AI-Optimized Test Program for Cross-Platform DUT Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Test programs developed for one test system often produce inconsistent results when run on different test systems, leading to uncorrelated test results, which can affect repeatability, test time, compliance, and safety of devices under test (DUTs).
Innovation Solution
The implementation of an optimization process using artificial intelligence (AI) and machine learning to vary parameters such as voltage, current, and timing in test signals, employing cost functions and genetic algorithms to generate a test program that optimizes criteria like correlation, repeatability, and compliance across different test platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test programs are developed for one test system, then the test program can be executed on that specific system, but the test results are not correlated when run on different test systems
Solution Approach 1:
The patent applies parameter changes by systematically varying test parameters (voltage, current, timing, temperature) across different test platforms to identify and adjust parameters that cause result divergence. The optimization process modifies these parameters to achieve consistent test results across multiple platforms while maintaining each platform's operational characteristics.
Solution Approach 2:
The patent implements dynamics by using an iterative optimization process that dynamically adjusts test parameters based on observed result correlations. The system continuously refines parameter settings across different test platforms, adapting the test program to achieve consistent results rather than using static, fixed parameters.
2Reliability
If traditional test programs are used without optimization, then the test implementation is simple, but the repeatability of test results across different platforms is poor
Solution Approach 1:
The patent applies self-service by implementing an automated optimization process that self-adjusts test parameters without extensive manual intervention. The system automatically collects test results, analyzes correlations, identifies divergent parameters, and refines the test program to achieve consistent results across platforms, reducing the need for manual tuning and expertise.
Solution Approach 2:
The patent implements feedback through an iterative optimization loop where test results from multiple platforms are continuously monitored and fed back into the parameter adjustment process. The system uses this feedback to identify which parameters need modification to improve result correlation, creating a closed-loop optimization system that progressively improves repeatability.
3Reliability
If multiple parameters are manually adjusted to improve test correlation, then test result consistency may improve, but the time required to develop and optimize the test program increases significantly
Solution Approach 1:
The patent replaces manual mechanical adjustment of test parameters with an automated computational optimization system. Instead of manually tweaking voltage, current, timing, and temperature parameters, the system uses automated algorithms to analyze test results and determine optimal parameter settings, dramatically reducing the time required for test program development while improving consistency.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between different test platforms and the final test program. This intermediary automatically processes test results from multiple platforms, identifies parameter adjustments needed for consistency, and generates optimized test configurations, eliminating the need for time-consuming manual parameter tuning.
Data Source
AI summary
An example method includes the following operations: receiving information about tests performed on a device, where the tests are associated with one or more parameters; performing an optimization process that includes varying the one or more parameters to optimize one or more criteria associated with the tests, where the optimization process includes an artificial intelligence process or a machine learning process; and outputting information that is based on which of the one or more parameters optimizes the one or more criteria.