ML-Based Approach for PCB Anomaly and Leakage Detection

Machine Learning (ML) applications in our daily life have seen tremendous growth and they are closely tied to various Electromagnetic Interference and Compatibility (EMI/EMC) scenarios. This paper proposes a new ML-driven approach for detecting leakage and troubleshooting Printed Circuit Boards (PCBs) anomalies. The approach utilizes a simple Feedforward Neural Network (FNN) model that requires minimal training and a small dataset. This model is first trained on measurement points of 3D E-field data from an unslotted PCB. It then uses this training to detect the exact location of anomalies or E-field leakage within this PCB. This process achieves a high precision with a Root Mean Square Error (RMSE) of only 0.02 demonstrating an efficient detection accuracy.