High temperature nanoprobing application for SRAM soft failure fault isolation

Rivai, Francis; Chen, C. Q.; Ng, P.T.; Tan, Puay Siew; Tan, H.; Ma, Yuelin; H., Z.; Lam, Jeffrey

doi:10.1109/ipfa.2017.8060082

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…In general, FA is fulfilled by conventional Physical Failure Analysis (PFA) techniques such as Passive Voltage Contrast (PVC), Scanning Electron Microscope (SEM), Focused Ion Beam (FIB), and Transmission Electron Microscope (TEM) [6][7]. However, traditional PFA takes long time to identify defects and limited failures could be analyzed.…”

Section: Introductionmentioning

confidence: 99%

SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

Kim

et al. 2021

International Symposium for Testing and Failure Analysis

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Static Random Access Memory (SRAM) has long been used for a new technology development vehicle because it is sensitive to process defects due to its high density and minimum feature size. In addition, failure location can be accurately predicted because of the highly structured architecture. Thus, fast and accurate Failure Analysis (FA) of the SRAM failure is crucial for the success of new technology learning and development. It is often quite time consuming to identify defects through conventional physical failure analysis techniques. In this paper, we present an advanced defect identification methodology for SRAM bitcell failures with fast speed and high accuracy based on the bitcell transistor analog characteristics from special design for test (DFT) features, Direct Bitcell Access (DBA). This technique has the advantage to shorten FA throughput time due to a time efficient test method and an intuitive failure analysis method based on Electrical Failure Analysis (EFA) without destructive analysis. In addition, all the defects in a wafer can be analyzed and improved simultaneously utilizing the proposed defect identification methodology. Some successful case studies are also discussed to demonstrate the efficiency of the proposed defect identification methodology.

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Section: Introductionmentioning

confidence: 99%

SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

Kim

et al. 2021

International Symposium for Testing and Failure Analysis

View full text Add to dashboard Cite

show abstract

High temperature nanoprobing application for SRAM soft failure fault isolation

Cited by 1 publication

References 2 publications

SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

SRAM Bitcell Defect Identification Methodology Using Electrical Failure Analysis Data

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