Design Rule Error Categorization via Parameter Similarity
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Solution Overview
Problem
Current design rule checking (DRC) processes in integrated circuit design generate a large number of violations, making it difficult to categorize and visualize errors effectively, as they do not dynamically determine categories based on similarity in parameters, leading to overwhelming amounts of data and inefficient error identification.
Innovation Solution
A method that categorizes design rule violations by comparing parameters associated with each violation, dynamically defining categories based on similarity, and displaying a visual representation of errors, allowing for the determination of local regions and classification of error types within the IC layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional design rule checking processes are used to check IC layouts, then all design rule violations are detected, but the large number of violations makes it difficult to categorize and visualize errors effectively
Solution Approach 1:
The patent segments the large set of design rule violations into distinct categories based on similarity of parameters such as violation type, location, and characteristics. This segmentation transforms the overwhelming list of individual violations into manageable groups, making it easier to analyze and correct errors while maintaining complete detection of all violations.
Solution Approach 2:
The patent changes the parameters used for error representation by aggregating violations based on their parameter similarities (violation type, location, characteristics). Instead of presenting each violation individually with all its parameters, the system transforms the data into categorical groups, reducing complexity while preserving essential information for error identification and correction.
2Loss of information
If all design rule violations are listed individually, then complete error information is provided, but the overwhelming amount of data makes error identification inefficient
Solution Approach 1:
The patent merges individual design rule violations that share similar parameters into consolidated error categories. By combining violations with identical or similar characteristics (type, location, parameters), the system reduces the total number of items to review while maintaining complete error information within each category representation, thereby significantly reducing error identification time.
Solution Approach 2:
The patent transforms the presentation parameters of error data by grouping violations based on parameter similarity rather than listing them individually. This parameter transformation consolidates redundant information while preserving complete error details within each category, enabling faster error identification without information loss.
3Productivity
If dynamic categorization based on parameter similarity is implemented, then error identification efficiency is improved, but additional processing complexity is introduced
Solution Approach 1:
The patent implements dynamic categorization by changing the parameters used for error grouping - specifically, it groups violations based on similarity of their parameters (violation type, location, characteristics) rather than using fixed categories. This parameter-based dynamic grouping improves error identification efficiency by presenting errors in meaningful categories, while the automated nature of the parameter comparison keeps processing complexity manageable.
Solution Approach 2:
The patent creates simplified representations (copies) of violation groups by extracting common parameters and characteristics from multiple similar violations. Instead of processing each violation individually, the system creates consolidated error category representations that capture the essential information, reducing processing complexity while maintaining error identification efficiency.
Data Source
AI summary
Embodiments of the invention include a method for categorizing and displaying design rule errors. The method may include receiving, from a design rule checker, more than one violation of a design rule within a design layout. The method may also include determining distinct categories of the design rule violations by comparing parameters associated with the design rule violations.