relation: http://repositorio.unib.org/id/eprint/14283/
canonical: http://repositorio.unib.org/id/eprint/14283/
title: Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model
creator: Shahroz, Mobeen
creator: Ali, Mudasir
creator: Tahir, Alishba
creator: Fabian Gongora, Henry
creator: Uc Ríos, Carlos Eduardo
creator: Abdus Samad, Md
creator: Ashraf, Imran
subject: Ingeniería
description: Wafer mappings (WM) help diagnose low-yield issues in semiconductor production by offering vital information about process anomalies. As integrated circuits continue to grow in complexity, doing efficient yield analyses is becoming more essential but also more difficult. Semiconductor manufacturers require constant attention to reliability and efficiency. Using the capabilities of convolutional neural network (CNN) models improved by hierarchical attention module (HAM), wafer hotspot detection is achieved throughout the fabrication process. In an effort to achieve accurate hotspot detection, this study examines a variety of model combinations, including CNN, CNN+long short-term memory (LSTM) LSTM, CNN+Autoencoder, CNN+artificial neural network (ANN), LSTM+HAM, Autoencoder+HAM, ANN+HAM, and CNN+HAM. Data augmentation strategies are utilized to enhance the model’s resilience by optimizing its performance on a variety of datasets. Experimental results indicate a superior performance of 94.58% accuracy using the CNN+HAM model. K-fold cross-validation results using 3, 5, 7, and 10 folds indicate mean accuracy of 94.66%, 94.67%, 94.66%, and 94.66%, for the proposed approach, respectively. The proposed model performs better than recent existing works on wafer hotspot detection. Performance comparison with existing models further validates its robustness and performance.
date: 2024-07
type: Artículo
type: PeerReviewed
format: text
language: en
rights: cc_by_nc_nd_4
identifier: http://repositorio.unib.org/id/eprint/14283/1/Hierarchical_Attention_Module-Based_Hotspot_Detection_in_Wafer_Fabrication_Using_Convolutional_Neural_Network_Model.pdf
identifier:   Artículo Materias > Ingeniería <http://repositorio.unib.org/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unib.org/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana México > Investigación > Producción Científica <http://repositorio.unib.org/view/divisions/uninimx=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros <http://repositorio.unib.org/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Universidad de La Romana > Investigación > Producción Científica <http://repositorio.unib.org/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Abierto Inglés Wafer mappings (WM) help diagnose low-yield issues in semiconductor production by offering vital information about process anomalies. As integrated circuits continue to grow in complexity, doing efficient yield analyses is becoming more essential but also more difficult. Semiconductor manufacturers require constant attention to reliability and efficiency. Using the capabilities of convolutional neural network (CNN) models improved by hierarchical attention module (HAM), wafer hotspot detection is achieved throughout the fabrication process. In an effort to achieve accurate hotspot detection, this study examines a variety of model combinations, including CNN, CNN+long short-term memory (LSTM) LSTM, CNN+Autoencoder, CNN+artificial neural network (ANN), LSTM+HAM, Autoencoder+HAM, ANN+HAM, and CNN+HAM. Data augmentation strategies are utilized to enhance the model’s resilience by optimizing its performance on a variety of datasets. Experimental results indicate a superior performance of 94.58% accuracy using the CNN+HAM model. K-fold cross-validation results using 3, 5, 7, and 10 folds indicate mean accuracy of 94.66%, 94.67%, 94.66%, and 94.66%, for the proposed approach, respectively. The proposed model performs better than recent existing works on wafer hotspot detection. Performance comparison with existing models further validates its robustness and performance. metadata Shahroz, Mobeen; Ali, Mudasir; Tahir, Alishba; Fabian Gongora, Henry; Uc Ríos, Carlos Eduardo; Abdus Samad, Md y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, henry.gongora@uneatlantico.es, carlos.uc@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR     <http://repositorio.unib.org/id/eprint/14283/1/Hierarchical_Attention_Module-Based_Hotspot_Detection_in_Wafer_Fabrication_Using_Convolutional_Neural_Network_Model.pdf>     (2024) Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.  IEEE Access, 12.  pp. 92840-92855.  ISSN 2169-3536     
relation: http://doi.org/10.1109/ACCESS.2024.3422616
relation: doi:10.1109/ACCESS.2024.3422616
language: en