Analog Circuit Placement Using Genetic Row-Based Optimization
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Solution Overview
Problem
Existing electronic circuit design systems face challenges in automatically placing analog devices at graphical user interfaces to meet snapping, row style, and grouping requirements, leading to inefficient optimization due to overlapping placements and difficulties in auto-placement based on reference placement.
Innovation Solution
A computer-implemented method and system using genetic algorithms to analyze and determine the location and orientation of analog and mix-signal circuit components, employing a row-based data structure and cost functions to optimize placement, including a genetic code refinement process for efficient placement without overlapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If each possible instance's X and Y coordinates are formulated as optimization variables, then the placement flexibility is improved, but the optimization efficiency deteriorates due to overlapping placements
Solution Approach 1:
The patent segments the placement problem into discrete row assignments and positional offsets rather than treating all coordinates as continuous optimization variables. Instances are first assigned to specific rows, then positioned within those rows, reducing the search space and avoiding overlaps by constraining the optimization to non-overlapping row-based regions.
Solution Approach 2:
The patent introduces a row dimension as an intermediate level of organization between individual instances and the final placement coordinates. By assigning instances to rows first and then positioning them within rows, the system transforms a two-dimensional coordinate optimization problem into a hierarchical structure that naturally prevents overlaps while maintaining placement flexibility.
2Productivity
If automatic placement is performed without row-based constraints, then the placement speed is improved, but the adherence to row style requirements deteriorates
Solution Approach 1:
The patent performs preliminary row assignment for each instance before final positioning. By determining which row each instance belongs to in advance of the detailed placement optimization, the system ensures row style requirements are satisfied while maintaining efficient automated placement through subsequent position optimization within the predefined row structure.
Solution Approach 2:
The patent applies different optimization strategies to different aspects of placement: row assignment ensures global structural compliance with row style requirements, while local position optimization within rows achieves efficient packing. This localized approach to different placement aspects maintains both speed and precision requirements.
3Manufacturing precision
If grouping requirements are enforced during placement, then the design requirement compliance is improved, but the placement complexity increases
Solution Approach 1:
The patent merges the grouping requirement satisfaction with the row assignment process. Instances that need to be grouped together are assigned to the same row, combining two constraints (grouping and row styling) into a single assignment decision. This reduces placement complexity by eliminating the need for separate grouping enforcement steps.
Data Source
AI summary
The present disclosure relates to a computer-implemented method for genetic placement of analog and mix-signal circuit components. Embodiments may include receiving an unplaced layout associated with an electronic circuit design and grouping requirements. Embodiments may also include identifying one or more instances that need to be placed in the unplaced layout and areas of the unplaced layout configured to receive the instances. Embodiments may further include analyzing one or more instances that need to be placed in the unplaced layout and the areas of the unplaced layout configured to receive the instances, wherein analyzing is based upon a row-based data structure. Embodiments may also include determining a location and an orientation for each of the one or more instances based upon the genetic algorithm and generating a placed layout based upon the determined location and orientation for each of the instances.


