20 citations · 45 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…