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Authors: Luigi Viagrande 1 ; Filippo Milotta 1 ; Paola Giuffrè 2 ; Giuseppe Bruno 2 ; Daniele Vinciguerra 2 and Giovanni Gallo 3

Affiliations: 1 STMicroelectronics, Catania, Italy, University of Catania, Catania, Italy ; 2 STMicroelectronics, Catania, Italy ; 3 University of Catania, Catania, Italy

Keyword(s): Semiconductor Manufacturing, Electrical Wafer Sorting Map, Wafer Map Clustering, Anomalies Signature Classification, Yield Analysis.

Abstract: We focused onto a very specific kind of data from semiconductor manufacturing called Electrical Wafer Sorting (EWS) maps, that are generated during the wafer testing phase performed in semiconductor device fabrication. Yield detractors are identified by specific and characteristic anomalies signatures. Unfortunately, new anomalies signatures may appear among the huge amount of EWS maps generated per day. Hence, it’s unfeasible to define just a finite set of possible signatures, as this will not represent a real use-case scenario. Our goal is anomalies signatures classification. For this purpose, we present a semisupervised approach by combining hierarchical clustering to create the starting Knowledge Base, and a supervised classifier trained leveraging clustering phase. Our dataset is daily increased, and the classifier is dynamically updated considering possible new created clusters. Training a Convolutional Neural Network, we reached performance comparable with other state-of-the-a rt techniques, even if our method does not rely on any labeled dataset and can be daily updated. Our dataset is skewed and the proposed method was proved to be rotation invariant. The proposed method can grant benefits like reduction of wafer test results review time, or improvement of processes, yield, quality, and reliability of production using the information obtained during clustering process. (More)

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Paper citation in several formats:
Viagrande, L.; Milotta, F.; Giuffrè, P.; Bruno, G.; Vinciguerra, D. and Gallo, G. (2020). Semisupervised Classification of Anomalies Signatures in Electrical Wafer Sorting (EWS) Maps. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2; ISSN 2184-4321, pages 278-285. DOI: 10.5220/0008914402780285

@conference{visapp20,
author={Luigi Viagrande. and Filippo Milotta. and Paola Giuffrè. and Giuseppe Bruno. and Daniele Vinciguerra. and Giovanni Gallo.},
title={Semisupervised Classification of Anomalies Signatures in Electrical Wafer Sorting (EWS) Maps},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={278-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008914402780285},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Semisupervised Classification of Anomalies Signatures in Electrical Wafer Sorting (EWS) Maps
SN - 978-989-758-402-2
IS - 2184-4321
AU - Viagrande, L.
AU - Milotta, F.
AU - Giuffrè, P.
AU - Bruno, G.
AU - Vinciguerra, D.
AU - Gallo, G.
PY - 2020
SP - 278
EP - 285
DO - 10.5220/0008914402780285

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