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Found 13 papers, 1 papers with code
Towards Improved Semiconductor Defect Inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS
This research's goal is to propose a scale-invariant ADCD framework capable to upscale images, addressing this issue.
Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance
The goal of this work is of two-fold as, (a) to quantify the impact of overlay on capacitance and (b) to see if we can predict the final capacitance measurements with selected machine learning models at an early stage.
Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy
However, a significant increase in stochastic defects and the complexity of defect detection becomes more pronounced with high-NA.
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.
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE
The technological advance of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) has opened the gates to extensive researches on thinner photoresists (below 30nm), necessary for the industrial implementation of High NA EUVL.
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.
SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation
To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model.
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.
