Sill Torres - Aging Analysis
Unintrusive Aging Analysis based on Offline Learning
Frank Sill Torres*+, Pedro Fausto Rodrigues Leite Jr.*, Rolf Drechsler+
*Universidade Federal de Minas Gerias, Belo Horizonte, Brazil+University of Bremen, Bremen, Germany
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Motivation
Aging of integrated systems of rising importance
But:
– (Still) less critical for customer applications
– Interest in low weight solutions (S.M.A.R.T. for HDDs, …)
This work:
– Low-weight aging monitoring / remaining lifetime prediction
– Based on (offline) learning
V
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Aging Monitoring
In-situ slack sensors – Detection / preview of failing timing– Added invasively to (selected) critical
paths
Online self-testing
– Built-In Self-Test (BIST) during test mode
– Additional circuitry (Scan chains, …)
Aging sensors
– Report experienced aging– Ignores system’s activity
C
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Software Layer
Prediction
Reporting
APDB MDB
Compression
Counter-measures
Unintrusive Aging AnalysisArchitecture
APDB, MDB: Databases
Simulations
Profiling
Stress Test
Field Data
VDD, Freq., Sleep
Hardware
Stress sensors
Temp, V, Activity
5Sill Torres - Aging Analysis
Sensors– Temperature, voltage, activity, …
– Low area offset, unintrusive
Profiling
– Simulations
Aging characterization at design time
Various scenarios (Temp, VDD, activity, …)
Parameter can vary
– Also possible: Data from stress test / field
Unintrusive Aging AnalysisProfiling
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Unintrusive Aging AnalysisCompression and Profile Storage
10 20 30 40
Set 4
Set 3
Set 2
Set 1
Set 0
0
… Sensor ST,4 … MTTFin Set 0
[%]… in Set 4
[%]
20 % 32 % 2e2 h
Compression of simulated / measured data
Insertion in Databases
Sen
sor V
alue
Time
Set 4
Set 3
Set 2
Set 1
Data bases for
– Profile Data (APDB)
– Measured Data (MDB)
MTTF – Mean Time To Failure
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Prediction– Relate Measured data (MDB) to Profiling Data (APDB) for
prediction of current Remaining Useful Lifetime (RUL)
– Three Models (Linear, Euclidean Distance, Correlation)
Unintrusive Aging AnalysisPrediction Models
Prediction APDB MDB
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Results
0 %
20 %
40 %
60 %
80 %
100 %
INV c499 c880 c1355 c5315
Accuracy of Prediction
Linear Euclidian Correlation Static
Best (Linear): 90.4%
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Conclusions
Methodology for low weight prediction of aging of integrated systems
Application of profiling data
Consideration of varying parameters
Simulation results: Prediction accuracy ca. 90 %→ Not exact but
– Enables proactive counter measurements
– User can be warned
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Thank you!
Unintrusive Aging Analysis based on Offline Learning
11Sill Torres - Aging Analysis
Activity Sensor
[7] R. Baranowski, et al., "On-line prediction of NBTI-induced aging rates," in DATE 2015, pp. 589-592.
Monitoring of switching activityof the circuit’s primary inputs (PI) or pseudo-primary inputs (PPI)
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Aging
Altera, RELIABILITY REPORT 56, 2013
0
20
40
60
80
130 nm 90 nm 65 nm 40 nm 25 nm
Stratix Stratix II Stratix III Stratix IV Stratix V
FIT
(Fai
lure
s in
109
h)
