Integrated Circuit Physical Cell Placement Optimization
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Solution Overview
Problem
Traditional methods for improving integrated circuit manufacturing yield are laborious, time-consuming, and inefficient, as they react to problems after they occur, focusing on detected failures rather than preventing them, and are challenged by the complexity of multi-factorial interactions in design and manufacturing.
Innovation Solution
A method using a physical design graph to partition and optimize integrated circuit designs by selecting and replacing sub-configurations based on target characteristics, allowing for the systematic characterization and optimization of design and manufacturing configurations to improve yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual yield improvement methods are used, then yield can be improved incrementally, but the process is laborious and time-consuming, taking several years to achieve significant improvement
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict yield-limiting patterns and configurations before manufacturing occurs. The system analyzes design data and manufacturing parameters in advance to identify potential issues, allowing preventive measures to be taken during the design phase rather than waiting for problems to manifest in production. This shifts the timing of yield improvement actions from post-manufacturing analysis to pre-manufacturing prediction and optimization.
Solution Approach 2:
The patent replaces the manual, mechanical process of yield improvement with an automated machine learning system. Instead of engineers manually analyzing data, identifying patterns, and implementing changes over years, the system uses algorithms to automatically process design data, predict yield issues, and recommend optimizations. This substitution of manual mechanical analysis with computational automation dramatically reduces the time required for yield improvement while maintaining or enhancing effectiveness.
2Reliability
If traditional manual analysis methods are used to identify yield-limiting patterns, then problems can be detected and addressed, but the complexity of multi-factorial interactions makes the process inefficient and difficult to execute
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the complex multi-factorial design/manufacturing data and the yield optimization goal. These models serve as mediators that automatically process and integrate multiple factors (design variations, process parameters, material properties) to identify yield-limiting patterns. This intermediary system handles the complexity of multi-factorial interactions without requiring manual analysis of each factor combination, making the process efficient despite increasing design and manufacturing complexity.
Solution Approach 2:
The patent applies parameter changes by using machine learning to analyze how variations in design parameters and manufacturing parameters interact to affect yield. The system identifies critical parameter combinations and their interactions that limit yield, then recommends optimized parameter settings. This approach systematically handles multi-factorial complexity by modeling parameter relationships and their combined effects on yield, enabling efficient optimization even as design and manufacturing complexity increases.
3Reliability
If restricted design rules are used to eliminate yield-limiting structures, then some yield issues can be prevented, but additional defects may be discovered after problems occur and the process remains manual and incremental
Solution Approach 1:
The patent implements feedback by using machine learning models that continuously learn from manufacturing data and defect information. The system analyzes actual yield outcomes and defect patterns, then feeds this information back into the model to improve future predictions and recommendations. This closed-loop feedback mechanism enables the system to adapt and improve yield optimization strategies over time, moving beyond static restricted design rules to dynamic, data-driven optimizations that continuously enhance the yield improvement rate.
Solution Approach 2:
The patent replaces the manual, incremental process of implementing restricted design rules with an automated machine learning system. Instead of engineers manually identifying and eliminating yield-limiting structures in an incremental fashion, the system automatically analyzes design data, predicts yield issues, and recommends comprehensive optimizations. This substitution accelerates the yield improvement rate by processing and acting on multiple factors simultaneously rather than through manual iterative steps.
Data Source
AI summary
In an embodiment, a method for optimizing an integrated circuit physical design for an integrated circuit. A physical design graph includes a plurality of physical design sub-configurations, each including a placement of a group of physical cells and having annotated characteristics. The method includes identifying, in the integrated circuit physical design, a first physical design sub-configuration including a first placement of a first group of the physical cells and having first annotated characteristics, the first annotated characteristics being outside target characteristics. The method includes selecting from the physical design graph, based on the first group of the physical cells and the target characteristics, at least a second physical design sub-configuration including a second placement of the first group of the physical cells and being within the target characteristics. The method includes replacing the first physical design sub-configuration in the integrated circuit physical design with the second physical design sub-configuration.