Analog Circuit Defect Detectability Analysis for NC Defect Coverage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing analog circuit defect simulation tools are nascent and laborious, leading to inefficient and inaccurate defect coverage estimation, which results in high product returns and manufacturing yield underestimation in mixed-signal integrated circuits.
Innovation Solution
A system and method for defect detectability analysis that includes static and dynamic stimuli, clustering of not-covered defects, and weighting to prioritize significant defects, enabling efficient defect coverage maximization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional analog circuit defect simulation tools are used, then defect coverage can be estimated, but the process becomes laborious and inefficient
Solution Approach 1:
The patent segments the defect analysis process into distinct operations: stimuli detection analysis (first operation) and user defect analysis (second operation). This segmentation allows parallel processing and automated workflow management, significantly improving efficiency while reducing manual intervention time.
Solution Approach 2:
The patent performs preliminary stimuli detection analysis before user defect analysis to pre-identify detectable defects. This preliminary action creates a foundation of known defects that reduces the workload in subsequent user analysis phases, improving overall productivity.
2Reliability
If comprehensive defect analysis is performed on all defects, then complete coverage is achieved, but the process becomes overly complex and difficult to manage
Solution Approach 1:
The patent divides the defect universe into two distinct segments: detectable defects (identified through stimuli detection analysis) and undetectable/not-covered defects. This segmentation simplifies management by allowing different analysis strategies for each segment while maintaining overall completeness.
Solution Approach 2:
The patent introduces an intermediary comparison operation that systematically compares results from the first operation (stimuli detection) with the second operation (user defect analysis). This intermediary process automatically identifies NC defects without requiring manual cross-referencing, reducing complexity while ensuring completeness.
3Measurement precision
If all identified defects are analyzed in detail, then maximum detectability is achieved, but the analysis becomes too time-consuming and resource-intensive
Solution Approach 1:
The patent extracts and isolates only the NC defects (not-covered defects) that require further analysis by separating them from the already-detected defects. This extraction focuses resources on the critical minority of defects that need detailed attention, maintaining precision while improving throughput.
Solution Approach 2:
The patent applies different levels of analysis quality to different defect categories: automated stimuli detection analysis for broad coverage and detailed user defect analysis only for NC defects. This local quality approach ensures high precision where needed while maintaining high productivity overall.
4Reliability
If defect analysis focuses on all defects equally, then comprehensive coverage is achieved, but critical defects may be lost among numerous minor defects
Solution Approach 1:
The patent extracts NC defects as a distinct category requiring prioritization attention. By taking out these defects from the general defect pool, the system makes it easier to focus on critical issues without being overwhelmed by the volume of all defects.
Solution Approach 2:
The patent changes the parameter of defect importance by identifying and flagging NC defects with higher significance. This parameter change enables operators to prioritize analysis based on defect detectability status rather than treating all defects equally, improving ease of operation while maintaining comprehensiveness.
Data Source
AI summary
A system and method are provided for detectability analysis to identify defective analog components of a circuit. The method includes applying detectability analysis stimuli to the circuit for the purpose of identifying all detectable defects within a defect universe of the circuit, the defect universe including all actual, and potentially undetectable, defects in the circuit, and the applying resulting in an identification of first defects, which is a subset of all of the actual defects within the defect universe. The method further includes applying a user defect analysis to the circuit to identify second defects, which is a subset of all of the actual defects within the defect universe, determining defects, from the first defects, that are not included in the second defects to be not-covered (NC) defects, grouping the NC defects into clusters, and providing the grouped NC defects as a result of the defect detectability analysis.