Probabilistic Failure Prediction for Electronic Devices
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Solution Overview
Problem
Current methods fail to accurately predict the failure of electronic components and systems due to variability in material properties and loading conditions, leading to inefficiencies in design and maintenance, as they rely on empirical models that do not account for microstructural fatigue mechanisms.
Innovation Solution
A method and apparatus using probabilistic analysis and microstructure-based failure models, incorporating Finite Element Models (FEM) and Monte Carlo simulations to predict component life by simulating stress at nodes, determining Representative Volume Elements (RVE), and comparing statistics to a probability of failure criterion, accounting for grain structure randomness and variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If empirical models are used to predict component failure, then the design process is simplified, but prediction accuracy deteriorates due to inability to account for microstructural fatigue mechanisms and material variability
Solution Approach 1:
The patent segments the failure prediction process into distinct computational stages: microstructure generation, finite element analysis, fatigue damage accumulation, and probability of failure calculation. This segmentation allows each stage to be optimized independently while maintaining overall accuracy, resolving the contradiction between computational complexity and prediction precision.
Solution Approach 2:
The patent replaces empirical mechanical testing with a physics-based computational model that simulates microstructural fatigue mechanisms. By substituting physical experimentation with computational mechanics, the system achieves higher prediction accuracy without requiring extensive physical testing, thus resolving the contradiction between design simplicity and prediction precision.
2Measurement precision
If microstructure-based failure models with probabilistic methods are used, then prediction accuracy improves by accounting for material variability, but computational complexity increases
Solution Approach 1:
The patent performs preliminary generation of microstructure representations and pre-computation of material property distributions before the actual fatigue analysis. This preliminary action allows the complex probabilistic model to be prepared in advance, reducing the computational burden during the actual prediction phase while maintaining high accuracy.
Solution Approach 2:
The patent transforms the complex microstructural problem into a probabilistic framework by changing parameters from deterministic microstructural details to statistical distributions of material properties. This parameter transformation reduces computational complexity while preserving the essential physics of microstructural fatigue mechanisms.
3Reliability
If extensive physical testing is conducted to validate failure predictions, then model accuracy is verified, but time and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of physical components through computational models that replicate microstructural features and fatigue behavior. These digital twins allow for extensive validation and testing in silico, reducing the need for physical prototypes and accelerating the validation process while maintaining confidence in model accuracy.
Data Source
AI summary
The technology includes methods, a system, and a computer readable medium for predicting the failure of an electronic device during design of the device, by receiving data associated with the device, the data including data indicative of a device response to a specific load on the system while the device is in operation, and predicting potential failure of the device using a probabilistic model and the data, wherein the probabilistic model utilizes at least one of fast probability methods and simulation techniques.