CNN Based Lithography Hotspot Detection

Abstract

The lithography hotspot detection process is crucial for semiconductor design development process. But, the lithography hotspot detection using optical simulation method takes much time and it slowdown the layout design development cycle. Though the geometry based approach is introduced as an alternative, it still revealed low detection performance and sophisticated framework. To solve this problem, we introduce a deep convolutional neural network based hotspot detection method. Our method made better results in ICCCAD 2012 dataset. To reach this score, we used lots of technical effort to improve the result in addition to just utilizing the nature of convolutional neural network. Inspection region reduction, data augmentation, DBSCAN clustering helped our work more stable and faster.

Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural Network

“…The proposed method takes the strategy of calculating the local density to down-sample the original pattern, thereby achieving data compression. The proposed method is different from the other methods [19,26]. On the one hand, the resolution of the down-sampled patterns is higher, which is 240 × 240 pixels.…”

“…The machine-learning-based hotspot-detection method obtains a hotspot detection model through model training based on layout data. The CNN has good image-classification performance, and layout data can be converted to pattern data, so CNN can be used for hotspot detection [19]. The CNN-based hotspot-detection method is shown in Figure 1.…”

“…Feature extraction and model design are important parts of the machine-learningbased hotspot-detection method. In order to obtain better performance, feature extraction has evolved from the artificially designed feature-extraction method [14,17,18] to using the convolutional neural network (CNN) [16,19]. Model design has also been developed from shallow networks to deep networks [16,20,21].…”

“…A good hotspot-detection model should perform well in F1 score, which means it has a high hotspot-detection rate and low false-alarm rate. Although the available machinelearning-based hotspot-detection methods perform well in recall, they still have insufficient precision and F1 score [16,18,19,25,26]. A high false-alarm rate will increase the postprocessing steps and increase the turn-around time of IC manufacturing.…”

Assistive Grasping Based on Laser-point Detection with Application to Wheelchair-mounted Robotic Arms

“…The deep learning method is popular in tracking, sensoring, and object classification, and a CNN has demonstrated a high performance in object detection and classification [21,22,23,24]. In this paper, a CNN is used to solve such problems as a misjudgment, poor real-time performance, and laser point and object detection issues in front of a dynamic background.…”