Abstract

The semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important as advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing: production control is often based on the “judgment” of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a data mining approach derived from the analysis of social networks, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study of wafer clustering was conducted on real production data for validating the proposed clustering algorithm.

Autore Pugliese

• Gallo Crescenzio

Tutti gli autori

• C. Gallo , M. De Bonis

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2013

ISSN

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ISBN

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Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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