Image Segmentation of Wafer Cutting Path based on RFB-Unet

In the process of chip wafer dicing, it is necessary to manually check the relative position of the cut mark and the cutting path in real time in order to adjust the cutting disc in time. During the process, the instability of manual calibration will affect production efficiency. Therefore, this paper proposes an improved U-Net convolutional neural network to locate the cutting path. It improves the network's ability to extract image features and generalization capabilities by adding the RFB receptive field module proposed in the target detection task to the U-Net encoder. On the image segmentation effect of the wafer cutting path, the network RFB-UNet designed in this paper improves 2% on the indicators of MIoU and FWIoU. It is verified that the RFB is a module that can improve the existing network in the task of semantic segmentation. Besides, this paper constructs a dataset for image segmentation of the wafer cutting path.