Deep Learning Electronic Component Authenticity Identification
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Solution Overview
Problem
Counterfeit electronics pose a significant risk in the global systems supply chain, particularly in critical systems like medical devices, air traffic control, and GPS systems, due to the challenges in identifying and authenticating electronic components.
Innovation Solution
A method and system utilizing deep learning techniques to authenticate electronic components by obtaining chip data, extracting feature information, and providing it to a trained deep learning model to determine the authenticity of the component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional authentication methods are used, then the system can identify counterfeit electronics, but the process is extremely challenging, time consuming, and expensive
Solution Approach 1:
The patent replaces traditional manual or conventional automated authentication methods with a deep learning-based system. The deep learning model processes chip data and provides authenticity indications, substituting complex manual analysis with an intelligent system that achieves both high accuracy and speed.
Solution Approach 2:
The system transforms raw chip data into extracted feature information through parameter transformation. By converting the data into meaningful features that the deep learning model can process, the system achieves efficient authentication while maintaining high accuracy, resolving the time-accuracy tradeoff.
2Device complexity
If chip data is processed directly without noise reduction, then the authentication process is simpler, but the noise and interference in the data reduce authentication accuracy
Solution Approach 1:
The patent extracts feature information from the raw chip data, separating the meaningful authentication features from the noise. This extraction process creates a cleaned version of the data that maintains authentication accuracy while reducing the complexity of processing noisy data directly.
Solution Approach 2:
The extracted feature information acts as an intermediary between the raw chip data and the deep learning model. This intermediate representation filters out noise and interference, allowing the model to process data more efficiently while maintaining high authentication accuracy.
3Measurement precision
If manual authentication methods are used, then the system can provide accurate authentication, but it requires highly skilled personnel increasing operational complexity and cost
Solution Approach 1:
The deep learning model provides self-service authentication capability. The system automatically processes chip data, extracts features, and provides authenticity indications without requiring highly skilled personnel. This automation maintains accuracy while dramatically improving ease of operation.
Solution Approach 2:
The patent substitutes manual skilled operations with an automated deep learning system. The model performs the authentication functions that previously required human expertise, replacing mechanical manual processes with an intelligent automated system that maintains accuracy while simplifying operation.
Data Source
AI summary
A method and a system for identifying authenticity of an electronic component is disclosed. The method may include obtaining chip data of an electronic component; extracting feature information of the chip data for reducing noise of the chip data; providing the feature information of the chip data to a trained deep learning model; and providing a user with an authenticity indication for the electronic component based on an output of the deep learning model. Other aspects, embodiments, and features are also claimed and described.