Machine Learning SIR Drop Prediction for CTS Layouts
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Solution Overview
Problem
Current semiconductor design processes fail to predict and prevent static voltage drop (SIR drop) violations in clock tree synthesis (CTS) layouts before routing, leading to inefficiencies and increased costs due to the need for post-routing analysis and potential device defects.
Innovation Solution
Implementing a machine learning-based system that predicts SIR drop violations by comparing CTS layouts with trained models using past data, allowing for adjustments to the layout, such as increased spacing between clock cells, to prevent violations before routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SIR drop analysis is performed after routing, then accurate detection of SIR drop violations is achieved, but design turnaround time is increased and manufacturing efficiency is reduced
Solution Approach 1:
The patent performs SIR drop analysis at the CTS layout stage, before routing is executed. By predicting potential SIR drop violations in advance using the CTS layout as input, the system identifies problematic regions early in the design flow, allowing corrections to be made before the routing process commits resources. This preliminary detection eliminates the need to wait until after routing to identify SIR drop issues, thereby reducing design turnaround time while maintaining detection accuracy.
2Measurement precision
If SIR drop analysis is performed after routing, then complete wiring information is available for accurate analysis, but manufacturing cost increases due to potential defects and rework
Solution Approach 1:
The system performs SIR drop prediction before routing by analyzing the CTS layout structure and estimating where SIR drop violations are likely to occur. This early prediction allows designers to modify the CTS layout proactively, such as adjusting clock cell spacing or repositioning elements, to prevent violations before routing. By catching issues at this stage, the patent avoids costly post-routing rework, defect remediation, and manufacturing failures, thereby reducing overall manufacturing cost while maintaining sufficient analysis accuracy for preventive action.
3Reliability
If CTS layout is modified to prevent predicted SIR drop violations, then yield is improved, but additional design iterations are required increasing complexity
Solution Approach 1:
The patent implements an iterative feedback loop where the SIR drop prediction results are fed back to the CTS layout design. The prediction system identifies regions at risk of SIR drop violations, and this information feeds back into modifying the CTS layout to mitigate those risks. The process can be repeated with updated layouts until predictions indicate acceptable SIR drop margins. This structured feedback mechanism systematically improves yield by continuously refining the design based on prediction outcomes, while the automation of the process helps manage design complexity through clear iteration cycles.
Data Source
AI summary
A violation prediction system includes machine learning circuitry trained based on past data to predict the presence of violations in electronic device designs after routing has been performed. The machine learning circuitry configured to predict, based on the past data and a pre-routing layout of an electronic device design, whether one or more violations would be present in in the electronic device design due to routing of the layout.