20 citations · 35 across the 6 of their papers we have counts for
6 papers
Improved Defect Detection and Classification Method for Advanced IC Nodes by Using Slicing Aided Hyper Inference with Refinement Strategy
Vic De Ridder, Bappaditya Dey, Victor Blanco +2
In semiconductor manufacturing, lithography has often been the manufacturing step defining the smallest possible pattern dimensions. In recent years, progress has been made towards…
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…
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…
Tailoring the interfacial magnetic interaction in epitaxial LaSrMnO/SmCaMnO heterostructures
Snehal Mandal, Sandip Halder, Biswarup Satpati +2
Interface engineering in complex oxide heterostructures has developed into a flourishing field as various intriguing physical phenomena can be demonstrated which are otherwise abse…
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…