Wafer Defect

Wafer defect detection is crucial for improving yield and quality in semiconductor manufacturing, driving research into automated, efficient methods for identifying and classifying defects. Current research focuses on applying machine learning, particularly deep learning models like convolutional neural networks and transformers, often enhanced with techniques such as domain adaptation and knowledge distillation to handle limited or imbalanced datasets. These advancements aim to improve accuracy, speed, and resource efficiency in defect detection, enabling real-time monitoring and predictive maintenance in semiconductor fabrication processes. The resulting improvements in yield and reduced production costs have significant economic and technological implications for the industry.

Papers

November 17, 2024

Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
Yin-Yin Bao, Er-Chao Li, Hong-Qiang Yang, Bin-Bin Jia
Convolutional Neural Network Soft Augmentation Autoencoder Based Defect Pattern Wafer Defect Defect Class Wafer Map Defect Pattern

July 15, 2024

FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
Yuqi Jiang, Xudong Lu, Qian Jin, Qi Sun, Hanming Wu, Cheng Zhuo
Large Multimodal Model Defect Sample Wafer Defect

April 23, 2024

Iterative Cluster Harvesting for Wafer Map Defect Patterns
Alina Pleli, Simon Baeuerle, Michel Janus, Jonas Barth, Ralf Mikut, Hendrik P. A. Lensch
Unsupervised Clustering Defect Pattern Wafer Defect Iterative Clustering Wafer Map Defect Pattern

October 16, 2023

Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers
Kamal Taha
Machine Learning New Machine Defect Classification Wafer Defect

October 6, 2023

Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation
Qiyu Wei, Wei Zhao, Xiaoyan Zheng, Zeng Zeng
Latent Vector Semiconductor Manufacturing Wafer Defect Wafer Map Defect Pattern

May 31, 2023

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Adrian Shuai Li, Elisa Bertino, Rih-Teng Wu, Ting-Yan Wu
Deep Learning Domain Adaptation Data Augmentation Data Scarcity Wafer Defect Novel Domain Adaptation Imbalanced Training

March 24, 2023

March 21, 2023

An Embarrassingly Simple Approach for Wafer Feature Extraction and Defect Pattern Recognition
Nitish Shukla
Deep Neural Network Feature Extraction Defect Detection Defect Pattern Wafer Defect

January 21, 2023

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting
Angzhi Fan, Yu Huang, Fei Xu, Sthitie Bom
High Quality Sensor Soft Sensor Semiconductor Manufacturing Wafer Defect Machine Learning Technology Silicon Wafer Production Monitoring

August 30, 2022

Deep Open-Set Recognition for Silicon Wafer Production Monitoring
Luca Frittoli, Diego Carrera, Beatrice Rossi, Pasqualina Fragneto, Giacomo Boracchi
Outlier Detection Open Set Recognition Wafer Defect Semiconductor Wafer Silicon Wafer Production Monitoring

July 3, 2022

WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects
Subhrajit Nag, Dhruv Makwana, Sai Chandra Teja R, Sparsh Mittal, C Krishna Mohan
Classification Code Segmentation Based Approach lightWeight Network Wafer Defect

November 12, 2021

Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned
Xiaoye Qian, Chao Zhang, Jaswanth Yella, Yu Huang, Ming-Chun Huang, Sthitie Bom
Fine Tuning Full Model Soft Sensor Wafer Defect Silicon Wafer Production Monitoring

Wafer Defect

Papers

Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network

FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries

Iterative Cluster Harvesting for Wafer Map Defect Patterns

Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers

Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Mixed-Type Wafer Classification For Low Memory Devices Using Knowledge Distillation

Efficient Mixed-Type Wafer Defect Pattern Recognition Using Compact Deformable Convolutional Transformers

An Embarrassingly Simple Approach for Wafer Feature Extraction and Defect Pattern Recognition

Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting

Deep Open-Set Recognition for Silicon Wafer Production Monitoring

WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects

Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned