impact of statistical variability and charge trapping on...
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Impact of Statistical Variability and Charge Trapping on 14 nm SOI
FinFET SRAM Cell Stability !
X. Wang1, B. Cheng1, A.R. Brown2, C. Millar2, J. B. Kuang3, S. Nassif3, A.
Asenov1,2!!!1 Device Modelling Group, University of Glasgow, UK !
2 Gold Standard Simulations Ltd, UK 3 IBM Research – Austin, USA !!ESSDERC, 16-20 September 2013, Bucharest Romania! 1!
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Outline!q Introduction!q 14 nm node DG SOI FinFETs!q Simulation of Random Charge Trapping and Statistical Variability Sources!q Compact Modelling Methodology!q Charge Trapping Impact on SRAM SNM!q Charge Trapping Impact on SRAM WNM!q Summary!
2!
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Introduction • Why this study?!• Novel 3-D architecture FinFET will be widely adopted at
14 nm technology, with reduced variability on SOI substrate due to tolerance to low channel doping.!
• However, (1) statistical aspect of reliability due to random individual trapping becomes an increasingly important issue. (2) In addition, charge trapping impact is affected by statistical variability sources.!
• Accurately modeling reliability of nanoscale transistors in circuit level should take care of above mentioned properties, therefore requires a “statistical” method, rather than describing average reliability behavior.!
• SRAM stability is susceptible to variability, therefore statistical study is needed.!
3!
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FinFET
Intel!22nm!!
TSMC!Chang et.,
IEDM,2009!
IMEC!Veloso et.
IEDM, 2009!
IBM!Chang et., VLSI tech.,
2011!
Bulk substrate! SOI substrate!
Fin Edge Roughness:! width, height, slope!
4!
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Simulation Design of 14nm SOI FinFETs (GU-IBM collaboration)
Ref.: ITRS 2010 update!
Double-Gate !SOI FinFET!
!
Lg (nm)! 20!EOT (nm)! 0.8!WF (nm)! 10!HF (nm)! 25!NSD (cm-3 )! 3.0E20!NCH (cm-3 )! 1.0E15!VDD (V)! 0.9!IOFF (nA/μm) ! 10!IDSAT (mA/μm) ! 0.9/0.8!DIBL (mV/V)! 56/65!
Wfin
tox
Hfin
LG
BURIED OXIDE
SUBSTRATE
SOURCE
GATE
DRAIN
HMMC calibrated @ 85°C!
20 30 40 50Position [nm]
0.0
0.5
1.0
1.5
2.0
2.5
Veloc
ity [x
107 cm
s-1 ] source drain
Default DD
Calibrated DD
VG 0.4- -0.8!
Monte Carlo!
5!
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Intrinsic Parameter Fluctuations Statistical Variability Sources
Random dopants! Polysilicon/Metal Gate!Granularity!
Line Edge Roughness!
potential!
TiN!
6!
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Statistical variability simulation
• Each variability source has different impact on the device parameters and performance.!
RDD: ΔRSD , ΔNA!
FER: ΔWFIN , Δconfinement!
GER: ΔLG , ΔSCE!
MGG: ΔΦM , Δψsurf !
Wang, et al, IEDM 2011, pp103-106!
ΔRSD!
7!
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Interaction: Charge trapping vs Statistical variability sources
• FER: local shortenings!
• MGG: metal grains with high currents underneath!
• RDD: current percolation paths!
8!
Sensitive regions!
Wang et al, SISPAD 2012, pp.296-299!
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Vt RTS Distribution and Reliability are affected by Statistical Variability
9!
0 2 4 6 8 10VT (mV)
0.001
0.01
0.1
1
1-CD
F
Uniform Device’Atomistic’ Devices
Single Trapping
Single Trapping!
Wang et al., SNW 2012, pp.77-78!• In the presence of SV, the RTS distribution tail is increased!
Uniform device! Atomistic device!
RTS: random telegraph signal!
Multi-trapping!
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Random charge trapping effect on VT
• First, the average VT shift increases with degradation heuristically;!
• Most important, the statistical variability increases with degradation.!
10!
0.1 0.15 0.2 0.25 0.3 0.35 0.4VT (V)
-4
-2
0
2
4
Nor
mal
Qua
ntile
01E115E111E12
Trapping Density (cm-2)
Poisson distribution !of trapping charge !number is assumed!
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Statistical Compact Modelling Method
• A small set of BSIM-CMG compact model parameters is used to extract statistical samples at fresh stage, also applied to degradation.!
• In circuits random fresh samples are assigned, responding stressed samples are put for stressed transistors.!
• Assume trapping effect is dynamically recoverable.!
• e.g., M2 is biased with high VG and low VD, subject to PBTI!
11!
6-Transistor SRAM cell!
retention!
PU!
PD!
PG! PG!
PU: pull-up transistor, p-FinFET;!
PD: pull-down transistor, n-FinFET;!
PG: pass-gate transistor, n-FinFET;!
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What happens to SRAM SNM after stress?
• Generally, stress induced trapping leads to less static noise margin !
• Heavier N/PBTI, more threshold shift, less stability!!
12!
0 0.2 0.4 0.6 0.8VL (V)
0
0.2
0.4
0.6
0.8V
R (V
)
Fresh Stress (state A)Stress (state B)
A
B
SNM(A)
SNM(B)
SNM: static noise margin, the SRAM stability for read mode!State A: left 0, right 1; State B: left 1, right 0!
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SNM Distribution
• First of all, the distribution is non-Gaussian.!• Compared with 111-fin SRAM cells, 112-fin cells
increase SNM.!• With charge trapping induced degradations, the SNM
is reduced.!13!
Two types of SRAM cells!with fin-number ratio!
of PU:PG:PD,!111 SRAM and 112 SRAM!
are examined!
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Charge trapping effects on SNM
• The average SNM is reduced by up to 30 mV, with charge trapping induced degradations.!
• The statistical variation of SNM increases by 30-40% with degradation.!
• 112-fin SRAM cells show better stability. Compared with 111-fin cells, 112-fin cells increases SNM by ~45% in average.!
14!
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
100
150
200
250SN
M (m
V)
SNM(A), 1:1:1SNM, 1:1:1SNM(A), 1:1:2SNM, 1:1:2
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
6
8
10
12
14
16
18
20
SNM
(mV
)
SNM(A), 1:1:1SNM, 1:1:1SNM(A), 1:1:2SNM, 1:1:2
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What happens to SRAM WNM after stress?
!• In contrary to SNM, WNM increases a bit due to
charge trapping.!• The WNM distribution is non-Gaussian.!
15!
0 0.2 0.4 0.6 0.8VR (V)
0
0.2
0.4
0.6
0.8V
L (V
) FreshStressed
WNM
WNM: write noise margin, SRAM stability for write mode!
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Charge trapping effects on WNM
• The average WNM increases after stress, which is contrary to read SNM.!
• The standard deviation of WNM increases after stress, which is similar to read SNM.!
16!
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
320
340
360
380
400
420
WN
M (m
V)
WNM (state A), 1:1:1WNM, 1:1:1WNM (state A), 1:1:2WNM, 1:1:2
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
8
10
12
14
16
WN
M (m
V)
WNM (state A), 1:1:1WNM, 1:1:1WNM (state A), 1:1:2WNM, 1:1:2
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SNM vs WNM with SV and random charge trapping
• Anti-correlation between SNM and WNM exists for one storing node.!
• Minimum defined SNM and WNM show decorrelations, due to statistically independent transistors responding to two storing states.!
17!
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
-0.8
-0.6
-0.4
-0.2
0
Corre
latio
n Co
effic
ient
(VL=’0’,VR=’1’), 1:1:1Minimum, 1:1:1(VL=’0’,VR=’1’), 1:1:2Minimum, 1:1:2
Correlation between SNM and WNM
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Impact on Six-sigma yield stress induced degradations
• 6-sigma of read SNM is greatly affected by stress induced charge trapping, not only due to average SNM reduction, but also by boosted statistical variability. !
• 112-fin SRAM cells show much better stability than high-density fin cells.!
18!
0 2e+11 4e+11 6e+11 8e+11 1e+12Trapping Density (cm-2)
0
100
200
300
400
µ -
6 (m
V)
SNM, 1:1:1SNM, 1:1:2WNM, 1:1:1WNM, 1:1:2
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Summary • The random charge trapping effect can be
accurately captured using the similar statistical compact modelling practice with statistical variability.!
• SRAM cell read stability is degraded by stress induced charge trapping; The statistical variation of SNM and WNM increased with degradations.!
• 112 FinFET SRAM shows much better stability compared with high-density SRAM cells.!
• With the more random trapping, the read SNM six-sigma yield is reduced dramatically due to enhanced variation. !
19!
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Acknowledge
• It is in part supported by Scottish Funding Council through Knowledge Transfer Project “Statistical Design and Verification of Analogue Systems”.!
!!
!!!Thank you for your attention.!
!! !
20!