Download Reliability in VLSI Design

PPGEE ’08
Reliability in Nanometer Technologies –
Problems and Solutions
Dr.-Ing. Frank Sill
Department of Electrical Engineering, Federal University of Minas Gerais,
Av. Antônio Carlos 6627, CEP: 31270-010, Belo Horizonte (MG), Brazil
[email protected]
http://www.cpdee.ufmg.br/~frank/
Agenda

Motivation

Failures in Nanometer Technologies

Techniques to Increase Reliability

Shadow Transistors
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Motivation

Reliability important for
 Normal
user
 Companies
 Medical
applications
 Cars
 Air
/ Space Environment
…
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Motivation
[Mill.]
Transistors [Mill.]
Transistors
130 nm
400
400
90 nm
300
300
100
100
0
0
100 nm
Yonah
65 nm
151 Mill.
200
200
Prescott
125 Mill.
45 nm
50 nm
Northwood
55 Mill.
Yonah,
151 Mill.
0 nm
2002
2002
2004
2004
Year
Year
2006
2006
Probability for failures increases due to:
 Increasing transistor count
 Shrinking technology
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150 nm
Technology
Wolfdale
410 Mill.
500
500
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2008
Dimensions
m
10
cm
100
nm
10
111mm
cm
µm
µm
Source: „Spektrum der Wissenschaften“
„65 nm“-Transistor
Source: Intel
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Failures in Nanometer
Technologies
Process Failures

Occur at production phase

Based on

Process Variations

Particles

…
Source: Mak
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Sub-wavelength Lithography
Generation [µ]
365nm
248nm
193nm
180nm
130nm
0,1
Gap
90nm
100
65nm
Generation
45nm
32nm
13nm
EUV
0,01
1980
1990
2000
2010
Lithography Wavelength [nm]
1000
1
10
2020
Source: Mark Bohr, Intel
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Field-dependent Aberrations
CELL _ A( X1, Y1)  CELL _ A( X 0 , Y0 )  CELL _ A( X 2 , Y2 )
Big Chip
Towards Lens
Lens
Cell A
(X1 , Y1)
Cell A
Wafer
Plane
(X0 , Y0)
Cell A
Center:
Minimal
Aberrations
Edge: High
Aberrations
(X2 , Y2)
Source: R. Pack, Cadence
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LineWidth [nm]
Varying Line Width
2.3
2.2
2.1
2.0
1.9
1.8
150
60
100
50
Wafer X
0 0
20
40
Wafer Y
Source: Zhou, 2001
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Mean Number of Dopant
Atoms
Random Dopant Fluctuations
Causes Vth Variations
10000
1000
100
10
1000
500
250
130
65
32
Technology Node (nm)
Non-uniform
Uniform
Source: Borkar, Intel
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Power Density
Sun’s
Surface
Power Density (W/cm2)
10000
Rocket
Nozzle
1000
100
Nuclear
Reactor
Prescott
Pentium®
8086 Hot Plate
10 4004
P4
8008 8085
Pentium®
386
286
486
8080
1
1970
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1980
1990
Year
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2000
2010
Source: Moore, ISSCC 2003
12
Temperature Variation
Power Map
On-Die Temperature

Power density is not uniformly distributed across the chip

Silicon is not a good heat conductor

Max junction temperature is determined by hot-spots

Impact on packaging, cooling
Source: Borkar, Intel
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Temperature Variation cont’d
Power4 Server Chip
Source: Devgan, ICCAD’03
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Delay [s]
Drain current IDS [pA]
Temperature Variation cont’d
Temperature [°C]
Threshold voltage Vth changes with temperature  drain-source current
changes  delay changes
Source: Burleson, UMASS, 2007
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Supply Voltage Drop
Source: Trester, 2005
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Failures Through Increasing Delay

FF
Logic
FF
Data are
processed before

clock phase is over
Clk
Clock (Clk)

VDD↓, Temp.↑, ...
FF
→ Data processing
FF

longer than clock
phase
→ Wrong Data in
next clock phase!
Clk
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Logic too slow!
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Soft Errors
Source: Automotive 7-8, 2004
1

In 70’s observed: DRAMs occasionally flip bits for no apparent reason

Ultimately linked to alpha particles and cosmic rays

Collisions with particles create electron-hole pairs in substrate

These carriers are collected on dynamic nodes, disturbing the voltage
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Soft Errors cont’d

Internal state of node flips shortly

If error isn’t masked by

Logic: Wrong input doesn’t lead to wrong output

Electrical: Pulse is attenuated by following gates

Timing: Data based on pulse reach flipflop after clock transistion
 wrong data
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FF
FF
FF
FF
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Electromigration
Electromigration:
Top View

Transport of material caused
by the gradual movement of
ions in a conductor

One of the major failure
mechanisms in interconnects.

Proportional to the width and
thickness of the metal lines

Inversely proportional to the
current density
Void
Metal 1
Metal 1
Whisker, Hillock
Cross Section View
Metal 1
Thick Oxide
Metal 2
Source: Plusquellic, UMBC
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Electromigration cont’d
Void in 0.45mm Al-0.5%Cu line
Source: IMM-Bologna
Whiskers in Sn
Source: EPA Centre
Hillocks in ZnSn
Source: Ku&Lin,2007
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Time-Dependent Dielectric Breakdown (TDDB)

Tunneling currents
Wear out of gate oxide

Creation of conducting path
between Gate and Substrate,
Drain, Source

Depending on electrical field over
gate oxide, temperature (exp.),
Source: Pey&Tung
and gate oxide thickness (exp.)

Also: abrupt damage due to
extreme overvoltage (e.g. ElectroStatic Discharge)
Source: Pey&Tung
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Variability Trends
70
60
Vdd
% Variability
50
Vth
40
Performance
30
Power
20
Lgate
10
0
90
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80
70
65 57 50 45 40
Technology Node [nm]
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32
28
Source: Burleson, UMASS, 2007
23
Variability Trends cont’d
Soft Error / Chip (Logic & Mem)
Relative SER
150
100
50
0
180 130 90
65
45
32
22
16
Technology [nm]
Source: Borkar, Intel
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Variability Trends cont’d
Frequency and sub-threshold leakage variations
Normalized Frequency
1.4
1.3
Frequency
~30%
30%
1.2
Leakage
Power
~5-10X
130nm
~1000 samples
1.1
1.0
5X
0.9
1
2
3
4
Normalized Leakage (Isub)
5
Source: Borkar, Intel
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Variability Trends cont’d
10000
16
Current Density Jox
Reliability (Weibull slope β)
Increasing probability for Gate-Oxide-Breakdown
12
8
4
0
1000
100
high-k?
10
1
0
2
4
6
8
10
12
Gate Oxide Thickness [nm]
Source: Kauerauf, EDL, 2002
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180 nm 90 nm
45 nm
22 nm
Technology
Source: Borkar, Intel
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Future Designs
 100 BT integration capacity
100
Billion
Transistors
 Billions unusable (variations)
 Some will fail over time
 Intermittent failures
Source: Borkar, Intel
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Approaches to Increase
Reliability
Failure Measurement
 Reliability R(t):
– Probability of a system to perform as desired until time t
– Example: R(tx) = 0.8  80 % chance that system is still running at time tx
 Mean Time To Failure MTTF:
– Average time that a system runs until it fails
 Failure rate λ:
– Probability that system fails in given time interval
R (t )  e   t

MTTF   R(t )dt 
0
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1

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Bathtube Failure Model
Wearout period
Infant mortality
 Increasing failure rate
 Based on TDDB, EM, etc.
 Declining failure rate
 Based on latent reliability
defects
Normal lifetime
Failure rate
 Constant failure rate
 Based on TDDB,
EM, hot-electrons…
1-40
weeks
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7-15 years
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Time
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Classification
Failure
Temporary
Permanent
Defects, wearout, out of
range parameters , EM,
TDDB ...
Transient
Intermittent
Process variations, infant
mortality, random dopant
fluctation, ...
Radiation
Non - Radiation
Soft errors
Power supply, coupling,
operation peaks
Source: Mitra, 2007
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The Whole System Counts!
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Triple Module Redundancy (TMR)
Input
Logic L
A
Copy of
Logic L
B
Voter
Output
C
Copy of
Logic L
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Triple Module Redundancy: Voter
Hardware realization of 1-bit majority voter
A
OUT = AB+AC+BC
Out
B
C
Requires 2 gate delays
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B
C
OUT
1
1
0
1
0
0
1
0
0
1
0
0
0
1
1
1
:
:
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Triple Module Redundancy cont’d
Note: For a constant module failure rate 
1.0
Reliability
TMR
0.5
Simplex (only 1 module)
0
Time
 After certain time: Reliability of TMR system is lower than of simplex
system
 Why: After some time probability that 2 modules are wrong is higher
that 2 modules are working!
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Self Adaptive Design

Extend idea of clock domains to Adaptive Power Domains

Tackle static process and slowly varying timing variations

Control VDD, Vth (indirectly by body bias), fclk by calibration at
Power On
Test inputs
and
responses
fclk
Test
Module
VDD
Module
VBB
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Self Adaptive Design: Example

21 submodules per die

Applying 0.5V Forward/Reverse Body Biasing (FBB/RBB) in steps
of 32 mV, respectively
noBB
ABB
within die ABB
Accepted die
100%
97% highest bin
100% yield
60%
20%
0%
Higher Frequency 

Source: Borkar, Intel
For given Freq and Power density

100% yield with ABB

97% highest freq bin with ABB for within die variability
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Razor Flip-Flop

For uncertainty- and variation-tolerant design

Razor methodology
 Voltage-scaling
methodology based on real-time
detection and correction of circuit timing errors
 Use
the actual hardware to check for errors
 Latch
the input data twice:

Once on the clock edge, and then a little later

If the data is not the same, you are going too fast
Source: Austin, Computer Magazine, 2004
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Razor Flip-Flop cont’d
D
Logic
Stage n
Shadow FF
M
U
X
Main
flip-flop
Q
Logic
stage n+1
Error_Sl
Shadow
latch
CLK
Comperator
Error
CLK_delayed
CLK
CLK_delayed
D
Instr 1
Instr 2
Error
Q
Instr 1
Instr 2
Source: Austin, 2004
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Shadow Transistor
Approach
TDDB model
TDDB between gate and channel
For an Inverter, 65nm-BPTM:
100%
Gate
20
Gate Oxide
Source
Drain
75%
50%
Model:
15
Vout/VDD
10
rel. delay
25%
5
0%
0
RGC
- RGC [kΩ] →
W
W1
W2
W= W1+W2
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Based on: Segura et. al., “A Detailed Analysis of GOS Defects
in MOS Transistors: Testing Implications at Circuit Level” 1995.
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TDDB Model cont’d
TDDB between gate and source/drain
For an Inverter, 65nm-BPTM:
Gate
Gate Oxide
Source
100%
Drain
75%
Vout/VDD
50%
Model:
25%
0%
RGD
RGS
W
-RGC [kΩ] →
W
Based on: Segura et. al., “A Detailed Analysis of GOS Defects
in MOS Transistors: Testing Implications at Circuit Level” 1995.
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Shadow Transistors
1. Insertion of additional transistors in
parallel to vulnerable transistors
Shadow transistors (ST)
VDD/Vout
Relative Delay
10
8
6
4
2
0
wo/ ST
100%
75%
w/ ST
50%
w/ ST
wo/ ST
25%
R
- GC [kΩ] →
0%
-R
GC
[kΩ] →
For an Inverter, 65nm-BPTM
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Shadow Transistors cont’d
H-Vt/To
2. Application of H-Vt/To transistors with:
– Higher threshold voltage
– Thicker gate oxide
Less vulnerable to TDDB
10
tox
0.22
MTTF – Mean Time To Failure
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0.15
MTTFH Vt / To
 10 0.22  4.81
MTTFLVt / To
Source: Srinivasan, “RAMP: A Model for Reliability Aware Microprocessor Design”
Stathis, J., “Reliability Limits for the Gate Insulator in CMOS Technology”
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Shadow Transistors cont’d
3. Selective insertion of shadow transistors in parallel to vulnerable
transistors:
– Component reliability depends on
Activity, state, temperature, size, fabrication …
Most vulnerable can be identified
Netlist
modification
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Shadow transistors
only added in parallel
to most vulnerable
devices.
45
Shadow Transistors cont’d
3. Selective insertion of shadow transistors in parallel to vulnerable
transistors:
– Component reliability depends on
New Approach



Activity, state, temperature, size, fabrication …
Estimation of stress factors
Most vulnerable can be identified
Determination of components reliability
Adding redundancy only at most vulnerable components
 Advantage: Lower area, power and delay penalty compared to
complete redundancy or random insertion [Sri04]
Shadow transistors
Source:
[Sri04]
Sirisantana, D&T, 2004
Netlist
only added in parallel
modification
to most vulnerable
devices.
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Shadow Transistors cont’d
Advantages
 Increased reliability in respect to TDDB
 H-Vt/To: Reliability increases by ~5x (for Δtox = 0.15 nm)
 Remarkable increase of system life time
Drawbacks


Higher input capacity → higher delay and dynamic power dissipation
Area increase
Remarks


Only slight improvements for Gate-Drain/Source breakdown
H-Vt/To has to be supported by technology
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ST – Improvement MTTF
Improvemnet of MTTF as regards TDDB
≈ 23 % additional transistors
20%
15%
10%
5%
0%
c17
c432 c499
c880 c1355 c1908 c2670 c3540 c5315 c6288 c7552
our algorithm
random insertion
Insertion of L-Vt/To Shadow Transistors
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Improvemnet of MTTF as regards TDDB
ST – Improvement MTTF (H-Vt/To)
250%
200%
150%
100%
50%
0%
c432 c499 c880 c1355 c1908 c2670 c3540 c5315 c6288 c7552
SPth = 30
SPth = 55
Insertion of H-Vt/To Shadow Transistors
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Take Home Messages

Integrated circuits face several kinds of failures

Decreasing structures sizes create more failure sources

Future designs should (have to) be failure tolerant

Possible approaches:


Triple Module Redundancy (TMR)

Self-Adapting Designs

Razor Flip-Flops

Shadow Transistors
There’s still a lot to do!
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Thank you!
[email protected]
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