High-performance one-stage detector for SiC crystal defects based on convolutional neural network
High-performance one-stage detector for SiC crystal defects based on convolutional neural network
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DOI：
10.1016/j.knosys.2023.110994
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Haochen Shi;Zhiyuan Jin;Wenjing Tang;Jing Wang;K. Jiang;Mingsheng Xu;Wei Xia;Xiangang Xu
中科院分区：
文献类型：
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作者：
Haochen Shi;Zhiyuan Jin;Wenjing Tang;Jing Wang;K. Jiang;Mingsheng Xu;Wei Xia;Xiangang Xu
SiC（碳化硅）作为最重要的第三代半导体材料，在5G基站、新能源汽车充电桩等众多领域有着巨大的市场前景。碳化硅晶体缺陷的识别是提高晶体质量的关键。目前，本研究主要依靠人工方法来识别缺陷，在准确性和效率上都有很大的局限性。因此，为了在复杂场景下快速检测和分类不同的SiC晶体缺陷，本研究首次提出了一种基于卷积神经网络的SiC晶体缺陷检测（SCDD-Net）模型。SCDD-Net采用一种改进的在线卷积再参数化方法，可以有效地提取SiC晶体缺陷的特征，减少了庞大的训练开销。我们设计了一个新的空间金字塔池模块，当与全局上下文块相结合时，可以快速融合高级晶体缺陷和底层特征。我们还设计了一个基于锚点的去耦检测头网络来识别较小的晶体缺陷。通过对5300多张高质量显微图像的采集和处理，首次建立了细粒度标记SiC晶体缺陷图像数据集SiC- crystal - 5k。实验结果表明，与其他先进模型相比，SCDD-Net具有良好的检测精度。高分辨率SiC晶体缺陷识别的平均精度达到99.53%，对应的单图像检测速度为102 fps。除了晶体缺陷检测之外，SCDD-Net模型还可以在广泛的场景中用作通用检测器。
SiC (silicon carbide), as the most important third-generation semiconductor material, has huge market prospects in numerous fields, such as 5G base stations and new energy vehicle charging piles. The identification of SiC crystal defects is essential for improving crystal quality. Currently, this study relies mainly on artificial methods to identify defects, which have significant limitations in terms of accuracy and efficiency. Thus, to quickly detect and classify different SiC crystal defects in complex scenarios, a convolutional neural network-based SiC crystal defect detection (SCDD-Net) model is presented for the first time in this study. SCDD-Net uses an improved online convolutional re-parameterization method that can effectively extract the features of SiC crystal defects and decrease the large training overhead. We devised a new spatial pyramid pooling module that, when combined with the global context block, enables the fast fusion of high-level crystal defects and underlying features. We also designed an anchor-based decoupling detection head network to identify smaller crystal defects. By collecting and processing more than 5300 high-quality microscopic images, we built a fine-grained labeled SiC crystal defect image dataset, SiC-Crystal-5K, for the first time. The experimental results show that the SCDD-Net has excellent detection accuracy compared to other state-of-the-art models. The mean average precision for high-resolution SiC crystal defect identification reached 99.53%, corresponding to a single-image detection speed of 102 fps. In addition to crystal-defect detection, the SCDD-Net model can be used as a general-purpose detector in a wide range of scenarios.