In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions. Wafer2Spike achieves an average classification accuracy of 98\% on the WM-811k wafer benchmark dataset. It is also superior to existing approaches for classifying defect patterns that are underrepresented in the original dataset. Wafer2Spike achieves this improved precision with great computational efficiency.

翻译：在集成电路设计中，晶圆图模式分析对于提高良率和检测制造问题至关重要。我们开发了Wafer2Spike，一种利用脉冲神经网络进行晶圆图模式分类的架构，并证明训练有素的SNN相比基于深度神经网络的解决方案能实现更优的性能。Wafer2Spike在WM-811k晶圆基准数据集上达到了98%的平均分类准确率。对于原始数据集中代表性不足的缺陷模式分类，该方法也优于现有方法。Wafer2Spike以极高的计算效率实现了这一精度提升。