Vt Variation Effects on
Lifetime Reliability
Brian Greskamp
Smruti R. Sarangi
Josep Torrellas
University of Illinois
at Urbana-Champaign
Motivation
• Chips are wearing out faster as technology scales
• >>180nm: 20 year design lifetime
• 130nm: 10 years
• 65nm: 7 years
• 32nm: ???
• How does process variation affect this trend?
Brian Greskamp
2
Vt Variation
Random Within-Die
Brian Greskamp
3
FPQ
FPMap
FPMul
FPAdd
FPReg
BPred
Vt Variation
UL2cache1
L1D
IntMap
UL2cache2
IntQ
L1I
IntExec
IntReg
DTB
ITB LdStQ
UL2cache3
Systematic Within-Die
Brian Greskamp
4
Vt Variation
Die-to-Die
Brian Greskamp
5
Vt Variation
Component
Variation σ/µ
Random WID
5.2%
Systematic WID
5.2%
Die-to-Die
5.2%
Total Vt variation σ/µ = 9%
Brian Greskamp
6
Time To Failure
lognormal PDF with mu = 1.0, sigma = 0.5
1.20
Probability
probability density ->
1.00
0.80
0.60
0.40
0.20
0.00
0.00
0.50
TTF1%
1.50
2.00
2.50
Time
to
Failure
MTTF TTF
1.00
Brian Greskamp
3.00
7
Failure Mechanisms
• TTF depends mostly on temperature
• Exponential temperature dependence
• Time-Dependent Dielectric Breakdown (TDDB)
• Electromigration (EM)
• Stress Migration (SM)
• Quadratic temperature dependence
• Thermal Cycling (TC)
Brian Greskamp
8
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Lifetime vs Temperature
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Normalized MTTF
Normalized MTTF
10
1
10
Combined
Combined
TC
TC TDDB
TDDB
SM
SM
EM
EM
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65 70 75 80 85 90 95 100 105
60 65 70 75 80 85 90 95 100 105
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Temperature (C)
Temperature (C)
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60
Lifetime vs Temperature
MTTF
as a function of Junction Temperature
for various voltages
Microprocessor MTTF (180nm SOI)
MTTF (Years)
100,000
10,000
1,000
100
55
65
75
85
95
105
115
Junction Temperature (C)
1.3 V
1.2 V
1.4 V
Image: Freescale Semiconductor
Revision History:
Brian Greskamp
Revision History
10
0 105
hermal Cycling
Migration (SM),
ould change
re dominant.
tems; if any
capture this
0 small cells.
ognormally-
R
sa
Heat spreader
Closing the Loop
R
Vt
R
cs2
Vteff
Ps
Cell 1
T
cs1
MTTF
Die surface
Fig. 2.
Thermal model and electrical equivalent of processor die, heat
spreader, and
sink. dependence
= heat
Linear
= Exponential dependence
and static power are both proportional to:
!
"2
!
"
−Vtef f − c2
kT
Ps ∝ µ
exp
q
c3 kT /q
Vtef f = V t − c1 (T − T 0)
The mobility µ itself has a temperature
dependence; we
c
/T
4
M T T 65nm
F ∝ emodel has c1 = 5.0 × 10−4 ,
use µ ∝ T −1.5 . HotSpot’s
c2 = −3.9 × 10−2 , and c3 = 1.3. With these constants, the
leakage model predicts a 41% increase in static power consumption as die temperature
changes from 75◦ C to 90◦ C, even
Brian Greskamp
11
0 105
hermal Cycling
Migration (SM),
ould change
re dominant.
tems; if any
capture this
0 small cells.
ognormally-
R
sa
Heat spreader
Closing the Loop
R
Vt
R
cs2
Vteff
Ps
Cell 1
T
cs1
MTTF
Die surface
Fig. 2.
Thermal model and electrical equivalent of processor die, heat
spreader, and
sink. dependence
= heat
Linear
= Exponential dependence
and static power are both proportional to:
!
"2
!
"
−Vtef f − c2
kT
Ps ∝ µ
exp
q
c3 kT /q
Vtef f = V t − c1 (T − T 0)
The mobility µ itself has a temperature
dependence; we
c
/T
4
M T T 65nm
F ∝ emodel has c1 = 5.0 × 10−4 ,
use µ ∝ T −1.5 . HotSpot’s
c2 = −3.9 × 10−2 , and c3 = 1.3. With these constants, the
leakage model predicts a 41% increase in static power consumption as die temperature
changes from 75◦ C to 90◦ C, even
Brian Greskamp
12
0 105
hermal Cycling
Migration (SM),
ould change
re dominant.
tems; if any
capture this
0 small cells.
ognormally-
R
sa
Heat spreader
Closing the Loop
R
Vt
R
cs2
Vteff
Ps
Cell 1
T
cs1
MTTF
Die surface
Fig. 2.
Thermal model and electrical equivalent of processor die, heat
spreader, and
sink. dependence
= heat
Linear
= Exponential dependence
and static power are both proportional to:
!
"2
!
"
−Vtef f − c2
kT
Ps ∝ µ
exp
q
c3 kT /q
Vtef f = V t − c1 (T − T 0)
The mobility µ itself has a temperature
dependence; we
c
/T
4
M T T 65nm
F ∝ emodel has c1 = 5.0 × 10−4 ,
use µ ∝ T −1.5 . HotSpot’s
c2 = −3.9 × 10−2 , and c3 = 1.3. With these constants, the
leakage model predicts a 41% increase in static power consumption as die temperature
changes from 75◦ C to 90◦ C, even
Brian Greskamp
13
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Temperature Model
Environment (45 C)
Heatsink
R
se
Heat spreader
R
R
cs2
cs1
Cell 1
Heatsink
Heat spreader
Package
Die
Die surface
#.;6 P6 "2(,-%& %))(-9&> ?,.;21@ %*0 (&(81,.8%& (V'.$%&(*1 ?&(:1@ +: C,+8())+,
0.(A 2(%1 )C,(%0(,A %*0 2(%1).*E6
Modeled with HotSpot (Skadron et al.)
Brian Greskamp
0(*8( %*0
+*&> % :'*
12( FC2(,
+61_) M<PO
8(&&) )(C%
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12( &(*;12
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-.*(0A 7(
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"2.) 8+-C
%*0 *+ )C
D%)(0
σVt0 = 0.0
8+*1,.9'1.+
7( )(1√
(0.09/ 3
14
Experiment
For i = 1 to 20000 do
1. Randomly generate diei Vt variation map
2. Partition die into 1000 equally-sized cells
3. Compute temperature for each cell
4. Generate lifetime distribution for each cell
5. Sample lifetime distribution for each cell
6. Diei lifetime = min(cell_lifetimes)
Brian Greskamp
15
Processor Model
• Model Intel Core Solo 65nm floorplan
• Per-unit dynamic power modeling
• SESC cycle-accurate simulator with WATTCH
• Profile from crafty SPECint benchmark
• Steady-state dynamic power = 14W
• HotSpot thermal model
Brian Greskamp
16
Example Die
Before Variation
DTB
BPred
IntReg
IntMap
FPMap
FPQ
IntMap
FPMul
IntQ
FPQ
IntQ
FPAdd
FPReg
BPred
ITB LdStQ
ITB LdStQ
FPAdd
FPReg
FPMul
FPMap
L1D
L1I
L1D
L1I
MTTF=0.90
DTB
IntExec
IntExec
IntReg
After Variation
MTTF=0.75
MTTF=0.95
MTTF=0.85
MTTF=1.0
UL2cache1
UL2cache2
UL2cache3
UL2cache1
UL2cache2
UL2cache3
MTTF=0.8
Fig. 6. Spatial distribution Brian
of cell Greskamp
MTTFs before and after variation for an
example die. Color and contours indicate MTTF.
extreme va
temperature
than norma
C. Future T
Assumin
on lifetime
17
scales, per-
65< Rse G 3&, %!,!%!P# $.# *#6!&(!6!$) !%A&3$ 5H Vt0 L&*!&$!5,
Result:
Vt
Variation
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Impact
on <.#,
Lifetime
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Reduction
Vt Variation
1% due to S1NbNc1
Pleak0
(W )
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Greskamp
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18
Conclusion
• D2D and systematic variations are biggest
problem for aging
• Potential solutions
• Reduce overall leakage
• Increase heatsink size
Brian Greskamp
19
Vt Variation Effects on
Lifetime Reliability
Brian Greskamp
Smruti R. Sarangi
Josep Torrellas
University of Illinois
at Urbana-Champaign