Dynamic Intelligent Test System for Minimum Power Consumption
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Solution Overview
Problem
Current testing methods for determining minimum power consumption values in product design are time-consuming, significantly impacting test capacity and efficiency.
Innovation Solution
A dynamic intelligent test system utilizing a data distribution model with AI convolution deep learning to predict test ranges, reducing the scope of testing and employing algorithms like binary search to quickly identify minimum power consumption values, which are then updated dynamically based on new test data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive testing is performed across all frequency and voltage ranges to ensure accurate minimum power consumption values, then measurement precision is improved, but testing time increases significantly
Solution Approach 1:
The system performs preliminary actions by establishing a data distribution model before actual testing begins. Historical test data is used to pre-determine test ranges and identify critical areas, allowing the testing process to focus only on relevant parameters rather than exhaustively testing all possibilities.
Solution Approach 2:
A data distribution model acts as an intermediary between historical test data and current testing requirements. This model processes and analyzes historical data to generate optimized test ranges, serving as a bridge that reduces the search space for minimum power consumption values without sacrificing accuracy.
2Reliability
If testing is performed across wide frequency and voltage ranges to ensure comprehensive coverage, then reliability is improved, but productivity decreases
Solution Approach 1:
The system dynamically adjusts test parameters (frequency and voltage ranges) based on the data distribution model. Instead of using fixed wide ranges, the system adapts the test parameters to match the actual distribution characteristics of the device under test, achieving both comprehensive coverage and efficient testing.
Solution Approach 2:
The testing system becomes dynamic by continuously updating the data distribution model with new test results. This allows the test ranges to evolve and adapt over time, improving reliability through better data accumulation while reducing testing time through learned optimizations.
3Productivity
If multiple test projects are added to the testing station to increase test capacity, then productivity is improved, but device complexity increases
Solution Approach 1:
The data distribution model serves multiple functions: it analyzes historical data, determines test ranges, identifies critical parameters, and optimizes test sequences. This multi-functional approach allows a single testing station to handle multiple test projects efficiently without requiring separate specialized equipment for each test type.
Solution Approach 2:
The system implements feedback mechanisms where test results are fed back into the data distribution model for continuous refinement. This feedback loop allows the system to learn from previous tests and automatically optimize future testing, reducing the need for manual configuration and decreasing operational complexity.
Data Source
AI summary
A method for dynamic intelligent testing of a target, to be tested according to projects, includes calling up a data distribution model of a project in response to a target being tested by the project, and obtaining a test range corresponding to the project based on the data distribution model. The method further includes obtaining a test value when the target is at a minimum power consumption value by testing the target based on the test range, and updating the data distribution model and the test range of the project based on the test value.