Comparative assessment of yield learning tools using information theory

Charles Weber; Vijay Sankaran; Kenneth W. Tobin Jr.; Gary Scher

doi:10.1117/12.346930

27 April 1999 Comparative assessment of yield learning tools using information theory

Charles Weber, Vijay Sankaran, Kenneth W. Tobin Jr., Gary Scher

Proceedings Volume 3743, In-Line Characterization, Yield Reliability, and Failure Analyses in Microelectronic Manufacturing; (1999) https://doi.org/10.1117/12.346930
Event: Microelectronic Manufacturing Technologies, 1999, Edinburgh, United Kingdom

Abstract

A model based on information theory, which allows yield managers to choose the optimal strategies for yield management in microelectronic manufacturing, is presented. The data reduction rate per experimentation cycle and data reduction rate per unit time serve as benchmarking metrics for yield learning. These newly defined metrics enable managers to make objective comparisons of apparently unrelated technologies. Four yield analysis tools -- electrical testing, automatic defect classification, spatial signature analysis and wafer position analysis -- are examined in detail to suggest an optimal yield management strategy for both the R and D and volume production environments.

Citation Download Citation

Charles Weber, Vijay Sankaran, Kenneth W. Tobin Jr., and Gary Scher "Comparative assessment of yield learning tools using information theory", Proc. SPIE 3743, In-Line Characterization, Yield Reliability, and Failure Analyses in Microelectronic Manufacturing, (27 April 1999); https://doi.org/10.1117/12.346930

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