Automated Failure Mode Distribution for Automotive ICs
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Solution Overview
Problem
The determination of failure mode distribution (FMD) for integrated circuit chips used in automobiles is typically subjective and incomplete, leading to inaccurate safety integrity level (ASIL) ratings and potential unsafe chip installations, which can result in dangerous automobile crashes.
Innovation Solution
A method and apparatus that automatically determine weighted soft error and permanent error failure mode distributions by tracing from an observation point in a circuit to its input, calculating observability probabilities, and summing weighted probabilities for logic elements within cones of influence, providing a ratio-based distribution and identifying fault locations, thereby eliminating subjective judgment and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual and qualitative engineering judgment is used to determine FMD, then the process is simple and fast, but the accuracy and completeness of FMD determination deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of engineering judgment with an automated computer-based system that performs quantitative analysis. The system automatically traces cones of influence, calculates observability probabilities, and determines FMD values through algorithmic computation rather than human subjectivity, thereby improving accuracy while maintaining efficiency through automation.
Solution Approach 2:
The system enables the design tool itself to perform FMD determination without requiring external expert judgment. The automated analysis tool uses the circuit design data and functional mode information already available in the system to self-determine FMD values through systematic probability calculations, eliminating the need for manual intervention.
2Ease of manufacture
If manual engineering judgment is used to determine FMD, then the process is easier to implement, but traceability and detectability of inaccurate FMD deteriorates
Solution Approach 1:
The automated system provides systematic feedback through detailed reporting that traces the origin of each FMD value. The system generates reports showing the cone of influence, observability probabilities, and calculation paths, creating an auditable trail that allows verification and detection of potential errors, thereby improving traceability while maintaining ease of use through automation.
3Measurement precision
If automated analysis with cone of influence tracing is performed, then the accuracy and completeness of FMD improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments the complex circuit analysis into manageable components by identifying cones of influence associated with specific observation points and failure modes. Each cone is analyzed independently for observability probabilities, and results are aggregated to determine overall FMD. This segmentation reduces the perceived complexity by breaking down the large-scale problem into smaller, systematic sub-problems that can be processed automatically.
4Reliability
If automated determination of FMD is implemented, then the reliability and safety of chip designs improves, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary analysis by pre-calculating cones of influence and observability probabilities during the design phase, before final safety assessments are needed. By preparing this foundational data in advance, the system reduces the time required for actual FMD determination when safety assessments are performed, thereby improving turnaround time while maintaining the thoroughness needed for reliability.
Data Source
AI summary
A method includes tracing from an observation point in a circuit to an input of the circuit to produce a cone of influence that includes a plurality of components of the circuit. The plurality of components is connected at a plurality of nodes in the cone of influence and the plurality of components includes a plurality of logic elements. The method also includes, for each node of the plurality of nodes, determining an observability probability that a logical high or low value at a corresponding node propagates to the observation point. The method further includes determining a weighted soft error probability for each logic element of the plurality of logic elements and determining a weighed soft error failure mode distribution for the cone of influence.