Polygon Clustering for IC Design Data Processing
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Solution Overview
Problem
Existing methods for organizing and analyzing polygon data in integrated circuit designs are inefficient, requiring significant processing time and resources due to the large number of components, which leads to inadequate data access and analysis as the complexity and size of layout data grow.
Innovation Solution
The approach involves clustering polygons based on proximity, where a set of polygons forms a cluster if there exists a sequence of polygons with distances less than or equal to a given threshold, allowing for repetitive patterns to be analyzed once and replicated across clusters, reducing the total number of shapes to be processed and optimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each polygon in the IC design is analyzed individually to determine applicability of scattering bars or check DRC rules, then complete coverage of all design elements is achieved, but processing time and system resources increase dramatically
Solution Approach 1:
The patent segments the IC design into clusters of polygons based on spatial proximity. By dividing the complete set of polygons into smaller clusters, the system can process each cluster independently and efficiently, reducing the overall processing time while maintaining complete coverage through systematic traversal of all clusters
Solution Approach 2:
The patent identifies repetitive patterns within clusters and creates representative models of these patterns. Instead of analyzing every polygon individually, the system analyzes the representative pattern once and copies the analysis results to all identical patterns, dramatically reducing processing time while ensuring complete coverage through pattern matching
2Stability of the object's composition
If the IC design data is organized in traditional structures, then data integrity is maintained, but data access and analysis efficiency deteriorate as complexity and size grow
Solution Approach 1:
The patent reorganizes IC design data by segmenting polygons into spatial clusters with defined hierarchical relationships. This segmentation maintains data integrity through structured organization while enabling efficient access by allowing queries to operate on localized clusters rather than traversing the entire design database
Solution Approach 2:
The patent introduces spatial clustering as an additional organizational dimension beyond traditional flat polygon lists. By adding this spatial hierarchy dimension, the system maintains complete design data integrity while enabling efficient access through spatial indexing and cluster-based navigation
3Productivity
If clustering is used to reduce processing workload, then processing efficiency improves, but complexity of data organization increases
Solution Approach 1:
The patent applies segmentation to create clusters of spatially proximate polygons, which simplifies processing by reducing the number of elements that must be analyzed together. The segmentation strategy uses straightforward distance-based criteria that, while adding organizational structure, employ simple and intuitive clustering rules
Data Source
AI summary
Disclosed is an improved approach for organizing, analyzing, and operating upon polygon data which significantly reduces the amount of data required for processing while keeping elements non-interfacing with each other. According to one approach, clusters of elements are extracted which are then handled separately. In some approaches, a set of polygons forms a cluster if for any two polygons from the set of polygons there exists a sequence of polygons from the set such that the distance between any sequential polygons are less than or equal to a given threshold number. Rather than analyzing each and every polygon in the design, repetitive unique patterns are analyzed once, which are then replicated for all clusters which have the same repetitive pattern.