SMTA International Conference Proceedings

Data Fusion For Augmented Electronic Counterfeit Detection Efficacy

Authors: Bill Cardoso, Ph.D.
Company: Creative Electron, Inc.
Date Published: 10/13/2013   Conference: SMTA International

Abstract: Multisensor data fusion is an emerging technology applied to Department of Defense (DOD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DOD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and other areas.

The independent use of radiological and optical microscopy for the detection of counterfeit electronic components is a well-established practice in the industry. In this paper we will present the results in fusing radiological and visual data from a set of thousands of components inspected. The scope of this work focuses on two levels of fusion: feature and decision-level.

Feature-level fusion involves the extraction of representative features from sensor data. In feature-level fusion, features from both radiological and optical systems are extracted from multiple observations, and combined into a single concatenated feature vector that is input to pattern recognition approaches based on neural networks.

Decision level fusion involves fusion of sensor information, after each sensor has made a preliminary determination of an entity’s location, attributes, and identity. Examples of features used as entities include pin one location, lead-frame placement, and pin alignment. The decision level fusion engine was fueled with Bayesian inference for final voting determination.

Key Words: 

data fusion, counterfeit components, radiography, x-ray inspection, visual inspection.

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