Clustering Critical Cells for DRC Violation Analysis
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Solution Overview
Problem
Automated Place and Route (APR) tools in the semiconductor industry face challenges in identifying and addressing Design Rule Check (DRC) violations efficiently, leading to complex analytical processes and suboptimal routing designs due to large numbers of violations.
Innovation Solution
A method is introduced to detect critical cells by selecting a group of cells with sufficient DRC violations, extracting relevant features such as DRC markers, pin density, and placement density, and using k-means clustering to partition cells into clusters, thereby generating a list of ranked critical cells for layout adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DRC analysis methods are used to identify and address all DRC violations, then comprehensive design rule compliance is achieved, but the analytical process becomes overly complex and time-consuming
Solution Approach 1:
The patent segments the large set of DRC violations into smaller, manageable groups using k-means clustering. Cells are divided into clusters based on their spatial distribution and violation patterns, allowing the analytical process to focus on representative samples from each cluster rather than analyzing every single violation individually. This segmentation reduces complexity while maintaining comprehensive DRC compliance coverage.
Solution Approach 2:
The patent extracts and identifies critical cells that represent the most significant DRC violation patterns within each cluster. By focusing on these extracted critical cells rather than all cells with violations, the method simplifies the analytical process while still addressing the root causes of DRC violations effectively.
2Measurement precision
If all cells with DRC violations are analyzed in detail, then complete identification of routing issues is achieved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary clustering of cells based on their spatial and violation characteristics before detailed analysis. This preliminary action groups similar cells together, allowing the system to identify representative critical cells from each cluster that capture the essential DRC violation patterns. This preliminary grouping significantly reduces the number of cells requiring detailed analysis while maintaining detection accuracy.
Solution Approach 2:
The patent uses critical cells as representatives or proxies for entire clusters of similar cells. By analyzing these representative critical cells, the method infers and addresses DRC violation patterns that apply to the broader cluster, reducing analysis time while maintaining measurement precision through the representative nature of the selected cells.
3Reliability
If comprehensive DRC analysis is performed on all cells, then all routing design issues are identified, but the productivity and routing design efficiency decrease
Solution Approach 1:
The patent segments the comprehensive DRC analysis task into cluster-based analysis, where k-means clustering divides cells into meaningful groups. This segmentation allows the routing design process to address DRC violations in an organized, systematic manner, improving productivity by avoiding redundant analysis of similar cells while maintaining routing design quality through comprehensive cluster coverage.
Solution Approach 2:
The patent applies local quality analysis by focusing detailed examination on critical cells that represent specific local DRC violation patterns within clusters. Rather than uniformly analyzing all cells, the method applies intensive analysis locally to representative cells while using clustering results to infer patterns for other cells, thereby improving routing design efficiency without compromising quality.
Data Source
AI summary
A method includes clustering cells in a group of cells into a selected number of clusters, and ranking the clusters based on a list of prioritized features to generate a list of ranked clusters. The method also includes ranking cells in each of one or more ranked clusters in the list of ranked clusters, based on the list of prioritized features, to generate a list of ranked critical cells. The method further includes outputting the list of ranked critical cells for use in adjusting cell layouts based on the ranked critical cells.