Probabilistic Parameter Reduction for Device Testing
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Solution Overview
Problem
Conventional methods for determining which parameters to include or exclude from device testing are complex, time-consuming, and prone to overfitting, often requiring large training sets and being difficult to debug, while also failing to efficiently identify redundant parameters.
Innovation Solution
A computer-implemented method that uses probabilistic representations, such as multivariate Gaussian distributions, to identify redundant parameters by calculating cumulative and incremental probabilities of passing, allowing for a reduced set of parameters to be determined and deployed for testing, thereby improving test efficiency and minimizing test escapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to determine which parameters to include or exclude from device testing, then comprehensive testing coverage is achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent creates a probabilistic model (copy) of the parameter behavior based on training data, which then predicts which parameters are redundant without requiring exhaustive analysis of all parameters. This model serves as a simplified representation that captures the essential relationships between parameters and device performance.
Solution Approach 2:
The patent transforms the parameter selection problem from a deterministic process to a probabilistic one by introducing probability distributions for parameter values. This allows the system to identify parameters with low impact on test outcomes and exclude them, reducing the overall number of parameters to be tested while maintaining reliability.
2Reliability
If conventional methods are used to determine parameter selection, then thorough testing is performed, but device complexity increases
Solution Approach 1:
The patent replaces complex deterministic analysis with a probabilistic model that captures parameter relationships in a simpler form. The model uses probability distributions and statistical measures to represent parameter behavior, making the selection process more manageable and less complex while maintaining thoroughness.
Solution Approach 2:
The patent divides the parameter selection process into distinct phases: training phase where the probabilistic model is built from historical data, and deployment phase where the model is applied to select parameters for new devices. This segmentation reduces complexity by handling different aspects of the problem separately.
3Measurement precision
If large training sets are used in conventional methods, then model accuracy improves, but overfitting risk increases and debugging becomes difficult
Solution Approach 1:
The patent changes the nature of the model from complex deterministic rules to simple probabilistic distributions. By using well-established statistical models (Gaussian distributions, probability density functions), the system achieves good accuracy without the complexity of large neural networks or ensemble methods, reducing overfitting risk and improving debuggability.
Solution Approach 2:
The patent creates a simplified probabilistic copy of the parameter relationships that captures essential patterns without replicating all the complexity of the training data. This abstraction maintains measurement precision while reducing model complexity and overfitting risk.
4Reliability
If all parameters are tested, then testing accuracy is maintained, but execution time increases
Solution Approach 1:
The patent extracts and identifies redundant parameters that have minimal impact on test outcomes using the probabilistic model. These redundant parameters are then removed from the testing process, allowing tests to execute faster while maintaining accuracy for the essential parameters that truly affect device performance.
Solution Approach 2:
The patent transforms the testing approach from testing all parameters to testing a subset of critical parameters identified through probabilistic analysis. This changes the testing paradigm from exhaustive to targeted, improving productivity while maintaining reliability through the use of probability distributions to identify which parameters matter most.
Data Source
AI summary
Described herein are systems, methods, and other techniques for identifying redundant parameters and reducing parameters for testing a device. A set of test values and limits for a set of parameters are received. A set of simulated test values for the set of parameters are determined based on one or more probabilistic representations for the set of parameters. The one or more probabilistic representations are constructed based on the set of test values. A set of cumulative probabilities of passing for the set of parameters are calculated based on the set of simulated test values and the limits. A reduced set of parameters are determined from the set of parameters based on the set of cumulative probabilities of passing. The reduced set of parameters are deployed for testing the device.