Parametric failure modeling and yield analysis for STT-MRAM

2018 ◽  
Author(s):  
Sarath Mohanachandran Nair ◽  
Rajendra Bishnoi ◽  
Mehdi B. Tahoori
Keyword(s):  
Yield Analysis ◽  
Failure Modeling ◽  
Parametric Failure

Device Selection for Failure Analysis of Chain Fails Using Diagnosis Driven Yield Analysis

2011 ◽  
Author(s):  
Chris Schuermyer ◽  
Brady Benware ◽  
Graham Rhodes ◽  
Davide Appello ◽  
Vincenzo Tancorre ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Yield Analysis ◽  
Analysis Methodology ◽  
Device Selection ◽  
Selection For ◽  
Random Defects

Abstract This work presents the first application of a diagnosis driven approach for identifying systematic chain fail defects in order to reduce the time spent in failure analysis. The zonal analysis methodology that is applied separates devices into systematic and random populations of chain fails in order to prevent submitting random defects for failure analysis. Two silicon case studies are presented to validate the production worthiness of diagnosis driven yield analysis for chain fails. The defects uncovered in these case studies are very subtle and would be difficult to identify with any other methodology.

Realizing Dynamic Thermal Laser Stimulation by Lock-in IR-OBIRCH Assisted with a Current Detection Probe Head

2013 ◽  
Author(s):  
Chunlei Wu ◽  
Suying Yao
Keyword(s):  
Operating Condition ◽  
Laser Stimulation ◽  
Detection Probe ◽  
Novel Method ◽  
Lock In ◽  
Current Detection ◽  
Parametric Failure ◽  
A Current ◽  
Probe Head

Abstract Lock-in IR-OBIRCH analysis, as a kind of static thermal laser stimulation (S-TLS) technique, is very effective to isolate a fault for the parametric failure cases. However, its capability is limited to localize a defect when the IC is operated under a defined operating condition. Whereas the dynamic thermal laser stimulation (D-TLS) technique is good at locating a fault while the IC is operated under some functions to activate the failure. In this paper, a novel method is presented to realize DTLS just by Lock-in IR-OBIRCH assisted with a Current Detection Probe Head. Two cases are studied to demonstrate the effectiveness of this method.

Advanced Scan Diagnosis Based Fault Isolation and Defect Identification for Yield Learning

2005 ◽  
Author(s):  
Chris Eddleman ◽  
Nagesh Tamarapalli ◽  
Wu-Tung Cheng
Keyword(s):  
Failure Analysis ◽  
Fault Location ◽  
Effective Means ◽  
Fault Isolation ◽  
Yield Enhancement ◽  
Yield Analysis ◽  
Isolation Techniques ◽  
Yield Learning ◽  
Time Required ◽  
Inspection Data

Abstract Yield analysis of sub-micron devices is an ever-increasing challenge. The difficulty is compounded by the lack of in-line inspection data as many companies adopt foundry or fab-less models for acquiring wafers. In this scenario, failure analysis is increasingly critical to help drive yields. Failure analysis is a process of fault isolation, or a method of isolating failures as precisely as possible followed by identification of a physical defect. As the number of transistors and metal layers increase, traditional fault isolation techniques are less successful at isolating a cause of failures. Costs are increasing due to the amount of time needed to locate the physical defect. One solution to the yield analysis problem is scan diagnosis based fault isolation. Previous scan diagnosis based techniques were limited with little information about the type of fault and confidence of diagnosis. With new scan diagnosis algorithms it is now possible to not only isolate, but to identify the type of fault as well as assigning a confidence ranking prior to any destructive analysis. This paper presents multiple case studies illustrating the application of scan diagnosis as an effective means to achieve yield enhancement. The advanced scan diagnostic tool used in this study provides information about the fault type as well as fault location. This information focuses failure analysis efforts toward a suspected defect, decreasing the cycle time required to determine root cause, as well as increasing the over all success rate.

Hybrid Beamforming System Diagnosis: Failure Modeling and Identification

2021 ◽  
pp. 1-1
Author(s):  
Rui Sun ◽  
Weidong Wang ◽  
Li Chen ◽  
Guo Wei ◽  
Wenyi Zhang
Keyword(s):  
Failure Modeling ◽  
System Diagnosis ◽  
Hybrid Beamforming
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