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<p>In the domain of semiconductor integrated circuit manufacturing, accurately identifying the root causes of defects is critical for enhancing yield rates. Traditionally, this analytical process has been both time-intensive and challenged by inaccuracies, primarily due to the intricate and varied morphology of wafer defects. While convolutional neural networks (CNNs) with encoder-decoder architectures have made significant strides in the segmentation of defects, they inherently struggle to capture distant interactions and achieve high performance in classification tasks. Conversely, recent advancements in transformers have showcased their proficiency in learning global image dependencies. However, transformers often lack the specific graphical priors and the adaptability typically associated with CNNs. Addressing these limitations, we introduce SCSNet, an innovative architecture that merges the strengths of transformers and CNNs. This fusion network is designed to enhance both segmentation and classification of scanning electron microscopy (SEM) images of wafer defects. SCSNet incorporates a conventional encoder-decoder framework, supplemented by shape flow branches and multi-cross-attention (MCF) modules within a skip connection architecture. Rigorous experimentation on a dataset of 4425 high-resolution wafer defects, sourced from our operational wafer fabrication facility, demonstrates SCSNet’s superior performance. Notably, SCSNet surpasses existing advanced CNNs, transformers, and their hybrid counterparts, achieving a classification accuracy of 97.62% and a segmentation Intersection over Union (IoU) of 84.09%. Currently implemented on our local server for engineering use, SCSNet represents a major advancement in semiconductor manufacturing, offering a more precise and efficient tool for wafer defect analysis.</p>

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SCSNet: a novel transformer-CNN fusion architecture for enhanced segmentation and classification on high-resolution semiconductor micro-scale defects

  • 期刊论文

  • 发表日期：

    2025年2月12日

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  • Yuening Luo,
  • Zhouzhouzhou Mei,
  • Yibo Qiao,
  • Yining Chen

摘要

In the domain of semiconductor integrated circuit manufacturing, accurately identifying the root causes of defects is critical for enhancing yield rates. Traditionally, this analytical process has been both time-intensive and challenged by inaccuracies, primarily due to the intricate and varied morphology of wafer defects. While convolutional neural networks (CNNs) with encoder-decoder architectures have made significant strides in the segmentation of defects, they inherently struggle to capture distant interactions and achieve high performance in classification tasks. Conversely, recent advancements in transformers have showcased their proficiency in learning global image dependencies. However, transformers often lack the specific graphical priors and the adaptability typically associated with CNNs. Addressing these limitations, we introduce SCSNet, an innovative architecture that merges the strengths of transformers and CNNs. This fusion network is designed to enhance both segmentation and classification of scanning electron microscopy (SEM) images of wafer defects. SCSNet incorporates a conventional encoder-decoder framework, supplemented by shape flow branches and multi-cross-attention (MCF) modules within a skip connection architecture. Rigorous experimentation on a dataset of 4425 high-resolution wafer defects, sourced from our operational wafer fabrication facility, demonstrates SCSNet’s superior performance. Notably, SCSNet surpasses existing advanced CNNs, transformers, and their hybrid counterparts, achieving a classification accuracy of 97.62% and a segmentation Intersection over Union (IoU) of 84.09%. Currently implemented on our local server for engineering use, SCSNet represents a major advancement in semiconductor manufacturing, offering a more precise and efficient tool for wafer defect analysis.