Integrated Circuit Forensics Using Multi-Modal Sensor Fusion
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Solution Overview
Problem
Existing methods for detecting defects or unauthorized items in a supply chain are inadequate, particularly in large volumes and for mass-produced parts, as they fail to account for manufacturing variations and often focus on single stress indicators like electrostatic discharge (ESD), lacking comprehensive evaluation capabilities.
Innovation Solution
The use of multiple test detection and data collection modes coupled with decision engines such as neural networks, image recognition, and statistical correlation tools, along with electromagnetic sensors, to evaluate microelectronic devices and determine the probability of unauthorized or defective status by analyzing various electrical characteristic modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing single-indicator detection methods are used, then the detection process is simple, but the detection accuracy and comprehensiveness deteriorate due to inability to account for manufacturing variations and multiple stress indicators
Solution Approach 1:
The patent segments the detection process into multiple independent test modes (electrical characteristics, electromagnetic emissions, thermal characteristics, optical characteristics) that can be performed separately but contribute to a comprehensive evaluation. Each test mode focuses on specific stress indicators while maintaining operational independence, allowing the system to achieve high detection accuracy without requiring all tests to run simultaneously, thus managing complexity.
Solution Approach 2:
The patent merges multiple test detection modes and data collection inputs into a unified decision engine that integrates results from electrical characteristics, electromagnetic emissions, thermal characteristics, and optical characteristics. This combination allows the system to evaluate multiple stress indicators simultaneously, achieving comprehensive detection accuracy while the decision engine manages the complexity of integrating diverse data sources.
2Adaptability or versatility
If multiple test detection modes and data collection inputs are integrated, then the comprehensive evaluation capability improves, but the system complexity increases
Solution Approach 1:
The decision engine is designed as a universal system that can process and integrate data from multiple different test modes and data collection inputs. It performs multiple functions including data aggregation, statistical correlation analysis, neural network processing, and decision-making across various stress indicators. This multi-functionality enables comprehensive evaluation capability while the standardized interface of the decision engine manages the complexity of handling diverse inputs.
Solution Approach 2:
The decision engine acts as an intermediary between the various test detection modes and the final evaluation output. It receives raw data from electrical characteristics, electromagnetic emissions, thermal characteristics, and optical characteristics tests, processes this data through statistical correlation tools and neural networks, and produces integrated assessment results. This intermediary role simplifies the system architecture by providing a standardized interface that manages the complexity of integrating multiple diverse test modes.
3Reliability
If comprehensive multi-modal analysis is implemented, then the ability to detect unauthorized or defective items improves, but the data processing requirements and computational complexity increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction at each test mode level before data reaches the decision engine. Electrical characteristics, electromagnetic emissions, thermal characteristics, and optical characteristics data are pre-processed to extract relevant features and reduce data dimensionality. This preliminary action reduces the computational burden on the decision engine while maintaining defect detection reliability by preserving critical diagnostic information.
Solution Approach 2:
The decision engine transforms raw multi-modal data into standardized parameters through statistical correlation analysis and neural network processing. It converts diverse data from different test modes into unified assessment parameters that represent various stress indicators. This parameter transformation reduces computational complexity by working with standardized representations rather than raw multi-dimensional data, while maintaining high defect detection reliability through the use of advanced analytical methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables a more accurate assessment of device status by incorporating multiple data sets and stress indicators, improving the detection of defects or unauthorized items across various stages of the supply chain, including factory and post-factory environments.
Implementation Method 1
electromagnetic (EM) sensors and data collection inputs adapted to sense test data and input the data to an embodiment of the multiple mode analysis decision engine to evaluate a device under test (DUT) system. For example, an embodiment of the invention can incorporate integration of multiple EM sensors as well as data inputs and in synchronization with DUT stimulation for the purpose of producing device unique EM signatures
Data Source
AI summary
A test system including an embodiment having a sensor array adapted to test one or more devices under test in learning modes as well as evaluation modes. An exemplary test system can collect a variety of test data as a part of a machine learning system associated with known-good samples. Data collected by the machine learning system can be used to calculate probabilities that devices under test in an evaluation mode meet a condition of interest based on multiple testing and sensor modalities. Learning phases or modes can be switched on before, during, or after evaluation mode sequencing to improve or adjust machine learning system capabilities to determine probabilities associated with different types of conditions of interest. Multiple permutations of probabilities can collectively be used to determine an overall probability of a condition of interest which has a variety of attributes.