Hotspot Detection of Semiconductor Lithography Circuits Based on Convolutional Neural Network

Abstract

In the advanced semiconductor lithography manufacturing process, the sub-wavelength lithography gap may cause lithographic error and the difference between the wafer pattern and mask pattern which may cause wafer defects in the later process. Even if a layout passes the design rule checking (DRC), it still might contain process hotspots which are sensitive to the lithographic process. Hence, process-hotspot detection has become a crucial issue. In this paper, we propose a convolutional neural network (CNN) based process-hotspot detection framework. Different network parameters including the training batch size, learning rate, loss functions as well as the optimization methods are compared and the optimal method is proposed with respect to a typical benchmark. The results of the tuned model are better than common machine learning methods. A general training flow is proposed. The method is flexible and can be applied to different benchmarks for better hotspot detection performance.

Keywords

• lithography
• hotspot detection
• cnn
• deep learning