scholarly journals Degradation of leakage currents and reliability prediction for tantalum capacitors

2016 ◽  
Author(s):  
Alexander Teverovsky
Keyword(s):  
Reliability Prediction ◽  
Leakage Currents

Reliability Prediction Based on Field Failure Data of Guided Missile

2018 ◽  
Vol 18 (3) ◽  
pp. 250-259 ◽  
Author(s):  
Yangwoo Seo ◽  
Kyeshin Lee ◽  
Younho Lee ◽  
Jeyong Kim
Keyword(s):  
Reliability Prediction ◽  
Failure Data ◽  
Field Failure

Micro-Raman Spectroscopy Evaluation of the Local Mechanical Stress in Shallow Trench Isolation CMOS Structures: Correlation with Defect Generation and Diode Leakage

1998 ◽  
Author(s):  
I. De Wolf ◽  
G. Groeseneken ◽  
H.E. Maes ◽  
M. Bolt ◽  
K. Barla ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Mechanical Stress ◽  
Defect Formation ◽  
Active Area ◽  
Wet Oxidation ◽  
Leakage Currents ◽  
Shallow Trench Isolation ◽  
Micro Raman Spectroscopy ◽  
Silicon Devices ◽  
Trench Isolation

Abstract It is shown, using micro-Raman spectroscopy, that Shallow Trench Isolation introduces high stresses in the active area of silicon devices when wet oxidation steps are used. These stresses result in defect formation in the active area, leading to high diode leakage currents. The stress levels are highest near the outer edges of line structures and at square structures. They also increase with decreasing active area dimensions.

Comparison of Active and Passive Voltage Contrast for Failure Localization

2007 ◽  
Author(s):  
R. Rosenkranz ◽  
W. Werner
Keyword(s):  
Case Studies ◽  
Optical Beam ◽  
Leakage Currents ◽  
Localization Method ◽  
Voltage Contrast ◽  
Failure Localization

Abstract In many cases of failure localization, passive voltage contrast (PVC) localization method does not work, because it is not possible to charge up conducting structures which supposed to be dark in the SEM and FIB images. The reason for this is leakage currents. In this article, the authors show how they succeeded in overcoming these difficulties by the application of the active voltage contrast (AVC) method as it was described as biased voltage contrast by Campbell and Soden. They identified three main cases where the PVC didn't work but where they succeeded in failure localization with the AVC method. This is illustrated with the use of two case studies. Compared to the optical beam based methods the resolution is much better so a single failing contact of e.g. 70 nm technology can clearly be identified which cannot be done by TIVA or OBIRCH.

Thermal Laser Stimulation Technique for AlGaN/GaN HEMT Technologies Improvement

2013 ◽  
Author(s):  
Dominique Carisetti ◽  
Nicolas Sarazin ◽  
Nathalie Labat ◽  
Nathalie Malbert ◽  
Arnaud Curutchet ◽  
...  
Keyword(s):  
Surface States ◽  
Leakage Currents ◽  
Laser Stimulation ◽  
Long Term Stability ◽  
Field Plate ◽  
Gan Hemt ◽  
Die Surface ◽  
Scan Speed ◽  
Stimulation Technique

Abstract To improve the long-term stability of AlGaN/GaN HEMTs, the reduction of gate and drain leakage currents and electrical anomalies at pinch-off is required. As electron transport in these devices is both coupled with traps or surface states interactions and with polarization effects, the identification and localization of the preeminent leakage path is still challenging. This paper demonstrates that thermal laser stimulation (TLS) analysis (OBIRCh, TIVA, XIVA) performed on the die surface are efficient to localize leakage paths in GaN based HEMTs. The first part details specific parameters, such as laser scan speed, scan direction, wavelength, and laser power applied for leakage gate current paths identification. It compares results obtained with Visible_NIR electroluminescence analysis with the ones obtained by the TLS techniques on GaN HEMT structures. The second part describes some failure analysis case studies of AlGaN/GaN HEMT with field plate structure which were successful, thanks to the OBIRCh technique.

Using neural networks in reliability prediction

IEEE Software ◽  
1992 ◽  
Vol 9 (4) ◽  
pp. 53-59 ◽  
Author(s):  
N. Karunanithi ◽  
D. Whitley ◽  
Y.K. Malaiya
Keyword(s):  
Neural Networks ◽  
Reliability Prediction
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