1. University of Connecticut Automated IC Defect Characterization Wesley Stevens Dan Guerrera Ryan Nesbit Professor Mohammad Tehranipoor Electrical and Computer Engineering

2. All Rights Reserved 2 Summary Automated system for identifying physical defects Take images for input Microscope, X-Ray, IR Image Analysis Output type, location, and confidence level of defect

3. All Rights Reserved 3 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

4. All Rights Reserved 4 Project Overview

5. All Rights Reserved 5 Project Overview Three main steps Acquire images of suspect ICs Give set of images to the detection algorithm Algorithm returns altered images with highlighted defects Ideal implementation Imaging and algorithm on same device Device takes consistent images Algorithm determines both location of defects and types of defects No reference images needed

6. All Rights Reserved 6 Defect Taxonomy Package Scratches Discoloration Faded markings/text Pattern change

7. All Rights Reserved 7 Existing Detection Methods Incoming Inspection Documentation and visual inspection of parts Package Analysis Material and composition Delid Remove part packaging, inspect die and wires

8. All Rights Reserved 8 Proposed Detection Methods 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 Identify patterns that suggest a defect

9. All Rights Reserved 9 Algorithm Approaches Statistical Averaging Error Margin Pattern Recognition Edge/Blob detection

10. All Rights Reserved 10 Project Plan Golden IC comparison Group Comparison analysis Self-reference analysis Statistical averaging Error margin Pattern recognition

11. All Rights Reserved 11 Project Status Have implemented a basic statistical averaging approach, few images tested Next step is to refine the statistical averaging, include basic error margin, comparison between Golden IC set and Suspect IC Need to create specific procedure and setup to acquire consistent images from suspect and golden ICs for testing