireless flight control system for cyborg moths is reported in [1.70], using IR-UWB in the 3-5 GHz band and operating at a data rate of 16 Mbps. This receiver SoC comprises a small battery, a flexible PCB of 4 cm2_{, 3D die-stacking, and an off-chip antenna with a total weight of 1 g.}

In this work, FM-UWB is chosen. It is a constant-envelope frequency-domain approach, suitable for low and medium data rates (LDR and MDR < 10 Mbps) [1.71]. The advantages of FM-UWB technology from an implementation and performance point of view are a simple hardware implementation, relaxed hardware specs, no receiver local oscillator nor carrier synchronization, robustness to interference and multipath, and steep spectral roll-off. These are discussed in detail in Chapter 2.

FM-UWB can be seen as an analog implementation of a spread-spectrum system with a spreading gain equal to the modulation index β. FM has the unique property that the RF bandwidth BRFis not only related to the bandwidth fmof the modulating signal, but also to

the modulation index β, as approximated by Carson’s rule [1.72]:

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11 where Δf is the frequency deviation defined by β = Δf/ fm.

This yields either a bandwidth-efficient narrowband FM signal (β < 1) or a (ultra-) wideband signal (β >> 1) that can occupy any required bandwidth compatible with the RF oscillator’s tuning range, where no carrier can be distinguished. Hence, the bandwidth of a wideband FM signal can be controlled by adapting β. By applying a triangular wave shape for the subcarrier modulated signal, the spectral roll-off of this FM-UWB signal is very steep. This strongly improves the coexistence of FM-UWB systems with other RF systems operating in adjacent frequency bands.

To visualize the time domain FM-UWB signal, Fig. 1.8(a) shows an example of a 20 MHz signal generated with a β of 10 at a data rate of 250 kbps and frequency shift-keying (FSK) subcarrier of 1 MHz with 250 kHz deviation. In Fig. 1.8(b), the spectral density of a FM-UWB signal centered at 4 GHz is plotted with a frequency deviation Δf of 800 MHz and subcarrier frequency of 10 MHz, yielding a β of 80. The flat spectrum is a result of the triangular subcarrier waveform.

Fig. 1.9 shows the maximum EIRP allowed by regulatory bodies, such as the FCC for the US, the European Commission (EC) for Europe, and the Ministry of Internal Affairs and Communications (MIC) for Japan. Other countries also have their own regulations. This work addresses the lower UWB band (3.1 to 5.1 GHz) and the upper band (8.5 to 10.5 GHz) which are used in the US, Japan, and other countries.

Figure 1.9 UWB EIRP regulations defined by the FCC, the EC, Japan, and frequency allocations used in this work. -30 -40 -50 -60 -70 -80 -90 1 2 3 4 5 6 7 8 9 10 11 12 0. 96 1.6 1 1.9 9 3. 1 6 8.5 10.6 2. 7 3. 8 1. 6 GP S IS M 2. 4G H z Wi M A X WiLA N 3. 4 3. 4 4. 8 _7.2 5 _10.2 5 11 .7 Frequency (GHz) M ax . m e an E IR P ( d Bm /M Hz ) outdoor FCC indoor_EC MIC This work

Figure 1.8 (a) Time domain signals of data, FSK subcarrier, and UWB, and (b) Spectral density of FM-UWB signal centered at 4 GHz. 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 t (µs) S igna l amp lit ud e FSK Data FM-UWB Sp ec tr um d ens ity (d Bm /M Hz ) 0 4 3 5 Frequency (GHz) -10 -20 -30 -40 -50 -60 -70 -80 -90 -100 (b) (a) -41.3

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1.4 Scope of PhD Research

1.4.1 Motivation

The goal of this work is to demonstrate autonomous wireless sensor nodes powered solely on light energy harvested either outdoors or indoors. By using FM-UWB, a simple transmitter architecture is adopted, thus achieving low-power consumption. To save system surface area in the packaging design and cost, the photovoltaic cell is used as the transmit antenna as well as providing energy to either charge a supercapacitor or a rechargeable battery. The integration approach is based on SiP using commercial-off-the-shelf (COTS) components.

The transmitter system block diagram is shown in Fig. 1.10. It is envisioned as an embedded system that includes: a photovoltaic antenna, a FM-UWB radio transmitter, digital signal processor (DSP) such as a microcontroller or FPGA, power management, energy storage device (e.g., a supercapacitor or a rechargeable battery), and optional sensor(s) integrated as a SiP. The occupied volume shall be under 10 cm3_.

Photovoltaic (or solar cell) antennas have been used for satellite communications [1.73]. However, it is the first time that such a wireless sensor node system is reported.

1.4.2 Project challenges

Challenges for these smart wireless systems are to be addressed in this research project, such as: 1) achieving UWB circuit functionality in the GHz range and low-power consumption (µW) simultaneously, 2) targeting a lower system cost ($5 target), 3) obtaining a compact system form-factor by saving surface and volume, 4) enhancing the performance of the antenna radiation pattern while in the presence of other components which also contribute to radiation effects, 5) proposing an adaptable design of a photovoltaic antenna to any application, and 6) extending the autonomy lifetime of the system.

1.4.3 Thesis outline

This thesis work is summarized in the chart of Fig. 1.11, which includes references to the chapters of this thesis where the work was completed and is being presented. As a system designer, once a wireless sensor node application is submitted for a smart system design, the tasks must follow five steps, defined as: 1) system definition, 2) system overview and block definitions, 3) circuit design and verification, 4) system integration, and 5) system verification and deployment.

Figure 1.10 Autonomous wireless system block diagram.

Sensor Digital signal _processor _transmitterRadio Photovoltaic _antenna

Power management

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13 The first step relates to the system definition and requirements. The application determines the type of functions it must perform via specific sensors used for monitoring (i.e., temperature, humidity, motion, glucose, etc.). The overall size and style or shape is also taken into account. A sensor node only needs to send packets at a periodic interval (e.g., every second, minute, or hour), which determines the duty cycle (i.e., ratio of the transmitter on time to the sum of transmitter on and off times). Thus, the system remains in a low-energy mode which minimizes the average energy demanded either from the energy harvester or the storage device. This determines the overall lifetime of the node. Reliability determines the node’s chance to communicate without failures. Finally, cost must be considered in the system hardware design which includes both electronic circuits and packaging, as discussed in Chapter 2. In a smart system, both hardware and software must be optimized to comply with the system’s specifications.

The second step, described in Chapter 2, determines the system architecture and technology choices in order to implement the circuits and individual devices. Moreover, a primary power source must be chosen. In this work, light energy harvesting is used.

The third step involves circuit design of all blocks. The radio transmitter or radio-frequency integrated circuit (RFIC) is described in Chapter 3. The photovoltaic (PV) antenna is presented in Chapter 4 in terms of PV cell DC and antenna RF performance. A design guideline is also provided. The power management module is described in

Chapter 5, which consists of the power supply management together with energy storage devices. Other digital and mixed-signal circuits, such as a microcontroller interfacing with a

Fig. 1.11 Smart systems integration design flow for a wireless sensor node, tasks and reference to thesis chapters. System architecture

Ch. 2

Smart systems integration

Software programming Ch. 6 Packaging/ Assembly Ch. 6 RFIC front-end Ch. 3 Antenna Ch. 4 Power supply Ch. 5 Integration techniques Ch. 6 Digital Ch. 6

Autonomous Wireless System Application Ch. 1 Cost System link Field behavior Ch. 6 Sensors (functions) Size Style Primary power source Ch. 2, 4 Duty cycle Reliability Technology & Process Ch. 2 Lifetime Energy storage Ch. 5 Mixed signal Ch. 6 Energy/bit Lifetime Ch. 6 Steps:

1

System definition

2

System overview Block definition

3

Circuit design & verification

4

System integration

5

System verification & deployment

Define

_Apply

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14

sensor and subcarrier modulator for generating the FM-UWB signal, are presented in

Chapter 6.

The fourth step relies on system integration, both at the hardware and software level. The complete packaging and assembly of the systems are illustrated in Chapter 6.

The fifth step demonstrates the feasibility of such wireless smart systems and verifies their performance, as shown in Chapter 6.

This thesis provides the concept basis of a light energy harvesting wireless sensor node using a photovoltaic antenna, benchmarking the technology by demonstrating its feasibility in outdoor and indoor applications. Other potential applications for a broad class of wireless devices, as highlighted in Section 1.1.2, may be extrapolated based on this concept.

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Chapter 2 Low Data Rate FM-UWB for

Autonomous Systems

This chapter presents system design approaches suitable for autonomous low data rate FM-UWB communications. An overview of the complete transceiver design is described, establishing the different blocks, target power consumptions, and defining the energy consumed per bit of data. From an RF view point, a general link budget is presented. The influence of cost on the system design is discussed. Hardware and software integration approaches are proposed for light energy harvesting wireless sensor nodes developed in this work. Finally, system limitations due to available chipsets in the market are highlighted. Although this work uses off-the-shelf components for complete system integration, it lays the foundation for an integrated autonomous transmitter sensor node concept.

2.1 FM-UWB Transceiver System

FM-UWB transmitter and receiver system block diagrams are illustrated in Figs. 2.1(a)

and (b), respectively, based on a sensor as a data source. A conventional constant-envelope FM-UWB transmitter uses binary frequency-shift keying (BFSK) followed by high modulation index analog FM to create a constant envelope UWB signal. A subcarrier modulator translates the binary data with a rate of 1-100 kbps onto a BFSK triangular waveform in the kHz or MHz range, which is used to modulate an RF oscillator and generate a wideband FM signal of at least 500 MHz. Triangle wave modulation ensures a flat spectrum with a steep roll-off [2.1]. A frequency calibration feedback loop is desired to correct variations in process, voltage, and temperature (PVT) and set the frequency band. For example, an all-digital frequency-locked loop (FLL) is proposed in [2.2] to calibrate the carrier and subcarrier frequencies. The oscillator is followed by a buffer amplifier to isolate it from potential interferers received at the antenna. The buffer helps to maximize power transfer to the antenna by effectively performing an active impedance transformation. To power all active electronic circuits, an energy source and a power management module is included in the system. The sensor(s) may be active or passive. The antenna transmits the FM-UWB signal whose spectral mask must conform to regulations, as defined by the FCC (US) [2.3], the European Commission [2.4], and other countries.

The low radiated power of an UWB transmitter (-41.3 dBm/MHz power spectral density or 37 µW in a 500 MHz bandwidth [2.3]) opens the door to low DC power consumption. The transmitter front-end (in its simplest form) may be implemented using just a wideband voltage-controlled oscillator without a separate power amplifier, thereby reducing power consumption even further, leading the way for continuous operation using only harvested energy.

Smart Systems Integration for Autonomous Wireless Communications

Mina Danesh

Mina Danesh