ATE Fault Isolation Using Truth Table Failure Matrix
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Solution Overview
Problem
Existing Automated Test Equipment (ATE) systems are inefficient in identifying and isolating all probable causes of failure (PCOF) within a unit under test, as they typically only detect the first occurring fault, requiring repeated test program executions to identify multiple faults, which is time-consuming and tedious.
Innovation Solution
A default isolation matrix-based system and method that applies failure isolation matrices to test and test group outcomes, enabling the detection of all faults and isolating each fault down to its probable cause by using a truth table-based approach, applicable to sequential, parallel, and time-coherent tests, allowing for user-configured criteria and improved fault detection and isolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential fault isolation methods are used, then the first occurring fault can be identified, but multiple faults require repeated test program executions which is time-consuming
Solution Approach 1:
The patent pre-computes and stores all possible fault isolation outcomes in a lookup table before test execution. During testing, the system collects test results and directly queries the pre-computed lookup table to determine the probable cause of failure, eliminating the need for repeated sequential test executions to isolate multiple faults.
Solution Approach 2:
The patent transitions from sequential one-fault-at-a-time isolation to parallel multi-fault isolation by introducing a multi-dimensional fault isolation matrix that simultaneously evaluates multiple potential faults across different test groups and tests, enabling comprehensive fault identification in a single test execution.
2Reliability
If comprehensive fault detection is implemented to identify all faults, then fault isolation accuracy improves, but test program complexity increases
Solution Approach 1:
The patent divides the fault isolation process into distinct segments: test execution, result collection, and lookup table querying. Each segment handles specific tasks independently, allowing the system to manage complex multi-fault detection scenarios through modular, organized processing steps rather than a monolithic complex test program.
Solution Approach 2:
The patent introduces a lookup table as an intermediary data structure that stores pre-computed fault isolation logic. This intermediary layer separates the complexity of fault analysis from the test execution process, allowing the test program to remain relatively simple while still achieving comprehensive fault detection through the intermediary lookup mechanism.
3Measurement precision
If multiple criteria are used for outcome definition, then fault isolation precision improves, but criteria creation complexity increases
Solution Approach 1:
The patent uses template-based criteria definitions where common fault isolation patterns are pre-defined and stored as templates. Users can create complex multi-criteria fault isolation rules by copying and adapting these templates rather than building criteria from scratch, reducing the complexity of creating precise fault isolation criteria while maintaining high accuracy.
Data Source
AI summary
The invention is a truth table-based failure isolation matrix in an Automated Test Equipment (ATE) environment to enable the mapping of measured results and/or test outcomes to probable causes of failure (PCOF). The failure isolation matrix is hierarchical in that it applies to both individual tests as well as groups of tests. For each level in the hierarchy, columns represent individual outcomes verified against expected results to produce a resultant outcome and rows represent possible combinations of measurement outcomes or outcome vectors. For groups of tests, the measurement results represent the combined results of the individual tests within the test group. The resultant matrix is a pattern of vectors that can be pattern matched against prior history of result patterns to further improve the accuracy and narrow the scope of PCOF.