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Found 9 papers, 1 papers with code
A Machine Learning Approach Towards SKILL Code Autocompletion
In this study, a novel, data-efficient methodology for generating SKILL code is proposed and experimentally validated.
Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization
We discuss the advantages and disadvantages of different feature extractors as well as the RL-based framework in general for semiconductor defect localization.
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
This inherent noise is one of the main challenges for defect inspection.
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection
We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects.
YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach
In this work, we propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns while requiring minimal quality control effort from a DSA expert.
A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation
This is validated by comparing different pretrained models trained on different subsets of the proposed Verilog dataset using multiple evaluation metrics.
SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering
In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation.
Optimizing YOLOv7 for Semiconductor Defect Detection
In this research, we experiment with YOLOv7, a recently proposed, state-of-the-art object detector, by training and evaluating models with different hyperparameters to investigate which ones improve performance in terms of detection precision for semiconductor line space pattern defects.
Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach
In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance.
