Abstract
Summary form only given. Yield managers have a large but expensive arsenal of yield improvement tools and methods at their disposal. Different tools perform different functions under different conditions and some combinations of tools and methods work better than others do. Yield managers need to know which combination of tools works the most effectively and the most cost-effectively, in order to maximize the profitability of their operations. They require metrics that allows them to assess the value of apparently unrelated options. This paper uses a model based on information theory in an attempt to create an objective method of comparing technology options for yield analysis. The knowledge extraction rate per experimentation cycle and knowledge extraction rate per unit time serve as benchmarking metrics for yield learning. Combinations of four yield analysis technologies-electrical testing (ET), automatic defect classification (ADC), spatial signature analysis (SSA) and wafer position analysis (WPA)-are examined in detail to determine an optimal yield management strategy for both the R&D and volume production environments.
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