ENS 8 21 2015

University of ConnecticutEngineered Nano-Signature (ENS): A Novel Authentication

Technology

All Rights Reserved 2

Project Personnel

PI Name: Mehdi Anwar

Student(s)Kiarash AhiAnas MazadyAbdiel Rivera

manufacturerSupply chainconsumer

Designing/ Encoding/

Fabrication of the ENS the ENS

Extracting/ Reading /

Decoding the ENS

Secure direct connection line between authentic

manufacturer and costumer

AES Cipher KeyENS Lookup Table


StepsDESIGN AND FABRICATION OF THE ENS USING METAMATERIALSTesting the robustness of the ENS against aging and ambient effectsExtraction and decoding of the encrypted code Realizing AES encoding and decoding of the data using ENS


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


2nd Reflection

Demonstration


StepsDesign and fabrication of the ENS Using MetamaterialsTESTING THE ROBUSTNESS OF THE ENS AGAINST AGING AND AMBIENT EFFECTSExtraction and decoding of the encrypted code Realizing AES encoding and decoding of the data using ENS


Robustness of ENS against aging and ambient effects

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 components 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.


Effects of Aging and Humidity

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Room Humidity High Moisture

Histogram of the original image and the filter.

Histogram after filtration

Luma components (Y2×2 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

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3 Months Old ENS

Adjusting the histogram to remove the background.

The luminance of the speckle pattern is extracted


Similarity Analysis: The ENS is RobustTable 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

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

Aging and Ambient do not have significant impact on the extracted information


StepsDesign and fabrication of the ENS Using MetamaterialsTesting the robustness of the ENS against aging and ambient effectsEXTRACTION AND DECODING OF THE ENCRYPTED CODE Realizing AES encoding and decoding of the data using ENS


Written Pattern

5 10 15 2015 5 10 2020 10 5 1510 20 15 5

84 um

53 um Ratio: 84/53=0.63Note: The IC is setup horizontally and thus 0.63 would bethe ratio of (horizontal/vertical) dimensions in this work.


Demonstration

Here the functionality of the code is demonstrated by a simple example which is perceivable by human vision system. Same structures with varying dimensions were considered: 25*25, 50*50, and 100*100 pixels.

TransposedOriginal


Demonstration of the Structural similarity detection code

1) When window is set to 25*25, distinct peaks appear suggesting similar structures with 25*25 pixels. Due to convolution properties these peaks appear in 50*50 pixels.

2) When window is set 50*50, distinct peaks appear suggesting similar structures with 50*50 pixels. Due to convolution properties the these peaks appear in 100*100 pixels.

3) When the window is larger than the structure, peaks are not distinct. The extracted dimension = structure + window. e.g. 75 is obtained for the dimension of 25*25 structures in case of a 50*50 widow.

25*25 50*50


Extracting the Dimensions of the Fine Structures in Practice

Dimensions and locations of the fine structures are extracted from the speckle pattern

The Speckle Pattern 531x 841 pixel


Results of Applying of Windows of different Dimensions

(a) (b)

(c)


Results of Appling of Windows of different Dimensions

Figure (a): By applying windows of the dimensions less than 24*24 pixels almost no local peak is distinguishable in the mesh grids. Its interpretation is that the sizes of the windows are smaller than the smallest structure, so each fine structure may occur in several iterative movements of the window (peaks will be merged as a result).Figure (b): By applying windows of the dimensions larger than 24*24 pixels almost all the local peaks are smoothed in the mesh grids. Its interpretation is that the sizes of the windows is larger than the smallest structure thus more than one fine structure may occur in one window at the same time (peaks will be smoothed into each-other).



Looking from the above gives the dimension of the peak



Note that the peak is the result of the convolution of two square windows; thus only one side of the valley to peak is counted in x and y dimensions of the previous Figure. The dimension of the surface of the ENS is 100 by 100 um. The images here are 242 by 181 pixels; a simple fraction gives the numbers form the previous figures in um as :

20/2.4 = 8.33 umby

18/1.87 = 9.62 um

This is inconsistent with the dimension of the fine structures.

9 µm

0.5 µm

9 µm


Deriving the location of the fine structures


StepsDesign and fabrication of the ENS Using MetamaterialsTesting the robustness of the ENS against aging and ambient effectsExtraction and decoding of the encrypted code REALIZING AES ENCODING AND DECODING OF THE DATA USING ENS (FUTURE WORK)


Advanced Encryption Standard (AES)

The AES is a block cipher and therefore encrypts and decrypts blocks of data according to a secret key. The AES is intended to replace the Data Encryption Standard (DES).Both the AES and the DES are iterated ciphers, which means that a certain sequence of computations, constituting a round, is repeated a specified number of times.The AES and the DES rely on the concepts of diffusion and confusion.


AES block structureThe AES transforms the plaintext into the cipher text via a sequence of intermediate 128-bit states.The 16 bytes in the state or a round key can be represented by the string of bytes

An equivalent representation is as a 4x4 array S of these bytes


Structure

The standard view of the AES is as a series of operations on a square array of 16 bytes which is to be represented by a 4x4 matrix.We developed a code to extract the dimension and locations of the ENS. So we can realize the AES-128 matrix by attributing the elements of the ENS matrix to the AES-128 matrix.


Attributing coded values to ENS dimensions and locations

For adding to the complexity of the procedure an additional lookup table is dedicated to the ENS. Regarding the fact that the locations of the ENS elements are known in the matrix, two orders of difficulties can be realized, namely dimensions and locations of the ENS in the matrix.


Attributing coded values to ENS dimensions and locations

5 μm 10μm 15 μm 20 μm1x1 32 55 3e ec1x2 80 a8 90 e5 1x3 79 6c 31 821x4 6b 7f 4f e0 2x1 c8 12 43 062x2 5a 8e 2b f3… … … … …4x4 34 e6 52 4d

32 43 f6 a8 88 5a 30 8d 31 31 98 a2 e0 37 07 34

5 10 15 2015 5 10 2020 10 5 1510 20 15 5

84 um

53 um


A complete description of AES encryption requires an initial AddRoundKey ("Round 0") followed by NR rounds of computation, where Nr= 10, 12, or 14 for AES-128, AES-196, or AES-256 respectively.The last round of computation does not contain a MixColumns operation.


Encoding

AES Cipher Key = 2b 7e 15 16 28 ae d2 a6 ab f7 15 88 09 cf 4f 3c

5 10 15 2015 5 10 2020 10 5 1510 20 15 5

84 um

53 um

32 88 31 e043 5a 31 37f6 30 98 07a8 8d a2 34

39 02 dc 1925 dc 11 6a84 09 85 0b1d fb 97 32

AES Cipher Key

ENS Lookup table

ENS Lookup Table on the previous slide

Two orders of complexity: 1. AES Cipher Key 2. ENS Lookup Table

manufacturerSupply chainconsumer

Designing/ Encoding/

Fabrication of the ENS

the ENSExtracting/ Reading /

Decoding the ENS

Secure direct connection line between authentic manufacturer

and costumer

AES Cipher KeyENS Lookup Table

39 02 dc 1925 dc 11 6a84 09 85 0b1d fb 97 32

39 02 dc 1925 dc 11 6a84 09 85 0b1d fb 97 32

Laser Pointer

Extracted ENS Pattern

Numerical Encrypted

Matrix

Numerical Encrypted

Matrix


Decoding

AES Cipher Key = 2b 7e 15 16 28 ae d2 a6 ab f7 15 88 09 cf 4f 3c

5 10 15 2015 5 10 2020 10 5 1510 20 15 5

84 um

53 um

32 88 31 e043 5a 31 37f6 30 98 07a8 8d a2 34

39 02 dc 1925 dc 11 6a84 09 85 0b1d fb 97 32

ENS Lookup table

AES Cipher Key

The device is authentic

AES Cipher Key and ENS Lookup Table are provided via a secure direct connection between authentic manufacturer and costumer.The Encrypted data matrix (the left matrix) can be provided on the package of the IC.

Ext

ract

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the

sam

e E

NS

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rix fr

om th

e sp

eckl

e pa

ttern

ENS Lookup Table (on the previous slides).


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

Effects of ambient (e.g. humidity, temperature) and aging did not have significant impact on the designed ENS

The fabricated ENS were extracted from the laser speckle pattern by a developed Matlab code

In our future work a procedure is to developed for the ENS data to be encrypted at the manufacterer side and encrypted at the customer side using AES.

35CHASE Meeting


PublicationsENS

1. Kiarash Ahi, Mehdi Anwar, “Implementation of robust engineered nanostructures for identification of authentic electronics components”, Accepted for the next issue of International Journal of High Speed Electronics and Systems, 2015.

2. Kiarash Ahi and Mehdi Anwar, “Encoding of engineered nanostructures by detection of their dimensions and locations for authentic purpose”, Accepted for the next issue of International Journal of High Speed Electronics and Systems, 2015.

3. Kiarash Ahi, Abdiel Rivera, Anas Mazady, and Mehdi Anwar, “Embedding and Fabrication of Authentication Signatures by Robust Engineered Nanostructures”, accepted for DMSMS 2015, Arizona, USA.

4. Kiarash Ahi and Mehdi Anwar “A Novel Approach for Extracting Embedded Authentication Engineered NanoSignatures”, accepted for DMSMS 2015, Arizona, USA.

5. Kiarash Ahi, Abdiel Rivera, Anas Mazady, and Mehdi Anwar, “Embedding and Fabrication of Authentication Signatures by Robust Engineered Nanostructures”, accepted for DMSMS 2015, Arizona, USA.

6. Kiarash Ahi, Abdiel Rivera, Anas Mazady, and Mehdi Anwar, “Authentication of electronic components using embedded nano-signatures”, Connecticut Symposium on Microelectronics & Optoelectronics (CMOC), Bridgeport, CT, April 1, 2015.


PublicationsENS

7. Abdiel Rivera, Kiarash Ahi, and Mehdi Anwar, “Engineering Nanostructures for Counterfeit Prevention”, Connecticut Symposium on Microelectronics & Optoelectronics (CMOC), Bridgeport, CT, April 1, 2015.

8. Kiarash Ahi, Abdiel Rivera, Anas Mazady, and Mehdi Anwar, “Embedding Complex Nano-Signatures for Counterfeit Prevention in Electronic Components,” CHASE Conference on Trustworthy Systems and Supply Chain Assurance, Storrs, CT, April 8-9, 2015

9. Kiarash Ahi, Anas Mazady, Abdiel Rivera, M. Tehranipoor and Mehdi Anwar, “Multi-level Authentication Platform Using Electronic Nano-Signatures,” 2nd International Conference and Exhibition on Lasers, Optics and Photonics, Philadelphia, September 8-10, 2014.

10. Mehdi Anwar, “Engineered Nano-Signatures (ENS) & Other Novel Authentication Technologies,” Joint Audit Planning Committee, NASA, June 17, 2014.


PublicationsTHz

1. Kiarash Ahi, Mehdi Anwar, “Terahertz techniques for quality control and counterfeit detection”, Accepted for the next issue of International Journal of High Speed Electronics and Systems, 2015.Kiarash Ahi, Abdiel Rivera, Anas Mazady, and Mehdi Anwar, “A Novel Approach for Enhancement of the Resolution of Terahertz Measurements for Quality Control and Counterfeit Detection”, accepted for DMSMS 2015, Arizona, USA.

1. Kiarash Ahi, Navid Asadizanjani, Sina Shahbazmohamadi, Mark Tehranipoor, Mehdi Anwar, “Terahertz characterization of electronic components and comparison of terahertz imaging with x-ray imaging techniques” Proc. SPIE 9483, Terahertz Physics, Devices, and Systems IX: Advanced Applications in Industry and Defense, 94830K (May 13, 2015)

2. Kiarash Ahi, Navid Asadizanjani, Mark Tehranipoor, Mehdi Anwar, “Authentication of electronic components by time domain THz Techniques”, Connecticut Symposium on Microelectronics & Optoelectronics (CMOC), Bridgeport, CT, April 1, 2015.

3. Kiarash Ahi, Navid Asadizanjani, Sina Shahbazmohamadi, Mark Tehranipoor, Mehdi Anwar, “THZ Techniques: A Promising Platform for Authentication of Electronic Components,” CHASE Conference on Trustworthy Systems and Supply Chain Assurance, Storrs, CT, April 8-9, 2015

4. Anas Mazady, Hung-Chi Chou, and Mehdi Anwar, "Terahertz Spectroscopy as a Means for Counterfeit IC detection”, Center for Hardware Assurance, Security, and Engineering (CHASE) meeting, Storrs, CT, April 8-9, 2014.

5. Anas Mazady, HungChi Chou, Mohammad Tehranipoor, and Mehdi Anwar, "Terahertz Spectroscopy: A Technology Platform for the Detection of Counterfeit Electronics", Diminishing Manufacturing Sources and Material Storages (DMSMS), Kissimmee, FL, Dec 2-5, 2013

6. H.C. Chou, J. W. Zeller, G. Ujjwal, M. Tehranipoor, and M. F. Anwar, “Time-domain THz spectroscopy for counterfeit IC detection” SPIE Defense, Security, and Sensing, April 30, 2013.

ENS 8 21 2015

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Transcript of ENS 8 21 2015