lecture 2 - lunds tekniska högskola · ibm research - brazil de vellidis et al. computers &...

Lecture 2

INTERNET OF THINGS AND LOW-POWER DEVICES

Mattias Borg / More than Moore – Future of Electronics 1

LUND UNIVERSITY

Outline

Low-power devices


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What is IoT?

Internet of things

The interconnection via the Internet of computing

devices embedded in everyday objects, enabling

them to send and receive data.

Typical Google search 5 years from now?


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• 1982 – Coke machine connected to internet

• Term ”Internet of Things” was made up in 1999

• Taking off in the last few years

History

1982

2017 version”Nbr of drinks are low”

”I am cold”

Twitter, Facebook,

Calendar, Direct food orders

etc, etc...


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Current architecture of IoT

• ”Wireless Sensor Networks” that communicate to Internet through gateways

– No inter-standard communication

• Low-power nodes Protocols are made for low-power consumption

• Popular protocols:

– ZigBee, Z-wave, Weave (Google) – 850MHz, 950MHz, 2.4GHz

– Insteon (open) – P2P, uses the power line

– EnOcean (Siemens spin-off) – energy harvesting!

• Mesh network topography to extend range/limit power

• Security:

– ZigBee, Z-wave, EnOcean uses AES-128 encryption

– Simpler protocols (433 MHz) have no encryption!

EnOcean solar energy harvester


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Future architecture of IoT?

• > 50 Billion devices by 2020

• All things connected by TCP/IP?

– Enables: Web services, inter-system compatibility

– IPv4 (32 bit) is not enough

– IPv6 (128 bit) certainly enough!

• IPv6 not ideal for low power devices

– Simpler adaption layer for low power (6LoWPAN)

• A unified new IoT architecture has not been decided on yet

Challenges:

• Compatibility across applications with diverse needs/requirements

(industry, military, transport, consumer)

IEEE 802.15.4

(Phys/MAC)

6LoWPAN

IPv6

Application layer


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IoT in use – Building Automation

• Automatically/remotely control lights, doors, locks, alarms,

temperature, window shades, ventilation, etc.

– 40% of energy in Europe building-related

• Mass market adoption hindered by

– Fragmented eco-system

– High unit prizes (~500 SEK)

• Most common solutions based on Z-wave, Zigbee or 433 MHz.


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IoT in use – Smarter Agriculture

Temperature, Moisture,

Light intensity, Soil nutrients...

Sensors Power

Unit

Processing

UnitWireless

CommLocal data analysis

Decision to send data

Battery, energy harvesting

Solar energy

Lower power

transceiver

By collecting real-time data on weather, soil and air quality, crop

maturity and even equipment and labor costs and availability,

predictive analytics can be used to make smarter decisions.

IBM Research - Brazil

Sensor

node

Vellidis et al. Computers & Electronics in Agriculture 2008


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IoT in use - Industry 4.0

• Industrial production systems with

machines/sensors/devices that can communicate and

take decisions in a decentralized manner

• Open sensor networks create a virtual image of the

system, and allows for machine-2-machine

communication + augmented reality.

• Examples of use:

– Machines which can predict failures and trigger

maintenance processes autonomously

– Self-organized logistics which react to

unexpected changes in production

• Networks may not be limited to single factory but

could be multi-factory or even world-wide!

• Firmly data- and analytics-based benefits

– But data and control comes from IoT


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IoT in use - Automotive

• Vehicle-2-X era (now)

– A wide range of spread-out devices communicate

and share data and with vehicle

– Example:

• Home weather station detects cold weather

• smart home system detects signs of person

leaving home

Car preheats and prepares for

arrival of driver

• Mobility era (2020 onwards)

• Self-driving cars sensors!, AI

• Car makers need adapt

- Car sharing instead of owning

- Hardware less important than software

• Future transportation model: Self-driving Uber?


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IoT in use - Retail

• Detailed customer

flow analytics

• Optimize inventory

• Track customer

interest/history

• Personalized

discounts

• ...

• Scary & off-putting?

Video at: https://www.youtube.com/watch?v=iAvUscjkxqI

https://www.youtube.com/watch?v=iAvUscjkxqI


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Fog computing

• Moving data is expensive

– Prize of comm. is scaling slower than storage and computation

– IoT: Exabytes/day of new data (most useless)

• Initial data analysis should be done where the data is saves traffic

• More services hosted in the Fog: routers, switches and end devices

– A hierarcial data down-selection network feeding into the cloud

– Open-source standard beneficial

CLOUD

SERVER

”Large scale

analytics”

device

FOG

NODE

device

device

Sensor data

Sensor data

Sensor data

Low level

analytics Medium level

analytics


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Security issues with IoT

• Data and analytics is distributed on open/shared networks

• How to prevent data theft?

• Connected things:

– How to protect privacy

– How to prevent hacking and malicious overtaking of devices

• Encryption is often too power consuming.

• Problem not yet solved...


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Requirements for IoT

• Low-power consumption is key

• Processing power/speed may be less important.

• Low-power communication

– Local analytics transmit only relevant information

– Simplified protocols 6LoWPAN

– Energy-efficient tranceivers

• Low-power sensing

– Integrated sensors (Lecture 5)

• Low-power logic devices

– Subthreshold logic

– Steep-slope devices


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Subthreshold logic

• Operating conventional CMOS in subthreshold saves power at expense of switching speed

• >1000x energy saving

• Typically limited to kHz-MHz processing speed

• Subthreshold current is very sensitive to variations:

– Temperature, bias, and VT variation


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Steep-slope devices

• Subthreshold swing below 60 mV/dec

• Enables operatíon voltage scaling beyond

that of the MOSFET

Examples:

• Tunnel-FET

• Nanomechanical switches

• Piezoelectrical transistor

• Negative-capacitance FETs


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Tunnel FET

• Utilizes interband tunneling mechanism to filter out ”hot” carriers

Enables sub-60 mV/dec operation

• Lower on-currents than MOSFETs

• III-V heterostructure designs may optimize current vs slope.

Filtering of the

Fermi function

source channel

EC

Memisevic et al. IEDM 2016GaAsSb/InAs TFET

10 µA/µm. SS = 48 mV/dec


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Nanoscale Electro-Mechanical Switches

• Mechanical relay switch near ideal switching

• Challenges:

– Operating voltage (often > 1 V)

– Footprint (> 1 µm2)

– Switching speed

– Reliability/lifetime

• Minimum switching energy set by adhesion force

Chen et al. EDL 2015

Ayala et al. ESSDERC 2016


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Piezoelectronic transistor

• A gate-controlled piezoelectric element

stresses a piezoresistive element

(SmSe) to control its resistance

• Switching speeds limited by speed of

sound.

– With optimized geometry high

switching speeds are attained

- Needs rigid frame around!

- Still no experimental proof-of-concept...


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Negative capacitance FETs

• Negative capacitance: A way to achieve steeper slope is to amplify gate voltage

• VGS = A*V’GS compresses transfer characteristics

• Voltage amplification by negative capacitance

• Benefit: Only a single alteration to standard device design

V’GS

log(IGS)

VGS

VGS

V’GS

CMOS

CNEG 𝑉𝐺𝑆′ =

𝐶𝑁𝐸𝐺𝐶𝑁𝐸𝐺 − 𝐶𝑀𝑂𝑆

𝑉𝐺𝑆

Amplification

If 𝐶𝑁𝐸𝐺 < 0,

VGS

CMOS

Standard MOS stack Modified MOS stack

Sounds nice, but really...how do you achieve a negative capacitance?


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• An external electric field aligns dipole moments

• Uniform polarisation excess charges only on surface

• This effect is used in gate dielectrics to transfer potential from gate channel

Polarisation in dielectric materials

Polarisation

”Total dipole moment density across a body”

In effect: the density of displaced charges (C/cm3)

The dielectric susceptibility 𝜒 is the measure of the "susceptibility of a material to be polarized by an external electric field". i.e. 𝑃 = 𝜖0𝜒𝐸 (ϵ𝑟 = 1 + 𝜒)

The internal depolarization field counteracts the polarization, thus

effectively reducing it.

+ + + +

-----

+P

-P

Eext

Eint

Eext

-+ -+

-+-+-+-+-+

-+

-+-+

-+


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Ferro-electricity

• Some materials can have a remanent polarization

• This originates in a bistable crystalline equilibrium state

• Common in perovskites Ex: Pb[ZrxTi1-x]O3 (PZT)

Eext

-+ -+

-+-+-+-+-+

-+

-+-+

-+

PZT unit cell


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FE negative Capacitance?

• Landau-Khalatnikov equation 𝜌𝑑𝑃

𝑑𝑡+

𝑑𝐺

𝑑𝑃= 0

𝐸𝑒𝑥𝑡 = 2𝛼𝑃 + 4𝛽𝑃3 + 6𝛾𝑃5 + 𝜌𝑑𝑃

𝑑𝑡

𝐶 =𝑑𝑄

𝑑𝑉= 𝐴𝐹𝐸𝑡𝐹𝐸

𝑑𝑃

𝑑𝑉

C < 0

BaTiO3


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Measurement of P-E diagrams

• Simplest way: Sawyer-Tower circuit

• Known positive capacitor in series with FE capacitor

• Stimulus voltage x axis

• Voltage across sensing capacitor Polarisation of FE

Y-axis

X-axis


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Stability of a NC-FET

– 𝐶𝑁𝐸𝐺 − 𝐶𝑀𝑂𝑆 > 0, otherwise the device will be unstable

– 𝐶𝑁𝐸𝐺 close to CMOS for strong amplification

𝑉𝐺𝑆′ =

𝐶𝑁𝐸𝐺𝐶𝑁𝐸𝐺 − 𝐶𝑀𝑂𝑆

𝑉𝐺𝑆

Amplification

If 𝐶𝑁𝐸𝐺 < 0,

Stable branch

VGS

V’GS

CMOS

CNEG

𝐶𝑡𝑜𝑡 =𝐶𝑁𝐸𝐺 𝐶𝑀𝑂𝑆𝐶𝑁𝐸𝐺 − 𝐶𝑀𝑂𝑆


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Achieving sub-60 mV/dec operation

• 𝐶𝑁𝐸𝐺 > 𝐶𝑀𝑂𝑆 for stability

𝑆𝑆 = 60𝑚𝑉

𝑑𝑒𝑐× 1 +

𝐶𝑑𝑒𝑝

𝐶𝑜𝑥−

𝐶𝑑𝑒𝑝

|𝐶𝑁𝐸𝐺|

𝐶𝑑𝑒𝑝

𝐶𝑜𝑥−

𝐶𝑑𝑒𝑝

|𝐶𝑁𝐸𝐺|< 0 for sub-60 mV/dec slope

𝐶𝑁𝐸𝐺 < 𝐶𝑜𝑥

𝐶𝑀𝑂𝑆 < 𝐶𝑁𝐸𝐺 < 𝐶𝑜𝑥


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Optimizing magnitude of CNEG

• 𝐶𝑀𝑂𝑆 < 𝐶𝑁𝐸𝐺 < 𝐶𝑜𝑥• 𝐶𝑁𝐸𝐺 as close as possible to CMOS for maximum amplification

• Difficult to make |CNEG| close to CMOS in subthreshold

– Channel depletion gives drop in CMOS


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Ways to control Cdep

• Ultra-thin body

– Pins depletion length to body thickness and gives reduced dip in CMOS

High voltage amplification throughout bias range


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Perovskite/organic FE-NCFETs

• Demonstrated devices have too low CFE 𝐶𝐹𝐸 < 𝐶𝑀𝑂𝑆 hysteresis

• Many materials lose the ferroelectricity at low thickness

– Perovskites (PZT and BaTiO3) minimum 6 unit cells, 2.4 nm

Dasgupta et al. IEEE J Expl. Solid State Comp. Dev. Circuits 2015

Rusu et al. IEDM 2010


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Ferroelectricity in HfO2

• Typically observed in doped HfO2 films

– Si, Al, Y...

– And in alloy with Zr (HfZrO2)

• Discovered after thermal annealing of HfO2

films above 400 °C.

• Crystallization into ferroelectric phase

• Remanent polarization ~ 15-20 µC/cm2

– Similar to perovskites

– Theoretically ~ 50 µC/cm2

– Possible with ~ 1 nm thickness

Müller et al. Nano Letters 2012


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Origin of the ferroelectricity in HZO

• Relative stability of various phases of HZO is

important

– (tetragonal, monoclinic, cubic, and ferroelectric

phase)

• Usually the m-phase most stable

• Only ”grain size” can explain ferroelectric stability

– 8-16 nm size optimal for HZO

– Doping can affect grain size ferroelectricity


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Experimental NCFETs without hysteresis

• Very thin HZO has given low SS + stability

• Higher annealing temperature stability

Lee et al (NTU) IEDM 2016

Li et al (Berkeley) IEDM 2015


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Speed limitation of FEFETs

• Limit to switching speed due to ”viscosity” of the ferroelectric transition between states

𝜏𝑚𝑖𝑛=ρtFE

2

𝐶𝑀𝑂𝑆

𝐴

viscosity

5 MHz 500 MHz

Limit for PZT: 200 ps 2.5 GHz...

For HfO2?

Yuan et al. IEEE TED 2016

• Not ideal, but fast

enough for IoT

• May be tunable...


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Summary

• Internet of Things

– Billions of connected devices

– Wide range of application areas

– A drastic change of the structure of the internet needed

• Distributed computation (fog)

– Security concerns

• Low-power devices to run the IoT

– Subthreshold logic

– Steep-slope devices

• TFETs

• Piezo-FETs

• NEMS

– Negative capacitance FETs

• Minimal design change

• demonstrated sub-60 SS

• Questionable high-frequency operation

lecture 2 - lunds tekniska högskola · ibm research - brazil de vellidis et al. computers &...

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