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Building reliable chips in future technologies: Fact, fiction or an oxymoron?

Vikas Chandra ARM Research

San Jose, CA

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Acknowledgments

§ Rob Aitken (ARM) § Greg Yeric (ARM) §  Subhasish Mitra (Stanford)

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Some things are beyond our control!

Courtesy Takahashi Kaito (SII Nanotechnology Inc.)

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The eras of computing

1960 1970 1980 1990 2000 2010 2020

Uni

ts

1M

10M Mainframe

Mini

1st Era

100M

1 Billion PC

Desktop Internet

2nd Era

100 Billion The Internet of Things

10 Billion Mobile Internet

2025

§ Cheap §  Low power § Ubiquitous

2030

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Need for reliability

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Lifetime profiles

Expected Lifetime

Usa

ge

low high

high

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Feature size scaling

§ Impact of feature size scaling § Charge discretization § Random manufacturing defects §  Increasing electric field § Thin gate oxides §  Interface defects at Si/SiON interface § Metal defects §  Susceptibility to soft errors

Density doubling ~ every 2.5 Years

0.028

3 2

1.5

0.8 1.0

0.5 0.35

0.25 0.18

0.09 0.13

0.065 0.045

0.032

0.022

§ Beyond 14nm, scaling is more reliant on new materials §  Will require more reliability analysis

Low-K ILD

Hi-K

FinFET

Cu Strain

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CMOS Switches: 19xx - 2015

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Devices 2015 – 2025

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Technology scaling overview

1980 1990 2000 2010 1970

Com

plex

ity

Transistors

Patterning

Interconnect

Al wires

NMOS

2020

Planar CMOS

HKMG Strain

CU wires

PMOS LE, ~λ

LE,

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Technology scaling overview

1980 1990 2000 2010 1970

Com

plex

ity

Transistors

Patterning

Interconnect

Al wires

NMOS

2020

Planar CMOS

HKMG Strain

CU wires

PMOS LE, ~λ

LE,

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FinFET LELE

Technology complexity inflection point?

1980 1990 2000 2010 1970

Com

plex

ity

Transistors

Patterning

Interconnect

Al wires

NMOS

2020

Planar CMOS

HKMG Strain

CU wires

PMOS LE, ~λ

LE,

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Technology complexity inflection point?

1980 1990 2000 2010 1970

Com

plex

ity

Transistors

Patterning

Interconnect

Al wires

NMOS

2020

Planar CMOS

HKMG Strain

CU wires

PMOS LE, ~λ

LE,

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LE

Future technology

2010 2015 2020 2025 2005

Com

plex

ity Transistors

Patterning

Interconnect

FinFET

LELE

SADP

LELELE

EUV

HNW

µ-enh

SAQP

// 3DIC Graphene wire, CNT via

EUV LELE

VNW

Opto I/O

EUV + DWEB

2D: C, MoS

EUV + DSA

Opto int Spintronics

Seq. 3D

Al / Cu / W wires

NEMS

Planar CMOS

W LI

10nm

eNVM

Cu doping

7nm 5nm 3nm

1D: CNT

You are here

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LE

Future transistors

2010 2015 2020 2025 2005

Com

plex

ity Transistors

Patterning

Interconnect

FinFET

LELE

SADP

LELELE

EUV

HNW

µ-enh

SAQP

// 3DIC Graphene wire, CNT via

EUV LELE

VNW

Opto I/O

EUV + DWEB

2D: C, MoS

EUV + DSA

Opto int Spintronics

Seq. 3D

Al / Cu / W wires

NEMS

Planar CMOS

W LI

10nm

eNVM

Cu doping

7nm 5nm 3nm

1D: CNT

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LE

Future technology: patterning

2010 2015 2020 2025 2005

Com

plex

ity Transistors

Patterning

Interconnect

FinFET

LELE

SADP

LELELE

EUV

HNW

µ-enh

SAQP

// 3DIC Graphene wire, CNT via

EUV LELE

VNW

Opto I/O

EUV + DWEB

2D: C, MoS

EUV + DSA

Opto int Spintronics

Seq. 3D

Al / Cu / W wires

NEMS

Planar CMOS

W LI

10nm

eNVM

Cu doping

7nm 5nm 3nm

1D: CNT

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Device lifetime and failure rate

1 – 20 weeks

Normal lifetime Early life failures Wearout

3 – 10 years time

Failu

re r

ate

Increasing manufacturing

defects

Increasing transient errors

Acceleration of aging

phenomena

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Unreliable transistors – 3 phases

Gate to source shorts Insulator cracks Thin oxide defects Small opens Poor vias/contacts

Burn-in

Soft errors - Memory/Flip-flops - Combinational cells Noise - Electrical - RTN

Design Architecture

ECC

Transistor wearout - BTI, TDDB

Metal wearout - Electromigration

ILD wearout

Design margins Overdesign

1 2 3 Early life failures Normal lifetime Wearout/Aging

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Soft error: Impact on circuits 1 0 0 1

upset

transient

Storage cells - SRAM bit cells, Flip-flops

Combinational cells

Flip-flops

Un protected memory

Soft error contribution rate

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Big system reliability §  “Always on” system are especially vulnerable to soft error

§  Vulnerability further increases substantially at low supply voltage § As technology scales down, number of cores scales up

§  Rates of failures increases: From 2 weeks (current) to 1 hour

Source: Draft ICiS 2012 Reliability Workshop

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SRAM upsets 0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  1  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

Single  cell  upset  

Mul0  cell  upset  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  0  0  0  0  0  

0  0  1  

1  0  0  0  

0  1  

1  1  

1  0  0  

0  0  

0  1  

0  0  0  

0  0  1  0  0  0  0  

0  0  

1  0  0  0  0  

cell  area  ê  cell  capacitances  ê  

Qcoll  ê      

Feature  size  scaling    

1   2   3   4   1   2   3   4  Word  arrangement  in  a  mux-‐4  arch  

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FIT rate in 28nm: SRAM

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Soft error mitigation - SRAM

§  ECC with physical interleaving reduces the FIT rate to ~0 §  Physical interleaving: multi cell error à single bit error § Temporal scrubbing to correct single bit errors

Error Correcting

Code

Area

Power Error rate

f

f Memory Compare

Corrector

Data in

Data out

Error signal

M

K

M

K

K

M

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SoC soft error trends Bitcell SER FIT rate per node

0

100

200

300

400

500

600

700

200 150 100 50 0

SCU Avg/node MCU Avg/node

SoC SER FIT rate per node

1

10

100

1000

200 150 100 50 0

Memory SER Logic SER

Even though per memory bitcell SER sensitivity is decreasing, overall FIT per SoC is increasing

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Are all bits equally vulnerable?

Intrinsic soft error

susceptibility

Architectural Vulnerability

Factor

Timing Vulnerability

Factor

Soft error rate

Functional masking

Timing masking

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Soft error in logic § No “cheap” way to protect - spatially distributed bits § Vulnerability factor analysis helps to identify critical ones

Architecture Workload

Critical flip-flop

identification

- Fault injection - Formal methods - …

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Error injection flow

Start application

Application outcome

Pick random injection cycle

Bit flip

Pick random register

Pick random bit Rep

eat

Vanished, Output Mismatch Unexpected Termination, Hang

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Fault injection results

The  Key  message  here  is  that  even  though  most  faults  vanish,  we  s6ll  need  to  worry  about  the  remaining  4-‐8%  of  faults.  

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Cross layer resilience approach Reliability Performance Power Area

Resilience Library

Circuit Logic

Architecture

SIHFT

Application

Fault Injection Guides cross-layer

Physical Design Aware Exploration

ARM IP ARM Core

Technology Library SP&R flow

Emulation

Cross layer resilience

Design

Workload

Critical FF identification

FIT analysis

Fault Injection Optimized

Design

ECO

SER optimized

library

FIT, power, area, performance

Workload

Workload

Library SER data

Circuit resilience example

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FinFET and soft error rate

SER = Adiff e(-Qcrit/Qcoll)

Qcoll ê leads to SER ê

LET

Col

lect

ed c

harg

e Planar SRAM FinFET SRAM

Source: Fang and Oates, TDMR 2011

Technology node

Soft

erro

r ra

te

Reduction due to FinFET

Source: S. Ramey, et al, IRPS 2013

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Random telegraph noise (RTN)

§ Discrete level current fluctuation with time § Caused by charge trapping/de-trapping in dielectric § Behavior is results in 1/f noise for large FETs

Drain current Dra

in c

urre

nt

Time (s)

10%

Source: K. Takeuchi, Renesas, VMC 2011

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RTN scaling

RTN scales faster (1/LW) as opposed to other variability phenomena which scale as 1/(LW)1/2

Sour

ce: K

. Tak

euch

i, R

enes

as, V

MC

201

1

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Phase 3: Reliability towards end of life

1 – 20 weeks 3 – 10 years time

Failu

re r

ate

Transistor wearout - BTI, TDDB

Metal wearout - Electromigration

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Wearout/aging

Vt gm Ids Ioff

Transistor

Igate

BTI

BTI TDDB

BTI TDDB BTI

TDDB

R

EM

BTI à Bias Temperature Instability

TDDB à Time Dependent Dielectric Breakdown

EM à Electromigration

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Bias Temperature Instability (BTI)

§ Reliability concern at Si-SiON interface § Gradual shift in transistor parameters with time

Si Si Si Si Si

* H

* H

H2 H

H

Silicon Gate oxide Poly

* H

Si-H bond recovery

ΔVt

Time

Si-H bond disassociation

Stress stage Recovery stage

PMOS

Negative Bias: Si-H bond disassociation

Zero Bias: Si-H bond recovery

++

++

H

HH0

VDD

VDD

-VDD

+

+

H

H

VDD or 0

VDD

VDD0

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Wearout: Impact on circuits

Combinational State-holding cells (bit cells, flip-flops)

§  Fmax ê § Timing failure as circuits age

D Q

clk

§  Static Noise Margin ê § Read and write stability ê §  Parametric yield loss

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Delay Degradation ≠ Failure

But there could be hold errors due to aging in clock path

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CPU workload dependent aging: A case study

§ Mid-size ARM CPU §  > 100K instances, > 10K sequentials §  Large enough to be interesting §  Small enough for rapid turnaround time

§  Simulation settings §  Vdd = 0.9V, Temp = 105 oC, Lifetime = 3 years §  CP 28LP standard-cell library §  >1K cell-topologies, >10K timing-arcs

§  NBTI and PBTI aging model §  RD: Reaction-Diffusion [Gielen 11, Zheng 09] §  TD: Trapping-Detrapping [Velamala 12]

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Instance based simulation flow

For more details: Workload Dependent NBTI and PBTI Analysis for a sub-45nm Commercial Microprocessor, Intl. Reliability Physics Symposium (IRPS), 2013.

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Block and Path Timing Degradation

§ Dhrystone workload x 10

6 9 12 15 18 0

5

10 4

max = 17.6% avg = 9.5% min = 4.9%

% Timing Degradation

Pat

h H

isto

gram

0 10 20 0

5

10

Block

% T

imin

g D

egra

datio

n

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Path Rank Analysis § Rank paths in fresh and aged design, sorted by slack § Non-critical paths can become critical and vice versa

Path rank % Timing

Degradation Fresh Aged (Dhrystone)

1 14084 7.64 2 9781 7.94 3 9329 8.02 4 12345 7.87 5 6220 8.31 6 36672 7.16 7 7771 8.19 8 11580 7.96 9 28975 7.40 10 20054 7.66

Path rank % Timing

Degradation Aged (Dhrystone) Fresh

1 179394 15.61 2 145042 15.41 3 134419 15.18 4 1413427 17.57 5 272323 15.67 6 224034 15.46 7 331934 15.76 8 275422 15.56 9 481425 16.06 10 208561 15.24

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Workload Power-State Trace

Workload Power-State (Active / Sleep)

vs. Time Average

Active Time

mp3 0.03

web-browse 0.07

3D rendering 0.24

Dhrystone 0.40

video H264 0.54

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Workload-Dependent Processor Aging

Workload

% Timing Degradation

Switching-activity Power-state

Switching-activity and power-state

RD TD RD TD RD TD

mp3 10.0 6.3 3.6 2.5 2.3 1.6 web-browse 10.6 7.3 6.1 4.2 4.1 2.8 3D rendering 12.3 8.4 6.9 4.7 5.4 3.7

Dhrystone 11.2 7.7 10.1 6.9 7.3 5.0 video H264 12.5 8.5 11.4 7.8 9.1 6.2 worst-case 15.6 10.7 15.6 10.7 15.6 10.7

RD: Reaction-Diffusion model TD: Trapping-Detrapping model

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Conclusions

§ Reliability is more critical than ever for RAS critical designs – Servers, HPC etc.

§  Errors can be mitigated at devices, circuits, architecture or even software level §  Lots of opportunities to design reliable systems from ground up

§ As we scale, some things are becoming worse, some better §  BTI, TDDB, EM é §  SER ê

§  Future technology challenges §  Need to keep an eye on the evolution of future devices §  Quantification of wearout impact on CPU PPA scaling §  RTN scaling in the era of new devices (nanowires, compound

semiconductors, CNT)

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Fin