Load Calculation for Circuit Boards Using Statistical Models
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Solution Overview
Problem
Conventional methods for estimating loads on electronics circuit boards are inadequate, particularly in predicting failure phenomena due to their reliance on local examinations and individual problem-specific approaches, which are not adaptable to varying conditions and fail to provide a uniform formulation for reliability modeling.
Innovation Solution
A load calculating device and method that utilize a variable acquiring unit, statistical models, and an arithmetic processor to estimate physical quantities related to failure phenomena by acquiring monitoring variables from sensors and performance characteristics, and using regression and probability distributions to determine intermediate and physical quantities, enabling efficient load calculation and reliability assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional local examination methods are used for load estimation, then the analysis process is simple, but the reliability and accuracy of failure prediction deteriorates due to inability to capture complex failure modes
Solution Approach 1:
The patent segments the complex load estimation problem into multiple independent statistical models, each handling specific failure modes (electromigration, whisker, thermal fatigue, etc.). This allows accurate modeling of each failure mode separately while maintaining manageable complexity through modular analysis structures.
Solution Approach 2:
The patent introduces statistical models as intermediary layers between observed load conditions and failure predictions. These statistical models serve as mediators that transform complex physical phenomena into quantifiable reliability metrics, enabling accurate failure prediction without directly modeling every physical detail.
2Adaptability or versatility
If problem-specific individual approaches are used for reliability design, then the method is easy to implement for known problems, but the adaptability to new and varying conditions deteriorates
Solution Approach 1:
The patent creates a universal statistical modeling framework that can handle multiple failure modes (electromigration, whisker, thermal fatigue, delamination, etc.) through a common methodology. This multi-functional approach allows the same statistical modeling process to be applied across different failure modes and varying operating conditions, enhancing adaptability while maintaining implementation consistency.
3Reliability
If uniform formulation is applied for all failure modes, then the methodology is consistent and generalizable, but the precision for specific failure phenomena may deteriorate due to lack of specialized analysis
Solution Approach 1:
The patent applies local quality by tailoring specific statistical models to each failure mode's characteristics while maintaining a uniform overall framework. Each failure mode (electromigration, whisker, thermal fatigue) receives specialized statistical treatment appropriate to its physics, ensuring precision for specific phenomena while preserving methodological consistency through the unified statistical modeling approach.
Data Source
AI summary
A device and method for providing a load calculating device is presented. In one embodiment, a load calculating device can include a variable acquiring unit configured to acquire monitoring variables. The monitoring variables can include in a detected value by a sensor monitoring a state of a circuit board and a performance characteristic obtained by a tool monitoring performance of the circuit board. The device can also store a first statistical model that is one of a regression model, an occurrence frequency distribution and a probability distribution; a second storage configured to store a second statistical model that is one of a regression model, an occurrence frequency distribution and a probability distribution. An arithmetic processor can then be used to calculate the intermediate variable from the monitoring variables according to the first statistical model and calculate the physical quantity from calculated intermediate variable according to the second statistical model.