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University of Connecticut
Multi-level Authentication Platform Using Electronic Nano-Signatures
Kiarash Ahi, Anas Mazady, Abdiel Rivera, Mohammad Tehranipoor and Mehdi Anwar
Reflection from ENSLaser pointer
The Challenge
Increased proliferation of counterfeit electronic components threatens both commercial and defense industries in the areas of product performance, reliability and dependability.
ImpactsNegative impact on innovation
The threat to welfare of consumers
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Existing Solutions
Visual Inspection
Optical Characterization
Electrical Testing
Material Inspection
X-ray Imaging
THz Imaging/Analysis (another ongoing effort at CHASE)
Counterfeit detection still has much intrinsic subjectivity, and thus the confidence level of the associated results is lacking
Lacks Conclusivity and Cross Referencing
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Smart Electronics
PUFS/Smart Electrical-Optical TechnologyMay be designed and incorporated in electronic components in the design phase ensuring component authenticity.
Components currently either in the market or in the production line (without any built in component authentication signatures)
The challenge is to be able to incorporate counterfeit identification signatures in COTS electronic components.
RequirementsInexpensive
Dependable/Electrically Robust
Integrable with existing production flow
Fast – able to incorporate signatures within a few seconds without causing delay in production line
Difficult to imitate
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ENS – Engineered Nano-SignaturesTechnology Comparison
HighLowHighLowLowHighLowResistance to Imitation
EUUEUEEAuthentication Accuracy
FastFastFastFastUFastSlowComponent Authentication
FastFastFastFastFastSlowSlowIncorporation Time
LowULowUUHighHighOperating Cost
LowLowLowHighHighHighHighInitial Cost
ENSIC Coating
SPUFRFIDIR Pigment
Nanotags
Applied DNA
U UnknownE Excellent
HighLowHighLowLowHighLowResistance to Imitation
EUUEUEEAuthentication Accuracy
FastFastFastFastUFastSlowComponent Authentication
FastFastFastFastFastSlowSlowIncorporation Time
LowULowUUHighHighOperating Cost
LowLowLowHighHighHighHighInitial Cost
ENSIC Coating
SPUFRFIDIR Pigment
Nanotags
Applied DNA
U UnknownE Excellent
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Multi-Layer Authentication
Family of ICs
Modified & CodedENS
Counterfeit
Counterfeit
StructuralVariation
FAIL
Optical Measurement
•
Optical Image Information
•
ENS Structure
Data
FAIL
Pass/Fail
Pass/Fail
PASS
COUNTERFEIT
Level 1
Level 2
Level 3
ENS Input Design
Optical Measurement
First Level Authentication
Second Reflection Test
Structural Data Stored for
Authentication
TRNGStructural Data
Stored for Authentication
Key
Authentication
Key
Authentic
Second Level Authentication
Third Level Authentication
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• Metamaterials are periodic or quasi-periodic, sub-wavelength metal structures. The electro-magnetic material properties are derived from its structure rather than inheriting them directly from its material composition.
• Electromagnetic properties altered to something beyond what can be found in nature, i.e. negative refractive index
Introduction to Metamaterials
empty glass
regular water, n = 1.3
“negative” water, n = -1.3
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εε < 0, μ < 0Not found in nature
ε > 0, μ < 0Gyrotropic
ε > 0, μ > 0Dielectrics
ε < 0, μ > 0Plasma
ABSORPTION
ABSORPTION
POSITIVE REFRACTION
NEGATIVE REFRACTION!!
Introduction to Metamaterials
μ
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Split ring resonator (SRR) made from copper. c=0.8 mm, d=0.2 mm, r=1.5 mm.
Resonance at 4.845 GHz
Both permeability (μ) and permittivity (ɛ) are negative in microwave range
Realization of Metamaterials
Smith et al. Physical Review Lett. vol. 84, no. 18 (2000)
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Realization of Metamaterials
Yao et al. Science. vol. 321 (2008)
Ag nanowires: diameter=60 nm, length = 1.5 mm Negative refraction was observed in optical frequencies for TM
wave Ag NWs inside porous alumina matrix acts as metamaterials. The effective permittivity parallel to the NW is negative while along
perpendicular direction it is positive
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ENS Employing Metamaterials
A tool allowing identification of good ICs, already been capped and in post design phase.
Allows the detection of over-produced or counterfeit ICs as the counterfeiters will not be able to re-generate the random ENS and resurfacing will destroy the ENS.
Non-destructive
Inexpensive detection: only a laser pointer does the job !!
The ENS array can be tailored to provide signatures unique to the IC.
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9 µm
9 µm
0.5 µm
Single pixel of metamaterial
ENS using a 5×5 array of metamaterials
Schematic of ENS
ENS was written on a commercially available IC using Electron Beam Lithography (EBL) followed by Au sputtering
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Reflection from the metal patch is very weak and the 2nd reflection spot is not observed.
Laser Experiment on Metal Patch
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Reflection from ENSLaser pointer
Laser Experiment on ENS (Background Minimized)
Video Demonstration
Height Adjustment
Distance Adjustment
Y-axis Adjustment
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Reflection from ENSLaser pointer
Frequency Information
Height Adjustment
650
400 600 800
0
5
10
15
20
25
Inte
nsi
ty (
mV
)
Wavelength (nm)
649.5
620 630 640 650 660 670 680-5
0
5
10
15
20
25
30
35
40
Inte
nsity
(m
V)Wavelength (nm)
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Preparing Images for Extracting Structural Information
1. Adjusting the dimension: • The images have been rescaled to 181×242 pixels.• Rescaling have been done By resizing and cropping.• The aspect-ratios of the images have been maintained.
2. Removing the color: • The color data has been removed from the images; only Y matrices which represents
the Luma information of the images have been kept for the comparisons.
3. Filtering the unwanted disturbances and noise:• For removing the unwanted disturbances and noise on the background of the images pixels with intensities lower
than 0.2 has been set to 0.
4. Image Registration: • For the sake of keeping the aspect ratio, in the first set of similarity measurements, the images are not aligned by
Image Registration process. • In a second set of similarity measurements, the images have been first aligned by employing image
registration principles using Matlab (results are not presented in this paper).
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Luminance (Y) Inphase (I) Qudrature (Q)
Original image is decomposed into three parts using the YIQ model:Luminance (Y) – containts the information about brightnessInphase (I) and Quadrature (Q) – contain color informationProcessing is performed on the luminance part, and the other two reamain untouched to reconstruct the original color
Image Processing
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Image with background removed
Reconstructed color image
Image Reconstruction: Zone 1
Original color image
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Image with higher intensity pixels
Reconstructed color image
Image Reconstruction: Zone 2
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Image with highest intensity pixels
Reconstructed color image
Image Reconstruction: Zone 3
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Image at 3.1V Image at 3.5V Image at 4.5V Estimated Dimension
(um)Original Segmented Original Segmented Original Segmented
Lower 200.60 73.89 94.31 60.29 259.95 77.02
Higher 812.84 151.10 132.91 108.08 273.36 160.37
MATLAB Routine1. Load image2. Decompose image into YIQ model3. Calculate image resolution4. Calculate image size in cm5. Create histogram and segmentize the image6. Compute FFT distribution in terms of wavenumber7. Determine the wavenumber at which peak occurs8. Calculate dimension
Extracted Structural Information
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Effects of Aging and Ambient
0
0.5
1
1.5
2
2.5
x 104
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
1000
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7000
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9000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.5
1
1.5
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x 104
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
1000
2000
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5000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Room Humidity High Moisture
Histogram of the original image and the filter.
Histograms after filtration
Luma components (Y matrix) of the Images after filtering
Extracted dimension from FFT
60 µm × 108 µm
The horizontal axis represents brightness levels and the vertical axis represents number of pixels with corresponding brightness.
0
0.5
1
1.5
2
2.5
x 104
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
3 Months Old ENS
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Similarity Analysis
Table 1: Structural SIMilarity (SSIM), for identical images value = 1, for the poorest similarities value =0
Table 2: Mean squared error (MSE), for identical images value = 0, for the poorest similarities value = 1
Table 3: Euclidean distance(ED), for identical images value = 0
Room Humidity High Moisture 3 Months Old ENS
Room Humidity 1 0.9988 0.9791
High Moisture 0.9988 1 0.9779
3 Months Old ENS 0.9791 0.9779 1
Room Humidity High Moisture 3 Months Old ENS
Room Humidity 0 0.0077 0.1333
High Moisture 0.0077 0 0.1408
3 Months Old ENS 0.1333 0.1408 0
Room Humidity High Moisture 3 Months Old ENS
Room Humidity 0 18.3457 76.4078
High Moisture 18.3457 0 78.5268
3 Months Old ENS 76.4078 78.5268 0
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Risk and Roadblocks• Initial Demonstration
• Metal Thickness Needs to be Optimized• Metal Type and Processing Steps Need Optimization
• Significance of Optical Readout and Identifying areas of Interest
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Conclusion
Metamaterials were employed to create ENS
IC chips with appropriated ENS show distinct features in the reflection
IC chips with inappropriate ENS or just metal patches do not show such features
Image processing was performed to extract the structural information of the ENS
35CHASE Meeting
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