20 citations · 74 across the 10 of their papers we have counts for
10 papers
Scanning Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Review
Enrique Dehaerne, Bappaditya Dey, Victor Blanco +1
In this review, automatic defect inspection algorithms that analyze Scanning Electron Microscopy (SEM) images for Semiconductor Manufacturing (SM) are identified, categorized, and…
A Machine Learning Approach Towards SKILL Code Autocompletion
Enrique Dehaerne, Bappaditya Dey, Wannes Meert
As Moore's Law continues to increase the complexity of electronic systems, Electronic Design Automation (EDA) must advance to meet global demand. An important example of an EDA tec…
Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization
Enrique Dehaerne, Bappaditya Dey, Sandip Halder +1
As semiconductor patterning dimensions shrink, more advanced Scanning Electron Microscopy (SEM) image-based defect inspection techniques are needed. Recently, many Machine Learning…
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
Thibault Lechien, Enrique Dehaerne, Bappaditya Dey +4
A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency…
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection
Vic De Ridder, Bappaditya Dey, Enrique Dehaerne +3
Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise…
YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach
Enrique Dehaerne, Bappaditya Dey, Hossein Esfandiar +4
Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed self-asse…
A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation
Enrique Dehaerne, Bappaditya Dey, Sandip Halder +1
Innovative Electronic Design Automation (EDA) solutions are important to meet the design requirements for increasingly complex electronic devices. Verilog, a hardware description l…
SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering
MinJin Hwang, Bappaditya Dey, Enrique Dehaerne +2
In this study, we applied the PointRend (Point-based Rendering) method to semiconductor defect segmentation. PointRend is an iterative segmentation algorithm inspired by image rend…
Optimizing YOLOv7 for Semiconductor Defect Detection
Enrique Dehaerne, Bappaditya Dey, Sandip Halder +1
The field of object detection using Deep Learning (DL) is constantly evolving with many new techniques and models being proposed. YOLOv7 is a state-of-the-art object detector based…
Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach
Bappaditya Dey, Enrique Dehaerne, Kasem Khalil +3
In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically obj…