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LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection
However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data.
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning
To address the problem, we propose a deep learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pre-trained pre-simulation model.
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work.
From IC Layout to Die Photo: A CNN-Based Data-Driven Approach
By learning the shape correspondences between pairs of layout design patterns and their scanning electron microscope (SEM) images of the product wafer thereof, given an IC layout pattern, LithoNet can mimic the fabrication process to predict its fabricated circuit shape.
