AI Floorplanning for PCB Layout Optimization
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Solution Overview
Problem
Manual placement of components on printed circuit boards (PCBs) is time-consuming and often results in suboptimal surface area usage, as it lacks efficiency in determining the optimal layout and orientation of components.
Innovation Solution
The implementation of AI-based floorplanning technology that uses Bayesian Optimization and simulated annealing to determine the aspect ratios and optimal layout of functional blocks on a PCB, ensuring minimal board area usage by employing a B*-Tree representation and random perturbation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual placement of components is used, then flexibility and control in layout design are improved, but time consumption and surface area efficiency deteriorate
Solution Approach 1:
The patent replaces the manual mechanical process of component placement with an automated AI-based system using Bayesian optimization and simulated annealing algorithms. The system automatically determines optimal component layouts, aspect ratios, and orientations by evaluating multiple floorplan candidates, thereby eliminating time-consuming manual operations while maintaining design flexibility through programmable optimization criteria.
Solution Approach 2:
The floorplanning system performs self-service by automatically generating and evaluating multiple layout configurations without human intervention. The AI algorithms independently explore the design space, assess layout quality based on predefined objectives (such as minimizing board area or optimizing signal integrity), and select optimal solutions, enabling the system to serve its own design needs efficiently.
2Ease of operation
If manual placement of components is used, then design control is improved, but surface area utilization efficiency deteriorates
Solution Approach 1:
The patent systematically varies critical design parameters including component aspect ratios, orientations, and positional coordinates to explore the design space. The AI algorithms adjust these parameters iteratively, evaluating how changes affect overall board area utilization. By programmatically modifying parameters and assessing their impact, the system achieves superior space efficiency compared to manual placement while maintaining design control through constrained optimization.
Solution Approach 2:
The floorplanning approach transitions from static manual placement to dynamic automated optimization. The system continuously evaluates and adjusts layout configurations based on performance metrics, allowing the design to adapt and improve iteratively. This dynamic process enables the system to discover non-obvious space-efficient arrangements that would be difficult to achieve through static manual design.
3Productivity
If AI-based optimization loops are implemented, then floorplanning speed is improved, but computational complexity increases
Solution Approach 1:
The patent divides the complex floorplanning optimization into nested loops with distinct functions: an outer Bayesian optimization loop that manages the overall optimization strategy and an inner simulated annealing loop that handles specific layout adjustments. This segmentation allows each loop to specialize in particular optimization tasks, improving computational efficiency while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces surrogate models as intermediaries between the AI optimization algorithms and the actual floorplanning evaluation. These surrogate models approximate the complex objective functions (such as board area calculation and constraint satisfaction), enabling faster evaluation of candidate solutions without requiring exhaustive computational analysis of each layout configuration.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that identifies a plurality of functional blocks in a circuit, wherein each functional block includes a plurality of components, conducts one or more passes of a first optimization loop to determine candidate aspect ratios for the functional blocks based on size data associated with the components, and conducts, within the one or more passes of the first optimization loop, one or more passes of a second optimization loop to determine candidate floorplan data for the circuit based on the candidate aspect ratios.