33 citations · 112 across the 23 of their papers we have counts for
12 papers · 1 filter
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…
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…
Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance
Bappaditya Dey, Anh Tuan Ngo, Sara Sacchi +3
Moore Law states that transistor density will double every two years, which is sustained until today due to continuous multi-directional innovations, such as extreme ultraviolet li…
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…
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE
Sara Sacchi, Bappaditya Dey, Iacopo Mochi +2
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…
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-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…