In recent years deep learning has made tremendous progress and has substantially improved the state-of-art in many applications that include object detection, speech recognition, and image classification3 The application and design of these deep learning tools in semiconductor defect recognition can be very useful, as it has the potential to reduce the setup time, cost and improving the overall (defect) detection and classification accuracy.
In this work we demonstrate the application of convolution neural networks on e-beam images of intentional defects on various types of patterns. We assess various filters and their outputs at each layer of a custom convolutional neural network (Figure 2) to ultimately improve the architecture and its accuracy. We also demonstrate the relative effectiveness of transfer learning in this application with limited availability of labelled data. Using the architecture illustrated in Figure 2 we are able to achieve a classification accuracy of 95%.