Technical Name 基於深度學習的光刻電路失真預測,光罩修正及新穎布局圖樣偵測的設計自動化技術
Project Operator National Tsing Hua University
Project Host 林嘉文
Summary The DNN models of this technology include a LithoNet, an OPCNet,a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction,OPCnet is a dual network of LithoNet for photomask optimization. Integrated with a well-trained LithoNet, our layout novelty detection network, consisting of a self-attention guided LithoNetan autoencoder, can check if there are layout patterns easily resulting in local distortions in contours of metal lines based on multi-modal (global-local) feature fusion.
Technical Film
Scientific Breakthrough This is the first set of image-based layout-to-SEM prediction, photomask optimization,layout novelty detection methods. Given different training ADI/AEI images, this technology can learn the knowledge of photolithographetching effects. It can thus help semiconductor manufacturers to predict the shape of metal layers of IC productsdetect layout novelties. Also, this learning-based technology can be easily extended by collecting suitable layout-SEM image pairs of different fabrication configurations. Hence, it sets a new milestonebreakthrough for EDA tool development.
Industrial Applicability Compared with existing commercial rule-based software designed for analyzing ADI (after-development-image), the proposed technology comprising three learning-based methods can easily learn etching effects by training them on AEI (after-etching-image) image datasets. Therefore, this technology can also be used to predict layout-to-SEM contour deformations, optimized photo-masks,layout novelty/weakness with respect to various fabrication parameters. Through this way, this technology can assist semiconductor manufacturers to save testing timefabrication costs.
Matching Needs 天使投資人、策略合作夥伴
Keyword Design for manufacturability Electronic design automation Deep neural network Virtual metrology Lithography simulation Novelty and anomaly detection Defect Detection Weak layout pattern detection Active Learning Deep learning
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