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The Spin Torque Lego from spin torque nano-devices to advanced computing architectures J. Grollier et al., CNRS/Thales, France NanoBrain Spintronics : roadmap Magnetic Giant Magneto-Resistance - 1988 Nanostructures reading the magnetization

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The Spin Torque Lego

from spin torque nano-devices to advanced computing architectures

  • J. Grollier et al., CNRS/Thales, France

NanoBrain

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julie.grollier.free.fr ISAMMA 2013

Spintronics : roadmap

Magnetic Nanostructures

Spin Transfer - 1996

1

HDD read heads

Giant Magneto-Resistance - 1988

reading the magnetization configuration

sensors

writing the magnetization configuration

  • J. Slonczewski JMMM 1996
  • L. Berger PRB 1996
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julie.grollier.free.fr ISAMMA 2013

Spintronics : roadmap

Magnetic Nanostructures

Spin Transfer - 1996

1

HDD read heads

Giant Magneto-Resistance - 1988

reading the magnetization configuration

sensors

writing the magnetization configuration

  • J. Slonczewski JMMM 1996
  • L. Berger PRB 1996
  • digital memories
  • nano-oscillators
  • memristors

New devices

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julie.grollier.free.fr ISAMMA 2013

Spintronics : roadmap

Magnetic Nanostructures

Spin Transfer - 1996

1

HDD read heads

Giant Magneto-Resistance - 1988

reading the magnetization configuration

sensors

writing the magnetization configuration

New Computing Architectures ?

  • J. Slonczewski JMMM 1996
  • L. Berger PRB 1996
  • digital memories
  • nano-oscillators
  • memristors

New devices

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julie.grollier.free.fr ISAMMA 2013

Principle of spin-torque devices

2

magneto-

I(t)

spin torque

magnetization dynamics

resistance R t, ns

resistance variations

m

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julie.grollier.free.fr ISAMMA 2013

  • ut-of-plane torque

Principle of spin-torque devices

2

magneto-

I(t)

spin torque

magnetization dynamics

resistance R t, ns

resistance variations

m 2 torques  2 knobs to engineer the dynamic response

in-plane torque

TIP TOOP

spin torque = +

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julie.grollier.free.fr ISAMMA 2013

In-plane versus out-of-plane torques

Tfield Tdamping Mfree

eq. position

Mfixed

H

3

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julie.grollier.free.fr ISAMMA 2013

In-plane versus out-of-plane torques

Tfield TIP Tdamping Mfree

eq. position

Mfixed

H destabilizes magnetization P AP E

TIP

3

in-plane torque  anti-damping

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julie.grollier.free.fr ISAMMA 2013

In-plane versus out-of-plane torques

Tfield TIP Tdamping Mfree

eq. position TOOP

Mfixed

H destabilizes magnetization modifies energy barrier E P AP HOOP P AP E

TIP

3

  • ut-of-plane torque

 field-like torque in-plane torque  anti-damping

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julie.grollier.free.fr ISAMMA 2013

In-plane versus out-of-plane torques

destabilizes magnetization P AP E

TIP

3

in-plane torque  anti-damping

Magnetization dynamics with the in-plane torque  3 scenarios depending on H

TIP

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julie.grollier.free.fr ISAMMA 2013

H < Hc

Hysteretic Switching

  • 2
  • 1

1 2 150 200 250 300 350 400

Resistance () d.c. current (mA)

AP P

P AP STT E

Binary Memory

4

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julie.grollier.free.fr ISAMMA 2013

H < Hc

Hysteretic Switching

  • 2
  • 1

1 2 150 200 250 300 350 400

Resistance () d.c. current (mA)

AP P

P AP STT E

www.nec.co.jp

Isolation transistor OFF

FIXED LAYER TUNNEL BARRIER FREE LAYER

Binary Memory

www.everspin.com

target : D-RAM replacement

4

Application : STT-MRAM First observations :

Katine et al. PRL 2000 Grollier et al. APL 2001

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Telegraphic Switching E P AP H STT

Stochastic device

5

H  Hc

300 320 340 10 20 30 40 50 60 300 320 340

Resistance ()

  • 2.7 mA

Time (µs)

  • 2.9 mA
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julie.grollier.free.fr ISAMMA 2013

Telegraphic Switching E P AP H STT

Stochastic device

5

: spin dice  nanoscale random number generators

H  Hc

300 320 340 10 20 30 40 50 60 300 320 340

Resistance ()

  • 2.7 mA

Time (µs)

  • 2.9 mA

Dwell times controlled by current

handle to control probabilities

Fukushima et al. SSDM 2010 Fabian et al. PRL 2003 Urazhdin et al. PRL 2003

spin torque =

First observations :

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julie.grollier.free.fr ISAMMA 2013

2 4 6 1 2 3 4 5 6 7

1.2 mA 1.0 mA

Power density (nW/GHz/mA

2)

frequency (GHz)

0.8 mA

Precessionnal state

Spin Transfer Nano-Oscillators

6

H > Hc

STT H P AP E

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2 4 6 1 2 3 4 5 6 7

1.2 mA 1.0 mA

Power density (nW/GHz/mA

2)

frequency (GHz)

0.8 mA

Precessionnal state E P AP H STT

Spin Transfer Nano-Oscillators

6

H > Hc

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julie.grollier.free.fr ISAMMA 2013

2 4 6 1 2 3 4 5 6 7

1.2 mA 1.0 mA

Power density (nW/GHz/mA

2)

frequency (GHz)

0.8 mA

Precessionnal state E P AP H STT

Spin Transfer Nano-Oscillators

6

H > Hc

small - work directly at the GHz tunable with I and H – radiations proof telecommunication, radars, read heads…

ST microwave devices

Kiselev et al. Nature 2003 Rippard et al. PRL 2004

First observations : Applications

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Challenges for ST nano-oscillators

7

Requirements for applications: initial performances: power 100 pW, linewidth 10 MHz

  • Power > 1 µW
  • Linewidth < 1 KHz
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Challenges for ST nano-oscillators

7

Requirements for applications:

  • Power > 1 µW : P  DR2  high TMR MgO based MTJs  
  • Linewidth < 1 KHz

initial performances: power 100 pW, linewidth 10 MHz

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Challenges for ST nano-oscillators

7

Requirements for applications:

  • Power > 1 µW : P  DR2  high TMR MgO based MTJs  
  • Linewidth < 1 KHz  

initial performances: power 100 pW, linewidth 10 MHz

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Strategies to decrease LW

  • 1st source of LW :

mode hopping (freq. spread)

  • 2d source of LW :

phase/amplitude noise

T  0

Tiberkevich et al, PRB 2008

8

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Strategies to decrease LW

  • 2d source of LW :

phase/amplitude noise

Tiberkevich et al, PRB 2008

  • 1st source of LW :

mode hopping (freq. spread)

work with a dynamic mode well separated in energy from other modes

 Vortex gyrotropic mode

LW = 590 kHz P = 0.6 µW

  • A. Dussaux , JG et al., Nature Com. 2010

8

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Strategies to decrease LW

  • 1st source of LW :

mode hopping (freq. spread)

work with a dynamic mode well separated in energy from other modes

 Vortex gyrotropic mode

LW = 590 kHz P = 0.6 µW

  • A. Dussaux , JG et al., Nature Com. 2010
  • 2d source of LW :

phase/amplitude noise  Synchronization

  • B. Georges , JG et al., PRL 2008
  • A. Dussaux, JG et al, APL 2011

rigidify the phase

8

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Microwave oscillator

MR Idc ST

sustained precession resistance osc.

m

stt stt

R t

ac voltage

V=RI V t I t

dc current

20 40

  • 40
  • 20

20 40

Voltage (µV) Time (ns)

 strong advances towards applications

9

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Idc ST

local sustained precession

m

stt stt

I t

dc current

  • exch. inter.

spin wave emission

Spin wave emitter

Tsoi et al. PRL 1998 Demidov et al. Nat. Mat. 2010, Madami et al., Nat. Nano. 2011

Applications: Magnonics (computing with spin waves)

10

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3.5 4.0 4.5 5.0 5.5 20 40 60 80 100 120 140

d.c. voltage (µV) Frequency (GHz)

MR ST

resonance if w = w0 resistance osc.

R

dc voltage

V I

ac current

m

sttI>0 sttI<0

t t t Idc V=RI diode sensitivity = Vdiode / Prf

 250 mV/mW

 sensitivity of the schottky diode at RT

Microwave detector

Spin torque diode

Tulapurkar et al. Nature 2005 - Ishibashi et al. APEX 2010

11

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detector (GMR,TMR) binary memory microwave oscillator stochastic device microwave detector spin wave emitter

Resistance Magnetic Field Resistance d.c. current Resistance Time d.c. voltage Frequency Voltage Time

Lego bricks

…

Spin torque bricks: different functionalities at the nano-scale

12

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Engineering new bricks

13

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Engineering new bricks

13

Can we tailor a spin torque memristor ?

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julie.grollier.free.fr ISAMMA 2013 Chua, IEEE Trans. Circuit Theory (1971) Strukov et al., Nature 2008

v = M(q) i R V OFF ON Vth

  • Nano resistance
  • Tunable (multi-resistance states)
  • Non volatile
  • Non-linear ( Vth )

Memristor

14

Digital multi-level memory Plastic Synapse in Neuromorphic architectures Memory - resistor

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Binary memory  2 state spin torque controlled memristor

  • other works : combine 2 state TMR + resistive switching

Krzysteczko et al. APL 2009 - Prezioso et al. Adv Mater 2011

Magnetic tunnel junction as a memristor

15

  • purely electronic write operation  ST induced DW motion
  • 2
  • 1

1 2 150 200 250 300 350 400

Resistance () d.c. current (mA)

How to obtain the quasi- analog behaviour ?

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L x ) R R ( R R

P AP p

   Resistance: proportion of parallel and anti-parallel domains

Spin torque memristor : concept

16

R t R t

t R

  x0 x1 x0 x1 x2

  • Resistance: DW position
  • DW position: charge

injected

i ) q ( R V q t J x    D  D

Dt j Dt j

Memristor

Grollier et al. WO 2010/ 125181 A1 Wang et al. IEEE 2009

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Vertical injection memristor

Racetrack memory IBM

  • Classical way to move a DW

by spin torque:  lateral current injection

e-

  • Pb2: no resistance variations
  • Pb1: lateral ST inefficient

 use vertical spin currents (Spin Hall effect)  use vertical spin currents in a magnetic tunnel junction

Spin current Spin current Charge current

17

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Vertical injection memristor

  • High TMR
  • High efficiency in MTJs

the out-of-plane torque drives the DW

TOOP

Khvalkovskiy, JG et al., PRL 2009

18

MR

DW displacement resistance variations

R V I

pulsed current

t t t Idc V=RI HOOP

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julie.grollier.free.fr ISAMMA 2013

  • 10
  • 5

5 10 15 16 17

  • 4
  • 2

2 4

resistance () dc current (mA) current density (10

6 A/cm 2)

∆T = 0.8 ns v = 621 m/s

2 4 6 8 10 12 200 400 600 800

DW velocity (m/s) Jpulse (MA/cm

2)

J=-7.8 MA/cm2

HToop

Spin torque memristor

19

1 2 3 4 5 0.0 0.2 0.4 0.6 0.8 1.0

normalized resistance time (ns)

  • A. Chanthbouala, JG et
  • al. Nature Phys. 2011

      Low current density: j  106 A/cm2, high speed: v > 600 m/s

  • P. Metaxas, JG et al.
  • Sci. Reports. 2013
  • J. Sampaio,

JG et al. in preparation

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memristor

d.c. current

Resistance

Spin torque Lego

detector (GMR,TMR) binary memory microwave oscillator stochastic device microwave detector spin wave emitter

Resistance Magnetic Field Resistance d.c. current Resistance Time d.c. voltage Frequency Voltage Time

20

Assembling the bricks to compute

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  • MTJs logic

Spintronic logic

21

  • DW logic
  • Nano-magnet logic
  • All-Spin logic

Ohno et al. IEDM 2010 Allwood et al. Science2005 Niemier et al. J. Phys. C. Matter 2011 Behin-Aein et al. Nature Nano. 2010

detector binary memory

Boolean logic: compete with CMOS + exploit only two bricks: READ WRITE / STORE

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julie.grollier.free.fr ISAMMA 2013

Spin torque Lego Architectures

  • innovative, non-boolean, hybrid CMOs/spintronic architectures
  • take full advantage of spin-torque functionalities

ST-Magnonics ST-Neuromorphic architectures

22

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julie.grollier.free.fr ISAMMA 2013

ST-Magnonics gates

Slavin and Krivorotov, US 7,678,475 B2 Bonetti and Akerman, Magnonics, 2013

Spin Torque Magnonics

spin wave creation, manipulation and detection

Kruglyak et al, Khitun et al., Serga et al. J.Phys.D: Appl. Phys. 2010

23

Spin wave manipulator Spin wave emitter ST nanocontact ST soliton bursting ST damping/anti-damping Spin wave detector dc detector GMR/TMR microwave detector spin diode

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Spin Torque Neuromorphic Architectures

24

Synapse Neuron ST memristor ST stochastic synapse ST nano-

  • scillators

ST stochastic neuron

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julie.grollier.free.fr ISAMMA 2013

Artificial Neural Networks algorithms:

Neuromorphic architectures : motivation

25

  • Massively parallel
  • Analog
  • Relatively uniform
  • Fast
  • Low energy demand
  • Defect tolerant

Semiconductor industry hurdles :

  • Excessive dissipation
  • Multicore scaling
  • Defects
  • P. Dubey, Tech. Intel Magazine 2005

Temam, ISCA 2010 Chen, Temam et al. IISWC 2012

  • very performant (deep networks)
  • key applications : « Recognition, Mining and Synthesis »
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julie.grollier.free.fr ISAMMA 2013

Neuromorphic architectures : basics

26

wij

synapse neuron inputs

  • utputs

xj xi

Neuron : - processing unit

  • integrates information sent from
  • ther neurons through synapses
  • Spikes when threshold reached
  • « integrate and fire »

Synapse : - define how well the information is

transmitted : synaptic weight

  • the weigths are adjustable (synaptic

plasticity)

  • all synapes : network memory

Network performances :

  • interconnectivity

(human brain 104 synapses / neuron)

  • scale of the network

w1 and w3 reinforced

w1 3 2 1 w2 w3

threshold

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Spin torque Synapse

27

1) Store synaptic weights 2) Synaptic plasticity:  10 µm CMOS implementation

SRAM banks plasticity

Schemmel et al., IJCNN 2006

R V

OFF ON

memristor implementation

STDP

1 memristor = 1 nano-synapse 1) Store synaptic weights : non-volatile 2) Synaptic plasticity: tunable

Jo et al., Nanoletters 2010

STDP

Spin torque memristor = ST synapse

d.c. current

Resistance

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julie.grollier.free.fr ISAMMA 2013

Spin torque Neurons

threshold

Zamarreño-Ramos et al., Frontiers Neuroscience 2011

~ 100 µm

CMOS implementation Biological neuron: « integrate and fire » neuron relaxation oscillators neuristor ST neuron

Voltage Time

Pickett et al. Nature Mat. 2013

28

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Spin torque Neurons

threshold

Zamarreño-Ramos et al., Frontiers Neuroscience 2011

~ 100 µm

CMOS implementation Biological neuron: « integrate and fire » neuron relaxation oscillators neuristor ST neuron

Pickett et al. Nature Mat. 2013 Petit, Kim, JG et al. Nature Phys. 2012

29

relaxation oscillator

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ST oscillators can synchronize

30

Grollier at al., PRB 2006 Ruotolo, Cros, JG et al., Nat. Nano 2009

coupling : spin waves

Mancoff et al. Nature 2005 Kaka et al. Nature 2005

coupling : microwaves

  • exp. demonstrated :

up to 4

RL Neural synchronization between different parts of the brain is a key

  • peration for information processing, in particular memory

Buzsaki, « Rhythms of the brain » 2006 Fell and Axmacher, Nature Reviews Neuroscience 2011

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julie.grollier.free.fr ISAMMA 2013

ST Synchronization: associative memories

31

Csaba et al., CNNA 2012 Roska et al., CNNA 2012 Macia et al., Nanotechnology 2011

 Applications: pattern recognition / classification  Code information in the phase of each oscillator  brain-inspired associative memories

pattern recognition - classification

?

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julie.grollier.free.fr ISAMMA 2013

Spin Torque Neural Networks

32

 Several recent proposals of hybrid spintronic/CMOS neural networks

Sharad et al., IEEE Trans Nano 2012, IEDM 2012, Arxiv 2012 Krysteczko et al., Adv. Mater. 2012

inspired from all-spin logic inspired from ST-induced DW motion Synapse = resistive switching Neuron = stochastic firing due to back- hopping

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Spin Torque Neural Networks

33

 Several recent proposals of hybrid spintronic/CMOS neural networks

Sharad et al., IEEE Trans Nano 2012, IEDM 2012, Arxiv 2012 Krysteczko et al., Adv. Mater. 2012

inspired from all-spin logic inspired from ST-induced DW motion Synapse = resistive switching Neuron = stochastic firing due to back- hopping

Resistance Time

stochastic device

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Advantages of stochasticity

Compute with stochastic devices = Saving energy 1) Working below threshold

  • Switching becomes probabilistic
  • Ex : binary probabilistic synapses

Modha and Parkin, US2010/0220523 A1

2) Decrease non-volatility degree

  • Long term memory not required for all synapses
  • Reduce the energy barrier  drastically reduce critical currents

34

 Ultra-low power hybric CMOS/ Spintronic stochastic architectures Noise : key element of neural computation

near-threshold signaling/decision making

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julie.grollier.free.fr ISAMMA 2013

Spin torque Lego

35

f(x)

  • Spin torque versatility: engineering complex functions at the nanoscale
  • Assembling ST bricks: promising for novel computing architectures

Let’s build something different !

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julie.grollier.free.fr ISAMMA 2013

Spin torque Lego

35

f(x)

  • Spin torque versatility: engineering complex functions at the nanoscale
  • Assembling ST bricks: promising for novel computing architectures
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julie.grollier.free.fr ISAMMA 2013

Acknowledgements

Nicolas Locatelli, Vincent Cros, Albert Fert, André Chanthbouala, Steven Lequeux, Joao Sampaio, Peter Metaxas, Sören Boyn, Eva Grilmadi, Paolo Bortolotti, Antoine Dussaux, Alexei Khvalkovskiy, Benoit Georges, Olivier Boulle, Sana Laribi, Cyrile Deranlot, Stéphanie Girod, Rie Matsumoto, Akio Fukushima, Hitoshi Kubota, Kay Yakushiji, Shinji Yuasa, Olivier Temam, Damien Querlioz, Pierre Bessière, Jacques Droulez

36

CNRS/Thales AIST INRIA IEF College de France

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Thank you