Attention-Enhanced Convolutional Deep Architecture with Adaptive Jacobian Regularization for Joint Multi-Modal Optical and X-Ray Screening of Silicon-Carbide Power Devices

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.