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University of Connecticut
Automated Counterfeit IC Physical
Defect Characterization
Team 176
Wesley Stevens
Dan Guerrera
Ryan Nesbit
Advisors: Professor Mohammad Tehranipoor
Professor Domenic Forte
Electrical and Computer Engineering
Summary
Automated system for identifying physical defects
Analyze various images (Optical, X-Ray, SEM)
Return relevant data regarding counterfeit status
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Background
Threat of counterfeit ICs increasing
Over 1 million counterfeit ICs found in military supplies
Can cause critical failure of systems
Leads to loss of life in military and medical applications
Current physical defect analysis done manually
Need expert to spend time on tests
Tests can be destructive
Subject to human error
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Image Capture
Several GB of optical and SEM images obtained
already
Needs to be consistent in terms of lighting as well
as distance from lens to chip
Image capture of the top, bottom, and sides of the
chip
Different algorithms can be used for specific parts of chip
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Project Overview
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Project Overview
Three main steps
Acquire images of suspect and/or “golden” ICs
Run different algorithms based on image location
Algorithms return altered images with highlighted defects
Uses images from different locations to find defects
Leads/Pins – scratches, bends, corrosion
Surface – scratches, discoloration, pattern variation
Markings – missing, faded, different location
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Image Groupings
Golden-IC Analysis
Take identically positioned images for one golden IC and
one suspect IC
Use comparison algorithm to determine inconsistencies
Self-Reference Analysis
Take images from different locations of the package of a
suspect IC
Use comparison algorithm to determine inconsistencies
Group Comparison Analysis
Compare data of individual ICs to group average
Large variation suggests counterfeit
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Example Group Comparison
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Algorithms
Binary Transformations
Text recognition
Ghost markings, extraneous markings
Edge detection
Feature acquisition and measurement
Statistical Analysis
Texture comparison
Scratches, color variation, different pattern, corrosion,
contamination, package damage
Feature matching
Image alignment
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Binary Transformations
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Feature Extraction & Edge Detection
One key goal is the ability to recognize defects.
This can be achieved through feature extraction.
Allows us to look at individual objects.
If it is a part of the device we can measure and
compare it to other objects
If it is not then we can try to identify what it is.
Also allows us to delve into text recognition.
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Statistical Analysis
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Feature Matching
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Project Plan
Finish algorithms for finding each defect in
taxonomy
Combine algorithms into one cohesive program
Automate algorithm ordering and parameters
Refine outputs to give meaningful results for user
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