Genetic Algorithm Net Routing for Analog Circuit Design
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Solution Overview
Problem
Existing electronic circuit design systems face challenges in automatically routing all nets at the graphical user interface to meet electrical performance and speed requirements, particularly in optimizing analog and mix-signal layouts.
Innovation Solution
A computer-implemented method and system that applies a genetic algorithm with a two-stage routing analysis, including intra-row and inter-row routing analysis, to optimize net routing, sorting nets based on connection requirements and assigning instTerms to tracks, thereby generating optimized routing for display at a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic routing is performed at the graphical user interface, then routing automation is improved, but electrical performance and speed requirements cannot be met
Solution Approach 1:
The routing process is segmented into multiple stages: pre-processing stage for initial routing, intra-row routing analysis stage for detailed routing within rows, and inter-row routing analysis stage for connections between rows. This segmentation allows each stage to focus on specific aspects of routing, improving overall automation capability while maintaining electrical performance requirements through specialized analysis at each stage.
Solution Approach 2:
The system changes routing parameters dynamically during the genetic algorithm optimization process. It adjusts routing paths, track assignments, and connection parameters based on electrical performance metrics and speed requirements, enabling the system to meet both automation and performance targets by adapting routing parameters to specific design constraints.
2Productivity
If comprehensive net routing is performed, then routing completeness is improved, but design complexity increases
Solution Approach 1:
The routing analysis is divided into intra-row and inter-row components, allowing the system to handle comprehensive routing by breaking down the complex task into manageable segments. Each segment can be processed independently with appropriate algorithms, improving completeness while reducing the complexity burden on any single processing stage.
Solution Approach 2:
The system introduces an intermediary routing representation that bridges the gap between simple routing and complex comprehensive routing. This intermediary structure enables the system to achieve complete net routing by coordinating multiple routing decisions through a unified representation layer, reducing overall design complexity.
3Manufacturing precision
If genetic algorithm optimization is applied, then routing optimization is improved, but computational time increases
Solution Approach 1:
The system performs preliminary routing actions through pre-processing stage and initial genetic algorithm iterations before final optimization. This preliminary action establishes a solid routing foundation that reduces the computational time needed for final optimization, improving routing precision while limiting total computational time through staged approach.
Solution Approach 2:
The genetic algorithm is configured to perform a controlled number of iterations and optimizations, applying partial action that is sufficient to achieve acceptable routing optimization without excessive computational time. The system balances optimization depth with time constraints by stopping when marginal improvements become negligible.
Data Source
AI summary
The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design having one or more unoptimized nets. Embodiments may further include applying a genetic algorithm to the electronic design, wherein the genetic algorithm includes a two stage routing analysis, wherein a first stage analysis is an intra-row routing analysis and a second stage is an inter-row routing analysis. Embodiments may also include generating an optimized routing of the one or more nets and displaying the optimized routing at a graphical user interface.