scholarly journals An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

2016 ◽  
Vol 17 (2) ◽  
pp. 160-172 ◽  
Author(s):  
Xin Li ◽  
Jin Sun ◽  
Fu Xiao ◽  
Jiang-shan Tian
Keyword(s):  
Yield Analysis ◽  
Optimization Framework ◽  
Parameter Variations

Outlet Surface Area Influence in Spiral Casing Design on Centrifugal Fan Performance

2020 ◽  
Vol 5 (1) ◽  
pp. 37-41
Author(s):  
Ardit Gjeta ◽  
Lorenc Malka
Keyword(s):  
Surface Area ◽  
Static Pressure ◽  
Total Efficiency ◽  
Centrifugal Fan ◽  
Recovery Coefficient ◽  
Fan Performance ◽  
Parameter Variations ◽  
Set Up ◽  
Spiral Casing ◽  
Pressure Recovery Coefficient

In this paper, the effect of the outlet surface area of the spiral casing on the performance of a centrifugal fan was investigated using open source CFD software OpenFOAM [1]. An automized loop with RANS and data post-processing is set up using Matlab, for allowing a large number of parameter variations. The effect was analyzed as a function of total pressure loss and static pressure recovery coefficient and on total efficiency as well.

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.

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.

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