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Introduction to Wafer Tomography: Likelihood-Based Prediction of Integrated-Circuit Yield

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

Listed:
  • Michael Baron

    (American University)

  • Emmanuel Yashchin

    (IBM Research Division)

  • Asya Takken

    (Alliant Cooperative Data)

Abstract

A concept of wafer tomography is introduced referring to a detailed reconstruction of hidden information on integrated circuits given incomplete and sparse layer-by-layer data that are usually available. Proposed tools associate chip failures with all observed, partially observed, and unobserved defects on a chip via a cause-and-effect relationship to predict the final yield at any time during the production process. The method also allows to determine the most probable causes of failures, the most dangerous defects, the most vulnerable layers, the most influential factors, and their combinations.

Suggested Citation

  • Michael Baron & Emmanuel Yashchin & Asya Takken, 2022. "Introduction to Wafer Tomography: Likelihood-Based Prediction of Integrated-Circuit Yield," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 227-252, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_9
    DOI: 10.1007/978-3-031-07155-3_9
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