Yield-oriented computer-aided defect diagnosis

Abstract

Any good yield-oriented defect strategy must have two main components-(a) the ability to perform rapid defect diagnosis for yield learning, and (b) the ability to efficiently extract defect parameters from the manufacturing line. In this work, an inductive fault analysis (IFA)-based defect methodology is investigated to see if it meets the above requirements. Using an SRAM test vehicle as an example, the research analyzes whether computer-generated mappings between defect types and tester fail data can provide sufficient resolution for both, defect diagnosis and defect parameter characterization.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>