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About Omics Group
OMICS Group International through its Open Access
Initiative is committed to make genuine and reliable
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About Omics Group conferences
OMICS Group signed an agreement with more than
1000 International Societies to make healthcare
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make the perfect platform for global networking as it
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Omics group has organised 500 conferences,
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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 ENS
Laser pointer
The Challenge
Increased proliferation of counterfeit electronic
components threatens both commercial and
defense industries in the areas of product
performance, reliability and dependability.
Impacts
Negative impact on innovation
The threat to welfare of consumers
Existing Solutions
Visual Inspection
Optical Characterization
Electrical Testing
Material Inspection
X-ray Imaging
THz Imaging/Analysis
(another ongoing effort at CHASE)
Lacks Conclusivity and Cross Referencing
Counterfeit detection still has much intrinsic subjectivity, and thus the confidence
level of the associated results is lacking
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Smart Electronics
PUFS/Smart Electrical-Optical Technology
May 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.
Requirements
Inexpensive
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-Signatures
Technology Comparison
U Unknown
E  Excellent
Applied
DNA
Nanot
ags
IR
Pigme
nt
RFID
SPUF
IC
Coati
ng
ENS
Initial Cost
High
High
High
High
Low
Low
Low
Operating Cost
High
High
U
U
Low
U
Low
Incorporation
Time
Slow
Slow
Fast
Fast
Fast
Fast
Fast
Component
Authentication
Slow
Fast
U
Fast
Fast
Fast
Fast
Authentication
Accuracy
E
E
U
E
U
U
E
Resistance to
Imitation
Low
High
Low
Low
High
Low
High
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Multi-Layer Authentication
Second
Reflection
Test
Pass/Fail
Level 1
FAIL
COUNTERFEIT
Structural Data
Stored for
Authentication
•
First Level Authentication
PASS
Optical
Measurement
Structural
Variation
Family of ICs
Level 2
TRNG
•
Second Level Authentication
Key
Pass/Fail
Counterfeit
Key
ENS Input
Design
Modified & Coded
ENS
Structural Data
Stored for
Authentication
Optical
Measurement
Optical Image
Information
ENS Structure
DataAuthentication
FAIL
Authentic
Counterfeit
Level 3 Third Level Authentication
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Introduction to Metamaterials
• 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
empty glass
regular water, n = 1.3
“negative” water, n = -1.3
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Introduction to Metamaterials
μ
ABSORPTION
ε < 0, μ > 0
Plasma
ε < 0, μ < 0
Not found in nature
NEGATIVE
REFRACTION!!
POSITIVE
REFRACTION
ε > 0, μ > 0
Dielectrics
ε
ε > 0, μ < 0
Gyrotropic
ABSORPTION
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Negative Refractive Index
Snell`s Law

 2
Regular Material
Metamaterial
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Realization of Metamaterials
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
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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Schematic of ENS
0.5 µm
9 µm
9 µm
Single pixel of metamaterial
ENS was written on a
commercially available IC
using Electron Beam
Lithography (EBL) followed
by Au sputtering
ENS using a 5×5 array of metamaterials
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SEM Images
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Optical Microscope Images (> 1000x)
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Experimental Setup
IC
Laser
Laser
Focus
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Demonstration
2nd Reflection
Video Demonstration
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Optical Image of Metal Patches
CHASE Meeting
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Laser Experiment on Metal Patch
Reflection from the metal
patch is very weak and the
2nd reflection spot is not
observed.
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Laser Experiment on ENS (Background Minimized)
Distance Adjustment
Y-axis Adjustment
Height Adjustment
Video Demonstration
Reflection from ENS
Laser pointer
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Frequency Information
25
650
40
649.5
35
20
Intensity (mV)
Intensity (mV)
30
15
10
5
25
20
15
10
5
Height Adjustment
0
0
-5
400
600
800
Wavelength (nm)
620
630
640
650
660
670
680
Wavelength (nm)
Reflection from ENS
Laser pointer
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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
the Luma information of the images have been kept for the comparisons.
Y
matrices
which
represents
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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Image Processing
Original image is decomposed into three parts using the YIQ
model:
Luminance (Y) – containts the information about brightness
Inphase (I) and Quadrature (Q) – contain color information
Processing is performed on the luminance part, and the
other two reamain untouched to reconstruct the original
color
Luminance (Y)
Inphase (I)
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Qudrature (Q)
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Luminance
Inphase (I)
Qudrature (Q)
Reconstracted image
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Histogram of Image Intensity
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Image Reconstruction: Zone 1
Original color
image
Reconstructed
color image
Image with background removed
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Image Reconstruction: Zone 2
Reconstructed color image
Image with higher intensity pixels
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Image Reconstruction: Zone 3
Reconstructed color image
Image with highest intensity pixels
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Extracted Structural Information
MATLAB Routine
1. Load image
2. Decompose image into YIQ model
3. Calculate image resolution
4. Calculate image size in cm
5. Create histogram and segmentize the image
6. Compute FFT distribution in terms of wavenumber
7. Determine the wavenumber at which peak occurs
8. Calculate dimension
Image at 3.1V
Estimated
Dimension
(um)
Image at 3.5V
Image at 4.5V
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
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Effects of Aging and Ambient
3 Months Old ENS
High Moisture
Room Humidity
The horizontal axis represents
brightness levels and the
vertical
axis
represents
number of pixels with
corresponding brightness.
9000
8000
5000
8000
7000
7000
4000
6000
6000
5000
5000
4000
4000
3000
3000
2000
2000
3000
2000
1000
1000
1000
0
0
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.6
0.7
0.8
0.9
1
Histogram of the original image and the filter.
4
4
x 10
4
x 10
x 10
2.5
2.5
2
2
2
1.5
1.5
1.5
1
1
1
0.5
0.5
0.5
0
0
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
1
Histograms after filtration
Extracted dimension from FFT
60 µm × 108 µm
Luma components (Y matrix) of the Images after filtering
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Similarity Analysis
Table 1: Structural SIMilarity (SSIM), for identical images value = 1, for the poorest similarities 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
Table 2: Mean squared error (MSE), for identical images value = 0, for the poorest similarities value = 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
Table 3: Euclidean distance(ED), for identical images value = 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
CHASE Meeting
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