Song Meng, Wang Zhaoba, Chen Youxing, Li Ya, Jin Yong, Jia Bei
School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.
National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, 030051, China.
Sci Rep. 2025 Jan 6;15(1):888. doi: 10.1038/s41598-024-85081-w.
The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis. LMFNet adopts a deep U-shaped encoder-decoder architecture, with the backbone designed using inverted residual blocks to optimize feature transmission while maintaining model compactness. Additionally, we introduce two flexible, plug-and-play modules: the Context Feature Fusion (CFF) module, which effectively integrates multi-scale contextual information at skip connection layers, and the Multi-Scale Perception Aggregation (MPA) module, which focuses on extracting and fusing multi-scale features at bottleneck layers. Experimental results demonstrate that LMFNet performs exceptionally well on the IUT dataset, significantly outperforming existing methods in terms of segmentation accuracy and model lightweighting performance.
绝缘栅双极型晶体管（IGBT）是一种关键的功率半导体器件，其内部结构的完整性直接影响其电气性能和长期可靠性。然而，IGBT超声断层图像的精确语义分割面临若干挑战，主要原因是高密度噪声干扰以及目标变形导致的视觉失真。为应对这些挑战，本文使用扫描声学显微镜（SAM）构建了一个专用的IGBT超声断层扫描（IUT）数据集，并提出了一种轻量级多尺度融合网络（LMFNet），旨在提高超声图像分析中的分割精度和处理效率。LMFNet采用深度U形编码器 - 解码器架构，其主干使用倒置残差块设计，以优化特征传输同时保持模型紧凑性。此外，我们引入了两个灵活的即插即用模块：上下文特征融合（CFF）模块，它在跳跃连接层有效集成多尺度上下文信息；以及多尺度感知聚合（MPA）模块，它专注于在瓶颈层提取和融合多尺度特征。实验结果表明，LMFNet在IUT数据集上表现出色，在分割精度和模型轻量化性能方面显著优于现有方法。
