1. TEMPLATE DESIGN © 2008 www.PosterPresentations.com TEMPLATE DESIGN @ 2013 by Jifeng Chen Center for Hardware Assurance, Security, and Engineering University of Connecticut Automated Counterfeit IC Physical Defect Characterization Team 176: Wesley Stevens, Dan Guerrera, Ryan Nesbit Advisors: Mohammad Tehranipoor, Domenic Forte ECE Department, University of Connecticut, {wesley.stevens, daniel.guerrera, ryan.nesbit}@uconn.edu {tehrani, forte}@engr.uconn.edu Motivation  Create an automated, user friendly program for identifying physical defects of ICs  Accept wide range of image inputs from various locations  Process different images with specific algorithms  Compile and display comprehensive results Objective and Solution  Increasing number of counterfeit integrated circuits (ICs)  Counterfeit ICs can cause catastrophic failure of systems  Current physical defect tests are destructive, time consuming  An expert is required both for performing tests and analysis of results General Specifications Language:MATLAB Analysis Types:Single, Golden Image Types:Surface, Pin, Text Image Magnification:20x – 100x Ideal Image Resolution:1000 by 1000 pixels Output:Current Algorithm, Identified Defects, Summary Counterfeit determination is based on identifying defects or abnormalities with the IC Physical defects can be categorized by the component or location at which they occur Imaging techniques provide data that can be used to identify defects and determine IC authenticity Defects detected include:  Pin: dents, contamination, color variations, misaligned  Surface: scratches, color variation, improper textures, package damage  Text: markings, ghost markings Surface Analysis Example Images Feature Matching and Alignment Pin Analysis About the Authors  Expand Defect Coverage  Improve Algorithm Robustness  Expand Group Comparison Analysis  Create Graphical User Interface  Modify User Results www.chase.uconn.edu Transformation: Algorithm Results: Original Image: Difference: 0.1103 Future Work Wesley Stevens (EE/CE), Dan Guerrera (CE), and Ryan Nesbit (EE) are full time undergraduate students at the University of Connecticut. Original Image: Isolation of distinct objects: Counting objects: Scratch Analysis: Counts results of all operations Highlights areas with count greater than a given threshold Statistical Averaging: Cleans up excess blocks Determines types of anomalies present in different blocks Correlate types to various defects Scratch Analysis: Converts image to binary using threshold Creates line structuring elements for comparison Iterates through operations while varying parameters Statistical Averaging: Divides image into blocks based on size Calculates Global and Local statistics Compares each block to gathered statistics Flags blocks outside of threshold Approach and Methods Object Isolation: Uses differences in intensity values to find objects Different structuring elements are used to find different objects Algorithm iteratively grows these objects The parameters of each structuring element are changed on each iteration Given the type of structuring element the type of defect can be determined Algorithm will count and find the area of each object This data is also used in determining what type of defects might exist Certain checks exist to help filter out false positives