Predicting DRC Violations in Placement Layouts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electronic circuit design processes face challenges in predicting and preventing design rule check (DRC) violations due to routing congestion, which hinders the minimization of chip size and increases design implementation time.
Innovation Solution
A system utilizing machine learning techniques to predict DRC violations before routing by comparing placement layouts with trained models based on past data, allowing for adjustments to the layout to avoid congestion and optimize routability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If routing is performed on a placement layout, then interconnections are formed to connect circuit elements, but routing congestion occurs in certain regions resulting in DRC violations
Solution Approach 1:
The system performs preliminary DRC violation prediction by analyzing placement layouts before routing is executed. Machine learning models trained on historical routing data identify regions likely to experience routing congestion and generate DRC violations. This early detection allows designers to modify placement layouts proactively, adjusting cell positions or adding spacing to prevent congestion before routing begins, thereby ensuring DRC compliance while maintaining manufacturability.
2Reliability
If routing congestion is avoided by optimizing placement layout, then DRC violations are reduced, but chip area increases due to additional spacing between cells
Solution Approach 1:
The system applies local quality optimization by identifying specific regions in the placement layout that are prone to routing congestion and DRC violations. Rather than uniformly increasing spacing across the entire chip, the machine learning model pinpoints localized areas requiring adjustment. The placement layout is then optimized locally in these identified regions, making minimal spacing adjustments only where necessary to prevent congestion. This approach maintains overall chip area efficiency while ensuring DRC compliance in critical regions.
3Measurement precision
If machine learning models are trained with extensive past data to improve prediction accuracy, then DRC violation prediction precision increases, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by training machine learning models with extensive historical routing data and DRC violation patterns in advance, before actual placement layout analysis. The models are pre-trained and stored for rapid deployment. When a new placement layout needs analysis, the pre-trained models can quickly evaluate it without requiring real-time computation of the full training dataset. This approach achieves high prediction accuracy through comprehensive training while maintaining fast processing speeds during actual use.
Data Source
AI summary
Systems and methods are provided for predicting systematic design rule check (DRC) violations in a placement layout before routing is performed on the placement layout. A systematic DRC violation prediction system includes DRC violation prediction circuitry. The DRC violation prediction circuitry receives placement data associated with a placement layout. The DRC violation prediction circuitry inspects the placement data associated with the placement layout, and the placement data may include data associated with a plurality of regions of the placement layout, which may be inspected on a region-by-region basis. The DRC violation prediction circuitry predicts whether one or more systematic DRC violations would be present in the placement layout due to a subsequent routing of the placement layout.