A New Backbone Network for Instance Segmentation: Application on a Semiconductor Process Inspection

Abstract

In this paper, we propose Instance Segmentation Detector (ISD) to extract the enhanced feature-maps under the situations where training dataset is limited in the specific industry domain such as semiconductor photo lithography inspection. ISD is used as a new backbone network of state-of-the-art Mask R-CNN framework for instance segmentation. ISD consists of four dense blocks and four transition layers. Each dense block in ISD has the shortcut connection and the concatenation of the feature-maps produced in layer with dynamic growth rate. ISD is trained from scratch without using recently approached transfer learning method. Additionally, ISD is trained with image dataset pre-processed by means of the specific designed image filter to extract the better enhanced feature map of Convolutional Neural Network (CNN). In ISD, one of the key principles is the compactness, plays a critical role for addressing real time problem and for application on resource bounded devices. To validate the model, this paper uses the real image collected from the computer vision system embedded in the currently operating semiconductor manufacturing equipment. ISD achieves consistently better results than state-of-the-art methods at the standard mean average precision. Specifically, our ISD outperforms baseline method DenseNet, while requiring only 1/4 parameters. We also observe that ISD can achieve comparable better results than ResNet, with only much smaller 1/268 parameters, using no extra data or pre-trained models.