Electronic component classification

• US 9,759,757 B2
• Filed: 06/10/2016
• Issued: 09/12/2017
• Est. Priority Date: 12/13/2013
• Status: Active Grant

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: House, Larry J., Engelhart, Dale C.
• Primary Examiner(s): Le, Toan
• Application Number:
  US15/178,708
• Publication Number:
  US 20160282394A1
• Time in Patent Office: 459 Days
• CPC Class Codes: G01R 29/26 Measuring noise figure; Measuring signal-to-noise ratio
• CPC Class Codes: G06F 18/24147 Distances to closest patterns, e.g. nearest neighbour classification
• CPC Class Codes: G06F 21/44 Program or device authentication
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06V 20/80 Recognising image objects characterised by unique random patterns

Abstract

A system and method of electronic component authentication or component classification can reduce the vulnerability of systems (e.g., satellites, weapons, critical infrastructure, aerospace, automotive, medical systems) to counterfeits. Intrinsic deterministically random property data can be obtained from a set of authentic electronic components, processed, and clustered to create a classifier that can distinguish whether an unknown electronic component is authentic or counterfeit.

Claims

1. A system for classifying an unknown electronic component comprising:

an electronic component interface for electrically interfacing with the unknown electronic component;

a sensor adapted to measure a power consumption signal of the unknown electronic component during an idle state via said electronic component interface;

a control unit configured to;

process said power consumption signal of the unknown electronic component measured during said idle state to generate an intrinsic noise signature unique to the unknown electronic component;

retrieve previously-stored information regarding power consumption signals measured during an idle state of a plurality of electronic components;

compare said intrinsic noise signature of the unknown electronic component and said retrieved previously-stored information regarding power consumption signals; and

classify the unknown electronic component based on the comparison of said processed power consumption signal of the unknown electronic component and said retrieved previously-stored information regarding power consumption signals.

2. The system of claim 1 wherein said unknown electronic component is at least one of a passive component and an analog component.

3. The system of claim 1 wherein the system includes an apparatus for generating said previously-stored noise signature information, said apparatus configured to:

measure an intrinsic deterministically random noise signal for each of a plurality of electronic components during an idle state, wherein the plurality of electronic components are authenticated or obtained from a trusted source;

segment each intrinsic deterministically random noise signal into an intrinsic deterministically random noise vector;

transform each intrinsic deterministically random noise vector into a feature vector;

conduct a statistical analysis on each feature vector; and

cluster the feature vectors to generate noise signature information.

4. The system of claim 3 wherein said feature vector is time independent and spatially independent.

5. The system of claim 3 wherein said feature vector is in the frequency domain.

6. The system of claim 3 wherein said statistical analysis on the feature vector includes organizing the feature vector by variance and discarding dimensions where the variance is below a threshold.

7. The system of claim 3 wherein said statistical analysis on the feature vector to includes organizing the feature vector by variance and discarding all but a predefined number of dimensions that have the highest variance.

8. The system of claim 3 wherein said clustering the feature vectors includes shared nearest neighbor clustering.

9. The system of claim 3 wherein said clustering includes comparing the feature vectors to each other using a distance metric.

10. The system of claim 9 wherein the distance metric includes cosine distance.

11. The system of claim 3 wherein said apparatus is configured to transform each intrinsic deterministically random noise vector into a feature vector using a hybrid transformation, wherein said hybrid transformation includes transforming each intrinsic deterministically random noise vector using a plurality of different transformations that are combined to create each feature vector.

12. The system of claim 3 wherein measuring an intrinsic deterministically random noise signal for each of a plurality of known electronic components from one or more trusted sources includes measuring at least one of a current signal and a power signal.

13. The system of claim 1 wherein the component interface includes connections for rail voltage, ground, and an external clock.

14. The system of claim 1 wherein said control unit is configured to compare said processed power consumption signal of the unknown electronic component and said previously-stored information regarding power consumption signals measured during idle conditions of trusted authentic electronic components by mapping said processed power consumption signal into a feature space defined by said previously-stored information regarding power consumption signals measured during idle conditions of trusted authentic electronic components.

15. The system of claim 1 wherein said sensor is adapted to measure said power consumption signal after the unknown electronic device reaches steady state.

16. The system of claim 1 wherein said sensor is integrated with said electronic component interface.

17. The system of claim 1 wherein each of said plurality of components is trusted and authentic.