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TRUDEVICE 2013
Noise Reduction on Memory-based PUFs
Vincent van der Leest Roel Maes Geert-Jan Schrijen
Intrinsic-ID B.V.High Tech Campus 9
5656 AE Eindhoven, The Netherlands
Mafalda Cortez Said Hamdioui
Delft University of TechnologyMicroelectronics & Computer Engineering Lab
Mekelweg 4, 2628 CD DelftThe Netherlands
TRUDEVICE 2013Avignon, France, May 30-31, 2013
Extended version of this work will appear in HOST 2013M. Cortez, S. Hamdioui, V. vd Leest, R. Maes, G.-J. Scrijen, “Adapting Voltage Ramp-up Time for Temperature Noise Reduction on Memory-based PUFs”.
Noise Reduction on Memory-based PUFs
Security based on cryptographic algorithms Permanent key storage is highly
prone to physical attacks
Solution:1. Do not permanently store a key in
Non-Volatile Memories (NVMs)
2. Generate the key only when needed (extract it from a physical structure of the IC)
3. Delete the key
2
Motivation - Secure Key Storage
Physical Unclonable Functions(PUFs)
Device 1 Device 2
~ 50%difference
~ 10%errors
PUFs’ characteristics• Uniqueness
• Reproducibility
Noise Reduction on Memory-based PUFs
Motivation…………… shortcomings and contributions
3
• Shortcomings• Techniques to minimize the noise (increase reproducibility) of PUFs
• Our contributions• Efficient scheme to reduce the noise in memory-based PUFs
• Most of work focus on• Developing new PUFs types• Proving PUF uniqueness• Validating with silicon PUF reproducibility• Developing peripheral circuits (e.g., error correction)
Noise Reduction on Memory-based PUFs
Layout
4
• Memory-based PUF Secure Systems
• Enrolment versus reconstruction
• Stability parameters classification
• Technology versus non-technology parameters
• Simulation Set-Up & Results
• Industrial/Silicon results
• Block diagram of the new scheme
• Conclusion
Noise Reduction on Memory-based PUFs
Memory-based PUF Secure Systems
5
Enrollment: Define Key Reconstruction: Reconstruct Key
PRR and PR must be close enough!
PUF measurement
FuzzyExtractor
FuzzyExtractor
PUF measurement ’
Helper Data(public)
PRR(PUF Reference Response)
PR(PUF Response)
Key Key≈=
Reproducibility is crucial… but expensive!
Noise Reduction on Memory-based PUFs
Stability Parameters Classification
6
Stability parameters
Non-technologyparameters
Channel length
modulation
Threshold voltage
Temperature Supply voltage
Technology parameters
Geometry of transistors
LengthOxide
thicknessDoping
concentrationRamp-up
timeWidth
Manufacture Operation
NOTE: Classification based on: M. Cortez, A. Dargar, S. Hamdioui and G.-J. Schrijen, “Modeling SRAM start-up behavior for Physical Unclonable Functions”, DFT, 2012
Can non-technology parameters be used for noise reduction?
Noise Reduction on Memory-based PUFs
Simulation Set-Up & Results
7
…1 1 0 0 1 0
Monte Carlo
Vth1Vth2
Vth3Vthn Vthn+1
Vth1000
Pow
er-u
p
PUF fingerprint
Generation of an SRAM fingerprint of 1k
NOTE: Vth distribution according to: W. Zhao, F. Liu, K. Agarwal, D. Acharyya, S.R. Nassif, K.J. Nowka and Y. Cao, “Rigorous extraction of process variations for 65nm CMOS design”, ESSDERC, pp. 89–92, 2007.
Q5
Q1
Q6
Q2
VDD
Vin Vout
Set-up• HSPICE: SRAM cell with its periphericals • 45nm Low Power BSIM4 model
Noise Metric: Fractional Hamming Distance (FHD)
Experiments performed• Voltage Ramp-up Time : 3 tramp (10μs, 50μs and 90 μs)• Temperature : 3 Temp (-400C, 250C and 850C)• Measurements : 20 Meas (different noise random seeds)
Noise Reduction on Memory-based PUFs
Simulation Results… Noise at different enrollments
8
• For Temp below enrollment, max FHD is lower if tramp is longer • For Temp above enrollment, max FHD is lower is tramp is shorter
Can noise be reduced by manipulating Temp and tramp?
Typical approach is not optimal!
max FHD max FHD max FHD
Noise Reduction on Memory-based PUFs
Industrial/Silicon Results: Setup & Experiments performed
9
Measurment flow
Set temperature(-400C, +250C, +850C)
Read and store fingerprint
Power-down for 1 second
X9 (same Temp and Tramp)
x9
Power-up with tramp (10µs up to
500ms)
X 2
Experiments performed• Temperature : 3 Temp (-400C, 250C and 850C)• Voltage Ramp-up Time : 10 tramp (from 10μs up to 500ms)• Measurements : 10 Meas (each measurement has different noise)
Used devices
Noise Reduction on Memory-based PUFs
Industrial/Silicon Results: Original results
• Max noise 28%• Min noise 4.5%
10
Measured noise as FHD (precision 0.5%)
• Worse μ-BCHD = 0.37• Best μ-BCHD = 0.5
• Worse Hmin = 0.40• Best Hmin= 0.87
Optimization algorithms• Reproducibility
• Identify tramp for enrollment temp such that the reproducibility is the highest • Uniqueness
• Identify tramp for enrollement such that Entropy is the highest (Hmin)
Best value
Worse value
Legend
Noise Reduction on Memory-based PUFs
Industrial/Silicon Results: Reproducibility optimization algorithm
Main observations• Noise reduction for all devices at all conditions!• Fastest tramp during enrollment does not lead to the lowest noise• For Temp below enrollment, max FHD is lower for longer tramp
• For Temp above enrollment, max FHD is lower for shorter tramp
• Algortithm detorates μ-BCHD and Hmin for some devices (e.g., 130nm LP SRAM)
11
Noise Reduction on Memory-based PUFs
Industrial/Silicon Results: Reproducibility optimization algorithm
Main observations• Noise reduction for all devices at all conditions!• Fastest tramp during enrollment does not lead to the lowest noise• For Temp below enrollment, max FHD is lower for longer tramp
• For Temp above enrollment, max FHD is lower for shorter tramp
• Algortithm detorates μ-BCHD and Hmin for some devices (e.g., 130nm LP SRAM)
12
New best / worse• Max reduction 6% to 2% (3X)• New max noise = 17% (previous 28%)• New min noise = 2% (previous 4.5%)
Noise Reduction on Memory-based PUFs
Industrial/Silicon Results: Uniqueness optimization algorithm
13
Main observations• μ-BCHD and Hmin improved (or maintained for SRAM)• Fastest tramp during enrollment does not lead to the best Entropy (exception 130nm LP SRAM)
• For Temp below enrollment, max FHD is lower for longer tramp
• For Temp above enrollment, max FHD is lower for shorter tramp
• Noise increased in some cases (e.g., Buskeeper PUF)
Noise Reduction on Memory-based PUFs
Block diagram of the new scheme
14
Main observations• No adaptations of the PUF-circuit required• Standard components
Noise Reduction on Memory-based PUFs
Conclusion
15
• Novel idea for reducing noise on memory-based PUF fingerprints• Operational conditions• Temp versus tramp
• Validated using both SPICE simulation and silicon measurements
• Easy to implement
• Noise reduction achieved is up to 3x lower• Reduce overall cost and improve the robustness