DocumentCode
169332
Title
Methodologies for fast yield ramp with limited engineering resources utilizing Inline Defect data overlay to SRAM Bitmap failure and Logic Diagnostics
Author
Muthumalai, Venkatesan ; Yoong Ern Ling ; Ross, Robert ; Lockwood, Ryan ; Wu, Min ; White, Steven
Author_Institution
GLOBALFOUNDRIES Inc., Malta, NY, USA
fYear
2014
fDate
19-21 May 2014
Firstpage
341
Lastpage
344
Abstract
Yield ramp is a significant metric in determining the profitability of the semiconductor industry. Defect inspection methodologies play a major role in detecting key defect modes inline before functional testing. A fast, production ready Yield learning methodology is needed to quantify the impact of inline detected and non-detected defects and to provide a way of determining the priority of processes. This information helps the engineering teams to highlight critical yield-limiting defects and facilitate an understanding of failure modes. Current conventional strategies have limitations of inaccuracy and larger cycle time. The methodologies presented in this paper utilize Inline Defect data overlay to SRAM Bitmap failure and Logic Diagnostics for fast yield ramp in limited engineering resources. These methodologies have demonstrated exceptional cycle time reduction on cutting edge 28nm, 20nm and 14nm test chips including customer products.
Keywords
SRAM chips; integrated circuit yield; logic testing; SRAM; defect inspection; engineering resources; functional testing; inline defect data; inline detected defects; logic diagnostics; non-detected defects; semiconductor industry; size 14 nm; size 20 nm; size 28 nm; yield ramp; Failure analysis; Inspection; Logic gates; Manufacturing; Measurement; Random access memory; Systematics; Bitmap signatures; Yield ramp; cycle time reduction; defect inspection; failure analysis; volume diagnostics;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Semiconductor Manufacturing Conference (ASMC), 2014 25th Annual SEMI
Conference_Location
Saratoga Springs, NY
Type
conf
DOI
10.1109/ASMC.2014.6846951
Filename
6846951
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