Regression Test Modules for Process Design Kit Change Detection
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Solution Overview
Problem
Current methods for detecting changes in Process Design Kits (PDKs) are inefficient, relying on human visual inspection or brute force techniques that are time-consuming and prone to errors, failing to accurately identify latent or undesired changes.
Innovation Solution
An automated and intelligent regression testing method is employed, using feature-specific modules to generate and compare sets of information before and after software revisions, filtering out irrelevant results to detect and report changes effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection is used to detect changes in PDKs, then human intelligence can identify relevant changes, but the process is time-consuming and prone to error
Solution Approach 1:
The patent segments the PDK comparison process into multiple independent test modules, each responsible for specific features or components. This segmentation enables parallel processing of different PDK aspects, significantly reducing the time required for comprehensive change detection while maintaining accurate comparison through specialized modules.
Solution Approach 2:
The patent introduces an intermediary automated testing system that acts as a bridge between human designers and PDK revisions. This intermediary performs intelligent change detection using predefined test cases and comparison algorithms, eliminating the need for time-consuming manual visual inspection while preserving accurate change identification.
2Extent of automation
If brute force techniques are used to detect changes in PDKs, then the process can be fully automated, but irrelevant output camouflages pertinent changes
Solution Approach 1:
The patent extracts and isolates only the pertinent changes from the PDK comparison by using targeted test modules that focus on specific features. Rather than processing all PDK data indiscriminately, the system extracts only the relevant information needed for change detection, eliminating irrelevant output that would otherwise camouflage important changes.
Solution Approach 2:
The patent applies local quality by assigning different levels of analysis to different PDK components based on their importance and change likelihood. Critical features receive more rigorous testing and comparison, while less critical areas use streamlined approaches. This selective quality distribution reduces noise in the output while maintaining comprehensive automation.
3Reliability
If comprehensive testing is performed on PDK revisions, then change detection accuracy is improved, but the complexity and resource requirements increase
Solution Approach 1:
The patent divides the comprehensive testing process into modular test cases organized by PDK features and revision types. This segmentation allows the system to perform thorough testing of each component independently while managing overall complexity through modular architecture. The segmented approach enables reliable verification without requiring an monolithic complex testing system.
Solution Approach 2:
The patent implements a tiered testing strategy where not all PDK revisions require the full suite of comprehensive tests. Based on the type and scope of revisions, the system selectively applies appropriate test levels - using partial testing for minor changes and excessive (comprehensive) testing only when necessary. This approach maintains high reliability for critical revisions while reducing unnecessary complexity for routine updates.
Data Source
AI summary
A method for detecting and reporting changes in functional features of a simulation model caused by a software revision is disclosed. In one aspect, the method is independent of simulation model architecture. One performs regression testing with a plurality of feature-specific modules. The feature-specific modules are configured to generate a first set of information with the simulation model and compare the first set of information to a second set of corresponding information from the simulation model. In the above-described testing, the first set of information postdates the software revision and the second set of information predates the software revision.