论文标题
半导体缺陷模式通过自我增殖和注意神经网络分类
Semiconductor Defect Pattern Classification by Self-Proliferation-and-Attention Neural Network
论文作者
论文摘要
半导体制造是革命的风口浪尖：物联网（IoT）。使用IoT，我们可以将所有设备和馈送信息连接回工厂，以便可以检测到质量问题。在这种情况下，将越来越多的边缘设备用于晶圆检查设备。此边缘设备必须具有快速检测缺陷的能力。因此，如何开发适合边缘设备的自动缺陷分类的高效率体系结构是主要任务。在本文中，我们提出了一种新颖的体系结构，可以更有效地执行缺陷分类。第一个功能是自我增殖，使用一系列线性变换以更便宜的成本生成更多特征图。第二个功能是自我注意事件，通过诸如频道和空间的注意机制捕获特征图的远程依赖性。我们将此方法命名为自我增殖和注意力集中的神经网络。该方法已成功应用于各种缺陷模式分类任务。与其他最新方法相比，在许多缺陷检查任务中，SP＆A-NET具有更高的精度和较低的计算成本。
Semiconductor manufacturing is on the cusp of a revolution: the Internet of Things (IoT). With IoT we can connect all the equipment and feed information back to the factory so that quality issues can be detected. In this situation, more and more edge devices are used in wafer inspection equipment. This edge device must have the ability to quickly detect defects. Therefore, how to develop a high-efficiency architecture for automatic defect classification to be suitable for edge devices is the primary task. In this paper, we present a novel architecture that can perform defect classification in a more efficient way. The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism. We named this method as self-proliferation-and-attention neural network. This method has been successfully applied to various defect pattern classification tasks. Compared with other latest methods, SP&A-Net has higher accuracy and lower computation cost in many defect inspection tasks.
