Automated Counterfeit IC Physical Defect Characterization

1. 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 www.chase.uconn.edu Example Images Motivation Surface Analysis Pin Analysis • 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 Original Image: Original Image: Objective and Solution • 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 Transformation: Isolation of distinct objects: Approach and Methods • 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 • 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 • 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 General Specifications Algorithm Results: Counting objects: 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 Future Work • Expand Defect Coverage • Improve Algorithm Robustness • Expand Group Comparison Analysis • Create Graphical User Interface • Modify User Results Feature Matching and Alignment Difference: 0.1103 • 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 • 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 About the Authors Wesley Stevens (EE/CE), Dan Guerrera (CE), and Ryan Nesbit (EE) are full time undergraduate students at the University of Connecticut.