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Online Timing Analysis for Wearout Detection. Jason Blome, Shuguang Feng, Shantanu Gupta, Scott Mahlke University of Michigan. Wearout Mechanisms. There are a lot of them: Electromigration (EM) Time-dependent dielectric breakdown (TDDB) Negative-bias threshold inversion (NBTI)

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Online timing analysis for wearout detection l.jpg

Online Timing Analysis for Wearout Detection

Jason Blome, Shuguang Feng, Shantanu Gupta, Scott Mahlke

University of Michigan

1


Wearout mechanisms l.jpg
Wearout Mechanisms

  • There are a lot of them:

    • Electromigration (EM)

    • Time-dependent dielectric breakdown (TDDB)

    • Negative-bias threshold inversion (NBTI)

    • Hot carrier injection (HCI)

  • All highly dependent on temperature and current density

    • Both increasing fast!

2


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Goals of this Research

  • Low-cost reliable system design

    • How do physical wearout mechanisms progress

    • How to determine that a device has failed

    • How do we maintain operation given failed components

3


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Traditional and Recent Approaches

  • Traditional detection techniques expensive

    • Redundant checking structures

  • Predictive techniques

    • Canary circuits

    • RAMP

4


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Proposed Technique

  • Key Insight:

    • Degradation in silicon  decrease in performance

    • Long incubation time followed by rapid deterioration

  • Examples:

    • TDDB: increases leakage, shifting voltage curves

    • EM: increases resistance

    • NBTI: shifts threshold voltage

5


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Outline

  • Microprocessor model

  • Wearout simulation methodology

  • Wearout simulation results

  • The wearout detection unit (WDU)

  • WDU Analysis

  • Conclusion

6



Simulation flow l.jpg
Simulation Flow

Step 1: Temperature and Activity Analysis

Activity

Trace

Power

Trace

Temperature

Trace

Benchmark

Synopsys

VCS

PrimePower

HotSpot

Netlist

Timing

Parasitics

8


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Synopsys

VCS

Benchmark

Signal Latency

Data

Timing

Age Index

Wearout

Simulation

MTTF

Calculation

Netlist

Temperature

Relative

Wearout

Factors

Activity

Simulation Flow

  • Device Delay = Original Delay * RWF * AI * RV

    • RWF: Relative amount of wearout for a device

    • AI: Performance degradation parameterized by age

    • RV: Random variable

Step 2: Wearout Simulation

9


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Simulation Flow

Step 2: Wearout Simulation

10


Wearout simulation results l.jpg

Signal Latency (ps)

Sample Mean Latency (ps)

Time (years)

Wearout Simulation Results

11


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Exploiting Performance Degradation

  • Exponential moving average:

    • EMA = α(sample – EMAprevious) + EMAprevious

12


Trend analysis l.jpg
Trend Analysis

TRIX can be used to accurately track both local and long term latency trends

13


Wearout analysis circuit l.jpg

0

1

0

1

0

1

0

1

0

0

0

1

0

1

0

Wearout Analysis Circuit

TRIXl

Calculation

1

input signal

1

1

Latency

Sampling

Prediction

TRIXg

Calculation

1

1

14


System integration l.jpg

TRIXl

Calculation

TRIXg

Calculation

+

System Integration

0

Latency

Sampling

Prediction

15


Dynamic variation l.jpg
Dynamic Variation

  • Temperature

    • 50oC  ~4% increase in latency at 130nm

  • Clock jitter

    • Impact on latency varies

    • Mean jitter typically modeled as 0

  • Worst-case variation would need to be sampled 12 times over 4 days

16



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WDU Prediction Results

  • Each unit calibrated for a 30 year MTTF

  • The WDU flagged at least one output from each module prior to the MTTF

18



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Conclusion

  • Low-cost reliable system design

    • Physical wearout mechanisms affect timing

    • Failure prediction can be much cheaper than detection

  • Wearout detection unit:

    • Online timing analysis a good detector of wearout, predictor of failure

    • Generic/self calibrating

20



Technology scaling l.jpg

OR1200 Power Densities

Technology Scaling

  • Quickly shrinking feature sizes

  • Sharp increase in frequency

  • Slow decrease in supply voltage

22



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