The recent increasing demand of Silicon-on-Chip devices has triggered a significant impact on the industrial processes of leading semiconductor companies. The semiconductor industry is redesigning internal technology processes trying to optimize costs and production yield. To achieve this target a key role is played by the intelligent early wafer defects identification task. The Electrical Wafer Sorting (EWS) stage allows an efficient wafer defects analysis by processing the visual map associated to the wafer. The goal of this contribution is to provide an effective solution to perform automatic evaluation of the EWS defect maps. The proposed solution leverages recent approaches of deep learning both supervised and unsupervised to perform a robust EWS defect patterns classification in different device technologies including Silicon and Silicon Carbide. This method embeds an end-to-end pipeline for supervised EWS defect patterns classification including a hierarchical unsupervised system to assess novel defects in the production line. The implemented "Unsupervised Learning Block" embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed "Supervised Learning Block" includes a Convolutional Neural Network trained to perform a supervised classification of the Wafer Defect Maps (WDMs). The proposed system has been evaluated on several datasets, showing effective performance in the classification of the defect patterns (average accuracy about 97%).

Explainable Deep Learning System for Advanced Silicon and Silicon Carbide Electrical Wafer Defect Map Assessment

Sarpietro, Riccardo Emanuele;Pino, Carmelo;Palazzo, Simone;Battiato, Sebastiano;Spampinato, Concetto;Rundo, Francesco
2022-01-01

Abstract

The recent increasing demand of Silicon-on-Chip devices has triggered a significant impact on the industrial processes of leading semiconductor companies. The semiconductor industry is redesigning internal technology processes trying to optimize costs and production yield. To achieve this target a key role is played by the intelligent early wafer defects identification task. The Electrical Wafer Sorting (EWS) stage allows an efficient wafer defects analysis by processing the visual map associated to the wafer. The goal of this contribution is to provide an effective solution to perform automatic evaluation of the EWS defect maps. The proposed solution leverages recent approaches of deep learning both supervised and unsupervised to perform a robust EWS defect patterns classification in different device technologies including Silicon and Silicon Carbide. This method embeds an end-to-end pipeline for supervised EWS defect patterns classification including a hierarchical unsupervised system to assess novel defects in the production line. The implemented "Unsupervised Learning Block" embeds ad-hoc designed Dimensionality Reduction combined with Clustering and a Metrics-driven Classification Sub-Systems. The proposed "Supervised Learning Block" includes a Convolutional Neural Network trained to perform a supervised classification of the Wafer Defect Maps (WDMs). The proposed system has been evaluated on several datasets, showing effective performance in the classification of the defect patterns (average accuracy about 97%).
2022
Production
Deep learning
Wavelength division multiplexing
Convolutional neural networks
Silicon carbide
Feature extraction
Artificial intelligence
Clustering methods
Artificial intelligence
convolutional neural network
explainable architectures
hierarchical clustering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/640734
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