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.
Highlights
The semiconductor photo lithography is a process of drawing semiconductor circuits on wafers, coating them thinly with photosensitive polymer materials that respond to light on wafers, placing a mask on top of the desired pattern and pecking the light to form the desired pattern
The computer vision system used in the process of spin coating finds defects through digital image processing algorithm
Nozzle or a new wafer is used, the defect detection accuracy of the computer vision system is inevitably reduced. Considering these problems, we propose instance segmentation method based on generalized deep learning in order to be more robust to the external environment and further improve performance instead of the specialized digital image processing and signal processing method used for semiconductor photo lithography inspection
Summary
The semiconductor photo lithography is a process of drawing semiconductor circuits on wafers, coating them thinly with photosensitive polymer materials that respond to light on wafers, placing a mask on top of the desired pattern and pecking the light to form the desired pattern. The common practice in advanced instance segmentation systems is to fine-tune models pre-trained on ImageNet [13]. This fine-tuning process can be viewed as transfer learning [14]–[19]. Researchers usually train CNN models on large scale classification datasets like ImageNet [13] first, fine-tune the models on target tasks, such as object detection [20]–[35], image segmentation [36]–[39], etc. Is it possible to train instance segmentation networks from scratch directly with only smaller dataset without the pre-trained models? Is it possible to train instance segmentation networks from scratch directly with only smaller dataset without the pre-trained models? Second, are there any principles to design a resource efficient network structure for instance segmentation, keeping high detection accuracy? Third, is there any methodology to improve inspection performance other than network design? To meet this goal, we propose instance segmentation detector (ISD) and pre-processing that is performed by using image filter before training
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