ECO Cell Clustering for IC Placement Feasibility Analysis
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Solution Overview
Problem
Integrated circuit designers face challenges in efficiently implementing engineering change orders (ECOs) due to the complexity of placing and routing additional circuitry in already laid out and routed designs, often resulting in suboptimal results and resource-intensive manual management, especially when ECO cells are densely packed or located in crowded areas.
Innovation Solution
A method involving two-phase clustering of ECO cells to assess feasibility through ECO Placement Impact (EPI) and Routing Impact (ERI) indices, allowing for early evaluation of placement and routing feasibility before actual implementation, enabling designers to prioritize or modify ECOs and reduce resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated placement and routing solutions are used for ECO cells, then productivity is improved, but manufacturing precision deteriorates due to mediocre results in densely packed areas
Solution Approach 1:
The patent segments ECO cells into clusters based on spatial proximity and connectivity relationships. By dividing the ECO implementation into cluster-level feasibility analysis and individual cell placement, the system achieves both automated processing efficiency and precise placement control within each cluster context
Solution Approach 2:
The patent performs preliminary feasibility analysis by calculating EPI and ERI indices for each ECO cluster before actual placement and routing. This advance assessment identifies potential conflicts and constraints, allowing the system to prepare appropriate strategies and achieve better precision while maintaining automated productivity
2Manufacturing precision
If manual management of ECO cells is performed, then manufacturing precision is improved, but productivity deteriorates due to resource-intensive processes
Solution Approach 1:
The patent implements feedback mechanisms by calculating EPI (ECO Placement Impact) and ERI (ECO Routing Impact) indices that provide quantitative assessment of placement and routing feasibility. This feedback guides automated decision-making, enabling the system to achieve manual-quality precision through algorithmic evaluation of cluster density, available space, and routing constraints
Solution Approach 2:
The system performs self-service by automatically assessing ECO cluster feasibility and making placement decisions based on calculated indices. The automated evaluation of EPI and ERI metrics enables the system to manage its own placement and routing processes without manual intervention, maintaining high precision while improving productivity
3Adaptability or versatility
If ECO cells are placed in densely packed areas, then adaptability is improved by utilizing available space, but device complexity increases making legalization difficult
Solution Approach 1:
The patent applies local quality analysis by evaluating EPI indices for each specific ECO cluster based on local characteristics such as cluster density, available space, and proximity to existing cells. This localized assessment allows the system to adapt placement strategies to specific area conditions, achieving good space utilization while managing complexity through context-aware decision-making
4Manufacturing precision
If feasibility analysis is performed for all ECO cells, then manufacturing precision is improved by identifying implementation difficulties, but productivity deteriorates due to increased processing time
Solution Approach 1:
The patent segments the feasibility analysis by grouping ECO cells into clusters and performing EPI and ERI index calculations at the cluster level rather than individually for each cell. This segmentation reduces the total number of analyses required, maintaining precise feasibility assessment while significantly improving processing efficiency
Solution Approach 2:
The patent performs partial feasibility analysis by focusing EPI and ERI assessments on ECO clusters that are most critical or problematic based on preliminary evaluation. This selective approach provides sufficient precision for decision-making without the excessive processing time of analyzing every single ECO cell in detail
Data Source
AI summary
An existing design of an integrated circuit includes existing cells that have already been placed and routed. An engineering change order (ECO) specifies additional new cells (ECO cells) to be inserted into the existing design. The ECO cells are also associated with target locations for their placement among the existing cells, but these target locations may violate design rules. The feasibility of “legalizing” the placement of the ECO cells within the existing design is assessed as follows. The ECO cells are clustered into clusters based on their target locations. Clusters are assessed by determining an ECO placement impact (EPI) index for individual clusters. The EPI index is a measure of the feasibility for legalizing the placement of the ECO cells in that cluster.