33 citations · 112 across the 23 of their papers we have counts for
23 papers
Information-Theoretic Causal Modelling of Semiconductor Process Dynamics
Daniel Sørensen, Giorgio Melchiorre, Sudip Bandyopadhyay +3
With the progress of the semiconductor industry toward increasingly complex compute devices and tighter process tolerances, advanced process control has become crucial. This work e…
High-Fidelity Synthetic Transmission Electron Microscopy Image Generation Using Diffusion Probabilistic Models for Data-Limited Semiconductor Metrology
Johannes Boehm, Bappaditya Dey
Advanced semiconductor nodes drastically increased demand for Transmission Electron Microscopy (TEM), yet destructive sample preparation, slow imaging and high costs severely limit…
Unsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series
Daniel Sorensen, Bappaditya Dey, Minjin Hwang +1
Semiconductor manufacturing is an extremely complex and precision-driven process, characterized by thousands of interdependent parameters collected across diverse tools and process…
Continuous Wavelet Transform and Siamese Network-Based Anomaly Detection in Multi-variate Semiconductor Process Time Series
Bappaditya Dey, Daniel Sorensen, Minjin Hwang +1
Semiconductor manufacturing is an extremely complex process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate…
Advancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes
Bappaditya Dey, Matthias Monden, Victor Blanco +2
In this research, we introduce a unified end-to-end Automated Defect Classification-Detection-Segmentation (ADCDS) framework for classifying, detecting, and segmenting multiple ins…
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…
An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection
Amit Prasad, Bappaditya Dey, Victor Blanco +1
Deep learning-based semiconductor defect inspection has gained traction in recent years, offering a powerful and versatile approach that provides high accuracy, adaptability, and e…
Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images
Bappaditya Dey, Vic De Ridder, Victor Blanco +2
Precision in identifying nanometer-scale device-killer defects is crucial in both semiconductor research and development as well as in production processes. The effectiveness of ex…
Towards Improved Semiconductor Defect Inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS
Ying-Lin Chen, Jacob Deforce, Vic De Ridder +4
Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufacturing.…
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…
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…