Test Program Complexity Quantification for ATE Effort Prediction
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Solution Overview
Problem
Automated test equipment (ATE) lacks an effective method to determine the complexity of test programs, which affects the effort required to develop and implement future test programs, leading to inefficiencies in engineering time and resource allocation.
Innovation Solution
A method involving identifying parameters associated with a test program, assigning weights to these parameters, and generating a numerical value indicative of complexity using equations, allowing for the prediction of effort needed to develop future test programs through graphical analysis and empirical calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If no complexity determination method is implemented, then ATE can execute test programs, but engineering time and resource allocation become inefficient
Solution Approach 1:
The patent transforms the abstract concept of test program complexity into quantifiable parameters by identifying specific attributes (number of test sites, signal types, measurement functions) and assigning numerical weights to them. This parameterization enables objective comparison and prediction of engineering effort across different test programs, directly resolving the inefficiency in resource allocation.
Solution Approach 2:
The patent introduces an intermediary complexity determination system that acts as a mediator between the test program specifications and the resource allocation decisions. This intermediary calculates a numerical complexity value based on weighted parameters, which then serves as the basis for predicting engineering time and allocating resources, eliminating the need for subjective estimation.
2Measurement precision
If complexity parameters and weighting equations are implemented, then engineering effort can be predicted accurately, but the system requires complex calculations and data processing
Solution Approach 1:
The patent segments the complexity determination process into distinct, manageable components: identifying test program parameters, assigning weights to each parameter, calculating weighted values, and summing them to produce a final complexity score. This segmentation makes the overall system more tractable and easier to implement despite the multiple steps involved.
Solution Approach 2:
The patent replaces subjective human judgment about engineering effort with an automated computational system. Instead of relying on engineers' intuition and experience to estimate complexity, the system uses mathematical equations and weighted parameter calculations to objectively determine complexity, substituting mechanical computation for human cognitive processes.
Data Source
AI summary
An example includes the following operations: identifying parameters associated with a test program, where the parameters are based on at least one of a device under test (DUT) to be tested by the test program or a type of test to be performed on the DUT by the test program; assigning weights to the parameters; generating a numerical value for the test program based on the parameters, the weights, and equations that are based on the parameters and the weights, where the numerical value is indicative of a complexity of the test program; and using the numerical value to obtain information about effort needed to develop future test programs.