University of Connecticut

Automated IC Defect Characterization

Wesley StevensDan GuerreraRyan Nesbit

Professor Mohammad TehranipoorElectrical and Computer Engineering

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Summary

Automated system for identifying physical defectsTake images for input

Microscope, X-Ray, IR

Image Analysis

Output type, location, and confidence level of defect

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Background

Threat of counterfeit ICs increasingOver 1 million counterfeit ICs found in military supplies

Can cause critical failure of systemsLeads to loss of life in military and medical applications

Current physical defect analysis done manuallyNeed expert to spend time on tests

Tests can be destructive

Subject to human error

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Project Overview

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Project Overview

Three main stepsAcquire images of suspect ICs

Give set of images to the detection algorithm

Algorithm returns altered images with highlighted defects

Ideal implementationImaging and algorithm on same device

Device takes consistent images

Algorithm determines both location of defects and types of defects

No reference images needed

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Defect Taxonomy

PackageScratches

Discoloration

Faded markings/text

Pattern change

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Existing Detection Methods

Incoming InspectionDocumentation and visual inspection of parts

Package AnalysisMaterial and composition

DelidRemove part packaging, inspect die and wires

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Proposed Detection Methods

Golden-IC AnalysisTake identically positioned images for one golden IC and one suspect IC

Use comparison algorithm to determine inconsistencies

Self-Reference AnalysisTake images from different locations of the package of a suspect IC

Use comparison algorithm to determine inconsistencies

Group Comparison AnalysisIdentify patterns that suggest a defect

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Algorithm Approaches

Statistical Averaging

Error Margin

Pattern RecognitionEdge/Blob detection

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Project Plan

Golden IC comparison

Group Comparison

analysis

Self-reference analysis

Statistical averaging

Error margin

Pattern recognition

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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