Automated Root-Cause Analysis for Static Verification Violations

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Solution Overview

Problem

Conventional static verification tools are inefficient and costly due to the manual and error-prone process of analyzing thousands or millions of messages or violations, making it difficult to identify root causes in circuit designs, especially when dealing with complex digital logic gates and auxiliary data.

Innovation Solution

The implementation of machine learning (ML) technology to automatically group and enrich violations, using an overlay mechanism and projection matrices to generate clone violations, allowing for automatic root-cause detection and visualization in a graphical user interface, thereby improving debugging efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual analysis of violation reports is performed, then domain knowledge and design understanding can be applied, but the process is slow and error-prone

Engineering Contradiction:

Improveaccuracy of root-cause identificationVSAvoidtime required for analysis

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically analyzing violation reports, grouping violations, and identifying root causes without requiring manual human intervention. The automated root-cause analysis system processes violation data, applies clustering algorithms, and generates diagnostic information autonomously, eliminating the slow and error-prone manual analysis process while maintaining high accuracy through systematic computational methods.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human designers manually examining violation reports and applying domain knowledge, the system uses computer processors to execute automated algorithms for violation grouping, clustering, and root-cause identification, substituting human cognitive work with automated computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If manual sorting and grouping of violations is performed, then some organization is achieved, but unrelated violations may be grouped together causing added difficulty

Engineering Contradiction:

Improveorganization of violation dataVSAvoidaccuracy of violation grouping

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The automated system uses feedback mechanisms through clustering algorithms that continuously evaluate violation characteristics and adjust grouping decisions. The system analyzes violation data, applies clustering criteria based on debug fields and violation properties, and refines groupings to ensure related violations are correctly associated while separating unrelated ones, achieving both organization and accuracy simultaneously.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes parameters by using multiple debug fields and violation characteristics as clustering criteria. Instead of simple manual sorting, the automated system evaluates multiple parameters including violation type, location, and associated debug fields to dynamically determine appropriate groupings, ensuring that violations are organized accurately according to their actual relationships.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If conventional static verification tools are used, then violation detection is performed, but root-cause analysis is inefficient and costly

Engineering Contradiction:

Improvecompleteness of violation informationVSAvoidefficiency of root-cause analysis

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The system segments the complex root-cause analysis process into distinct automated components: violation data reception, debug field extraction, violation clustering, and root-cause identification. By dividing the analysis into manageable automated segments, the system maintains complete violation information while dramatically improving processing efficiency and reducing costs compared to conventional manual approaches.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated root-cause analysis system acts as an intermediary between violation detection and final diagnostic conclusions. It receives raw violation data, processes it through clustering algorithms, and generates organized diagnostic information, serving as an efficient intermediate processing layer that preserves complete information while enhancing productivity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11288427B2Automated root-cause analysis, visualization, and debugging of static verification results

Publication Date: 2022.03.29 SYNOPSYS INC

AI summary

Disclosed herein are system, method, and computer-readable storage device embodiments for implementing automated root-cause analysis for static verification. An embodiment includes a system with memory and processor(s) configured to receive a report comprising violations and debug fields, and accept a selection of a seed debug field from among the plurality of debug fields. Clone violations may be generated by calculating an overlay of a given violation of the violations and a seed debug field, yielding possible values for a subset of debug fields. A clone violation may be created for a combination of the at least two second debug fields, populating a projection matrix, which may be used to map violations and clone violations to corresponding numerical values in the projection matrix and determine a violation cluster based on the mapping having corresponding numerical values and score(s) satisfying a threshold, via ML. Clustering may further be used to generate visualizations.