33 citations · 88 across the 8 of their papers we have counts for
8 papers
SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation
Vic De Ridder, Bappaditya Dey, Sandip Halder +1
With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection…
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…
Deep Learning-Based Defect Classification and Detection in SEM Images
Bappaditya Deya, Dipam Goswamif, Sandip Haldera +3
This proposes a novel ensemble deep learning-based model to accurately classify, detect and localize different defect categories for aggressive pitches and thin resists (High NA ap…