Electronic Component Testing With Learned Stabilized Response Estimation
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Solution Overview
Problem
Existing electronic component testing methods in production lines face challenges in reducing test duration without degrading production quality or increasing infrastructure investment, and there is a risk of late detection of defective components, which leads to productivity losses.
Innovation Solution
A test method optimized by a learning algorithm that reduces wait time between excitation and measurement by using a trained algorithm to estimate a stabilized response value, based on anticipatory response values, while maintaining reliability through iterative adjustments of wait time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the wait time is reduced to shorten test duration, then productivity is improved, but the reliability of test results deteriorates
Solution Approach 1:
The patent applies preliminary action by conducting a learning phase before actual testing, where the system pre-calculates and stores the relationship between wait times and response values for different component types. This preliminary data collection enables the system to determine optimal wait times in advance, allowing faster testing without sacrificing reliability since the wait time-component type relationship is established beforehand.
Solution Approach 2:
The patent uses copying by creating a lookup table that copies and stores the relationship between wait times and response values for various component types during the learning phase. During actual testing, the system retrieves pre-computed information from this table based on the component type, eliminating the need to re-evaluate wait time effects for each test, thus reducing test duration while maintaining reliability.
2Productivity
If sampling is increased to reduce test time, then productivity is improved, but the quality control reliability deteriorates
Solution Approach 1:
The patent applies parameter changes by determining optimal wait time parameters specific to each component type based on the learning phase data. Instead of using a uniform sampling approach, the system adjusts the wait time parameter according to the specific component being tested, allowing for more efficient testing of each component type while maintaining appropriate detection sensitivity for defects.
3Productivity
If parallel testing is implemented to reduce test duration, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical approach of physical parallel testing infrastructure with an algorithmic solution. Instead of adding more physical testing equipment to run tests in parallel, the system uses a learning algorithm that calculates optimal wait times based on component type, achieving faster testing through computational optimization rather than physical parallelization, thus avoiding the complexity of additional test infrastructure.
Data Source
AI summary
An electronic component test method. A component is excited and after a reduced wait time, an anticipatory response of this component is measured. A stabilized response is estimated, corresponding to a response that would have been measured after a nominal wait time, based on the anticipatory response. An acceptance condition is verified, for the estimated stabilized response value, for assessing the quality of the component. The estimation is carried out by a learning algorithm, previously trained by, measuring several response values of each component of a set of reference components, for each wait time, during a progressive decrease of the wait time. Based on the measurements obtained, the reduced wait time is determined, and the learning algorithm is trained to estimate a stabilized response value.