PCB Layout Parasitic Impedance Prediction Using GNN Models

The patent replaces traditional physics-based electromagnetic simulation methods with a machine learning model that has been trained on simulation data. This substitution allows the system to achieve high prediction accuracy for parasitic impedances while avoiding the computational complexity and time consumption of traditional simulation methods. The machine learning model learns patterns from training data and directly predicts results without requiring complex real-time simulations.

The patent performs preliminary training of the machine learning model using extensive simulation data before actual design work. This preliminary action creates a pre-trained model that can quickly and accurately predict parasitic impedances during the actual design phase, eliminating the need to perform complex simulations for each new design iteration.

The patent replaces time-consuming traditional simulation methods with a trained machine learning model that provides rapid predictions. This substitution dramatically reduces the time required for each design iteration, enabling engineers to perform multiple iterations quickly and identify optimal designs before manufacturing, thereby reducing the need for costly iterative revisions.

The patent implements a feedback mechanism where the machine learning model's predictions are continuously refined based on comparison with actual measurement data from manufactured boards. This feedback loop improves the model's accuracy over time, leading to even better predictions and further reduction in iterative revisions needed.

The patent performs the computationally intensive model training phase as a preliminary action before actual design work. Once the model is trained on comprehensive simulation data, it can be deployed for rapid predictions during design iterations. This separates the heavy computational burden (training) from the operational phase (prediction), making the system efficient for practical use.

The patent creates a simplified computational model that copies the essential patterns learned from extensive training data. This copied model can then make accurate predictions without requiring the full computational resources needed for training or for traditional physics-based simulations, achieving a balance between accuracy and resource consumption.