Neural Network Reward for PCB Genetic Optimization
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Solution Overview
Problem
Current PCB design automation lacks automation, relying heavily on manual effort and heuristic methods, which limits quality and time to market.
Innovation Solution
A computer-implemented method using a genetic optimization methodology with a reward function, combined with a neural network trained on placed designs and routability scores, to predict PCB routability and improve design automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional heuristic methods are used for PCB design automation, then the design process can be simplified and easier to implement, but the quality and time to market are limited due to lack of automation
Solution Approach 1:
The patent introduces a neural network as an intermediary component that bridges the gap between traditional heuristic methods and automated PCB design. The neural network learns from training data to predict optimal design decisions, enabling automation without requiring complete redesign of the entire design process. This intermediary layer allows the system to leverage existing heuristic knowledge while adding intelligent automation capabilities.
Solution Approach 2:
The patent transforms the design approach by changing the parameters used for decision-making. Instead of relying solely on traditional heuristic rules, the system uses machine learning models that analyze multiple parameters simultaneously (such as component placement, routing constraints, and design rules) to predict optimal outcomes. This parameter-based approach enables more sophisticated automation while maintaining manageability.
2Reliability
If manual heuristic methods are used for PCB design, then implementation is simpler, but routing completion rate and design quality are insufficient
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance on large datasets of PCB designs and their routing outcomes. The neural network learns optimal placement and routing strategies before actual design work begins. During the actual design process, the pre-trained model can quickly predict and suggest optimal configurations, significantly reducing the time required to achieve high routing completion rates without sacrificing quality.
Solution Approach 2:
The system implements feedback mechanisms where the neural network continuously learns from the results of previous design attempts. By analyzing which placements and routing decisions lead to successful completions versus failures, the model refines its predictions over time. This feedback loop enables the system to improve routing completion rates while progressively optimizing design time through learned patterns.
3Productivity
If more automated algorithms are implemented, then productivity increases, but the complexity of the design system increases
Solution Approach 1:
The patent segments the PCB design automation system into distinct functional modules: a neural network for high-level decision-making, traditional heuristic algorithms for specific sub-tasks, and existing EDA tool integrations. Each component handles a specific aspect of the design process, allowing the system to achieve high productivity through coordinated specialization rather than monolithic complexity. This modular segmentation makes the overall system more manageable despite the advanced capabilities.
Data Source
AI summary
The present disclosure relates to electronic circuit design, and more specifically, to training a neural network to serve as the reward function for optimization-based approaches to PCB design automation. Embodiments may include generating, using a processor, one or more placed designs using a genetic optimization methodology including a reward function and adjusting the one or more placed designs and the reward function during the generating. Embodiments may further include routing the one or more placed designs using an auto-router to assign a routability score label and training a neural network, using the one or more placed designs and the routability score label, to extract one or more intermediate features from the one or more placed designs. Embodiments may also include predicting a routability of the PCB design based upon, at least in part, the one or more intermediate features.