Nanoelektronik Technische Fakultät Christian-Albrechts ... · PDF fileNanoelektronik...

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Hermann Kohlstedt

NanoelektronikTechnische Fakultät

Christian-Albrechts-Universität zu Kiel

Memristive Devices in Analog Neuromorphic Circuits

NanoNetwork Workshop_Bergen_June 2013

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V = 1000 cm3

ab

c

Achip 1 cm2

N 2 104 chips

Ptotal 106 W 1 MW (!)

Ntransistors 2 104 1010 2 1014

PMOSFET 5 nW

A Brain replaced by Computer Chips

approx. number of synapses

Pbrain 25 W

Psynapse 250 fW

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Contents

• Introduction

• Neurobiology – A few Milestones

• Neuromorphic Electronics

• Two examples: Pavlov`s Dog and an Amoeba

• A memresistive Flash cell

• Summary

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Computer - Brain

Computer: Arithmetic operation

Brain: Pattern Recognition / Associative Memory

2376492 = 1541,5875

Vacation:

Computing Gap

Introduction

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DendriteSynapse

AxonSoma

Neurons for information processing

Analog VLSI and Neural SystemsCarver Mead, Addison‐Wesley 1989, p. 44

M. Mahowald, R.Rodney Douglas, A silicon neuron, Nature 1991

Bible of analog VLSI for Neural Circuits: A survey of Bio‐Inspired and other alternative ArchitecturesD. Hammertrom in:Nanotechnology, Vol. 4, Ch. 10, p. 252Wiley, 2008, ed. by R. Waser

Data spikes

Introduction

Review: G. Indiveri et al. Neuromorphic silicon neuron circuits,frontiers in Neuroscience 5, article 73 (2011).

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Pulse duration:3, 5 ms (in electronics: 60 ns)

Signal speed ‐ along the axon:100 m/s(in electroncis 2.4 x 108 m/s)

Spikes – the information unitsIntroduction

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Neurobiology – A few Milestones

In Search of Memory, Eric R. Kandel, W. W. Norton & Company, New York 2006.S. R. Cajal , La fine structure des Centres Nerveux, Proc. R. Soc. London (B) 1894 , 55 , 444

Santiago Ramón y Cajal

In other words: He suggest already that something like a synaptic cleft must exist! (in 1890!!)

Cajal: Learning means, that the synaptic interconnection are not fixed. They adjust in correspondence to the input signals from the environment.

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Donald O. Hebb.

“The memory in brains is distributed overthe whole “system” but certain regions store different aspects!”

Neurobiology – A few Milestones

Hebbs learning rule: When an Axon of cell A excites cell B and repeatedly or persistently takes part in it's firing,some growth process of metabolic changes take place in one or both cells. Thus, that's efficiency is increased!

"Cells that fire together, wire together."

D. O. Hebb , The Organization of Behavior , John Wiley , New York 1949 .

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Neurobiology – A few Milestones

T. V. P. Bliss and T. LØmo, Long‐Lasting Potentiation of Synaptic Transmission in the Dentate Area of the anaesthetized Rabbit Following Stimulation of the Perforant Path, J. Physiol. 232, 331 (1973).

From Molecules to Networks, Ed. John H. Byran and James L. Roberts, Academic Press 2009:J. H. Byren et al., Learning and Memory Basic Mechanisms: , Chap 19 p. 541

Hippocampal Brain Slice

Long Term Depression (LTD)Long Term Potentiation (LTP)

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What means learning in biological Systems?

Three Levels

1BehaviorPsychology

Implicit learningExplicit learning

2Networks

Architecture

3Nerve CellsBiochemistry

Automatic in quality:habituation, sensitization,classical conditioning

Conscious or declarative:Recall people, places, facts, and events etc.

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In Search of MemoryEric R. KandelW. W. Norton Company 2006

A reductionistic Principle

Aplysia California: a Snail

To bridge the Gap between Behavior and Cell Biology

E. R. Kandel, Science 294, 1030 (2001).

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In Search of Memory, Eric R. Kandel, W. W. Norton & Company, New York 2006.

A reductionistic Principle

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L. O. Chua,Memristor – the missing circuit element, IEEE Trans. Circuit Theory 18, 507 (1971).See also: materials today Dec. 2011 Memory matters and MRS Bulletin, Resistive switching phenomena in thin films, Feb. 2012

Leon Chua`s Memristor

I

V

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Which memristive Device should I use?

NanoionicsR. Waser , R. Dittmann , G. Staikov , K. SzotAdv. Mater. 2009

Ferroelectric Tunnel JunctionsAndrè Chanthbouala, et al.Nature Nanotechnology 2012

You have the choice – a few Examples

Ionics and Tunnel BarriersD. S. Jeong et al. Solid‐State Electronics 63, 1 (2011)

Ti‐Oxide

Spin Transfer Torque Devices

MgO

P. Krzysteczko et al., Adv. Mater. 2012

NanoinonicsD. B. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams, Nature 2008, 453, 80.

Reviews: Doo Seok Jeong et al. Rep. Prog. Phys. 75 (2012)S. D. Ha and S. Ramanathan, JAP 110 (2011)

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Memristive Devices for Neuromorphic Systems

Sung Hyun Jo et al., Nano Lett. 10, 1297‐1301 (2010).

• synaptic plasticity: spike timing dependent plasticity (STDP)

Memristive devices as artifical synapses

T. Ohno et al., Nature Materials 10, 591–595 (2011).

• precondition of learning: long term potentiation

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Image No. 0030628Image No. 0030628Credit:Credit:The Granger Collection, The Granger Collection, NYC NYC —— All rights reserved.All rights reserved.

Pavlov`s Dog: Classical Conditioning

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• Experiment to understand implicit learning in biological systems.

before conditioning

after conditioning

IVAN PETROVICH PAVLOV (1905)

Experimental Psychology and Psychopathology in Animals, Vol. 1 p. 47‐60, Ivan P. Pavlov, Lectures on Conditioned Reflexes, International Pub., New York 1928

Pavlov`s Dog: Classical ConditioningAssociative Learning

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• Voltage dividercomprizing a memristive device

• Electrical circuit layout: single memristive device implemented in an analogue circuitry

Adder Comparator

+

-

+

-

Reference set-point: Threshold Vcth

RM VM

OP1

OP2R1

Alertness Vout

Unconditional stimulus (UCS)

Conditional stimulus (CS)

Neural mediating circuit for associative learning

Vcth

Vmth

M. Ziegler, et al., Advanced Functional Materials, 22, 2744 (2012)/ExperimentalO. Bichler et al. Neural Computation 25, 549 (2013)/ExperimentalY. V. Pershin and M. Di Ventra, Neural Networks 23 (2010)/ Emulator

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• circuit with threshold voltages for the comparator and mem device

Vbell < Vcth & Vfood> Vcth

Vbell + Vfood > Vpmth (before conditioning) & Vbell > Vcth (after conditioning)

Implicit learning

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• Pt/SiO2/Ge0.3Se0.7/Cu memristive device in voltage divider

• synaptic potentiation via transition LRS to HRS

• effective threshold voltage of the device

Device requirements

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

-0.5

0.0

0.5C

urre

nt (m

A)

Voltage (V)

0.47 kΩ

Vnmth

= -0.18V

Vpmth

= 0.33V1

2

3

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R. Soni et al., J. Appl Phys. 110, 054509 (2011).

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Learning behavior in biological systems

Unicellular organism, able to solve mazes

Interesting candidate to study basic cognitive functions

T. Ueda, Hokkaido University

Amoeba: Physarum polycephalum

• Anticipation to enviormental changes for periodic repetition

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Amoeba Anticipate Periodic EventsT. Saigusa, A. Tero, T. Nakagaki, Y. Kuramoto, Phys. Rev. Lett, 100, 018101 (2008)

Temperature

favorable

unfavorable

Humidity

Biological experiment:

anticipated events

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R = 1 Ω L = 2 H C = 1 F

resonant circuit

R L

C

A memristive circuit model to mimic an amoebaY. V. Pershin, S. La Fontaine and M. Di Ventra, Phys. Rev. E 80, 021926, 2009.

Simulation: Electronic Emulatormemristive device

Problems for experimental implementation:

Circuit parameter

Using a real memristive device

24Y. V. Pershin and M. Di Ventra, Adv. Phys. 60 (2011) and references therein

Periodic input pattern needed for learning

Amoebae anticipation

Simulation

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Electronic circuit

R = 100 Ω L = 100mH

C = 50 nF

R = 10 kΩ

memristive device

I Output current

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High off‐resistance for an ideal LC circuit

Change to on‐state requires a threshold voltage

High Reset voltage in respect to set voltage

Requirements for the memristive device

TiO2‐x

Al

Ag

1.

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Experimental implementation:M. Ziegler, et al. An electronic implementation of amoeba anticipationApplied Physics A (2013)

anticipated events

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Amoeba anticipation Periodic EventsT. Saigusa, A. Tero, T. Nakagaki, Y. Kuramoto, Phys. Rev. Lett, 100, 018101 (2008)

Better anticipation to environmental changes for periodic repetition

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Response (µA

)

Amoeba anticipation

Non-periodicpattern

Periodicpattern at resonancefrequency

Vin (V)

Vin (V)

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Which memristive Device should I use?

NanoionicsR. Waser , R. Dittmann , G. Staikov , K. SzotAdv. Mater. 2009

Ferroelectric Tunnel JunctionsAndrè Chanthbouala, et al.Nature Nanotechnology 2012

You have the choice – a few Examples

D. S. Jeong et al. Solid‐State Electronics 63, 1 (2011)

Ti‐Oxide

Spin Transfer Torque Devices

MgO

P. Krzysteczko et al., Adv. Mater. 2012

NanoinonicsD. B. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams, Nature 2008, 453, 80.

Reviews: Doo Seok Jeong et al. Rep. Prog. Phys. 75 (2012)S. D. Ha and S. Ramanathan, JAP 110 (2011)

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Floating Gate Transistor as Memristive Device?

• Memristive operation mode of a single EEPROM cell

• Reduction to a two‐terminal device: simultaneous read/write

H. C. Card and W.R. Moore, Electronic Letters 25, 805 (1989).C. Diorio, P. Hasler, B.A. Mimich, and C. A. Mead, IEEE Trans. on Elec. Dev. 43, 1972 (1996).

What about:

Three terminal devices:WriteReadErase

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A two‐terminal MemFlash‐cell

M. Ziegler, et al., Appl Phys. Lett. 101, 263504 (2012).

• Reduction to a two‐terminal device: simultaneous read/write

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MemFlash

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•Memristive devices with improved performance:Yield, parameter spread, retention, etc.

• System architecture:Mixed signal circuits including memristive devices

•Which neurobiological schemes are essential ?Long Term Potentiation, Spike Time Dependent Plasticity, Feedback Loops, Coding, Encoding etc.

How large is the benefit of memristive Devices for Neuromorphic Electronics?

Doo Seok Jeong et al. Towards artificial and synapses: a material point of viewRSC Advances 3, 3169 (2013).

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Thanks to …

Martin Ziegler, Mirko Hansen, Christoph Riggert, Rohit Soniand Marina Ignatov

Thorsten Bartsch

Wolfgang Krautschneider, Dietmar Schröder

Karlheinz Ochs, Thomas Mussenbrock

Doo Seok Jeong

AG Nanoelektronik 2012

Paul Meuffels

Financial support from Schleswig‐Holsteins Landesgraduiertenförderung is gratefully acknowledged.

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…and Axel as Pavlov`s Dog,…

…my daughter Nora for painting her dog