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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


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


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


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


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


• 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


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


Snell`s Law

Negative Refractive Index

2

MetamaterialRegular Material


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)


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


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.


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


SEM Images


Optical Microscope Images (> 1000x)


LaserLaser

Focus

IC

Experimental Setup


2nd Reflection

Video Demonstration

Demonstration


Optical Image of Metal Patches

20CHASE Meeting


Reflection from the metal patch is very weak and the 2nd reflection spot is not observed.

Laser Experiment on Metal Patch



Laser Experiment on ENS (Background Minimized)

Video Demonstration

Height Adjustment

Distance Adjustment

Y-axis Adjustment



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)


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).


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


Luminance

Reconstracted image

Inphase (I)Qudrature (Q)


Histogram of Image Intensity


Image with background removed

Reconstructed color image

Image Reconstruction: Zone 1

Original color image


Image with higher intensity pixels




Image with highest intensity pixels




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


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

2000

3000

4000

5000

6000

7000

8000

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

2

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

3000

4000

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

1000

2000

3000

4000

5000

6000

7000

8000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

3 Months Old ENS


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 0 0.0077 0.1333

High Moisture 0.0077 0 0.1408

3 Months Old ENS 0.1333 0.1408 0


Room Humidity 0 18.3457 76.4078

High Moisture 18.3457 0 78.5268

3 Months Old ENS 76.4078 78.5268 0


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


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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