YIELD-ORIENTED COMPUTER-AIDED DEFECT DIAGNOSIS

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)-ba sed defect methodology is investigated to see if it meets the above re quirements. Using an SRAM test vehicle as an example, the research ana lyzes whether computer-generated mappings between defect types end tes ter fail data can provide sufficient resolution for both, defect diagn osis and defect parameter characterization.