Efficiently Performing Yield Enhancements by Identifying Dominant Physical Root Cause from Test Fail Data

Abstract

Yield enhancements in the manufacturing process today require an expensive, long and tedious physical failure analysis process to identify the root cause. In this paper we present Axiom, a new technique geared towards efficiently identifying a single dominant defect mechanism (for example in an excursion wafer) by analyzing fail data collected from the production test environment. Axiom utilizes statistical hypothesis testing in a novel way to analyze logic diagnosis data along with information on physical features in the design layout and reliably identify the dominant cause for yield loss. This new methodology was validated by applying it to a single excursion wafer produced on a 90 nm process, in which the dominant failing physical feature was correctly identified.