3D Magnetic Inverse Routine for Single-Segment Magnetic Field Images

In semiconductor packaging, accurately recovering 3D information is crucial for non-destructive testing (NDT) to localize circuit defects. This paper presents a novel approach called the 3D Magnetic Inverse Routine (3D MIR), which leverages Magnetic Field Images (MFI) to retrieve the parameters for the 3D current flow of a single-segment. The 3D MIR integrates a deep learning (DL)-based Convolutional Neural Network (CNN), spatial-physics-based constraints, and optimization techniques. The method operates in three stages: i) The CNN model processes the MFI data to predict (ℓ/zo), where ℓ is the wire length and zo is the wire's vertical depth beneath the magnetic sensors and classify segment type (c). ii) By leveraging spatial-physics-based constraints, the routine provides initial estimates for the position (xo, yo, zo), length (ℓ), current (I), and current flow direction (positive or negative) of the current segment. iii) An optimizer then adjusts these five parameters (xo, yo, zo, ℓ, I) to minimize the difference between the reconstructed MFI and the actual MFI. The results demonstrate that the 3D MIR method accurately recovers 3D information with high precision, setting a new benchmark for magnetic image reconstruction in semiconductor packaging. This method highlights the potential of combining DL and physics-driven optimization in practical applications.