Root Cause Identification of Power System Faults using Waveform Analytics

2018 ◽  
Author(s):  
Mahfuz Ali Shuvra ◽  
Alberto Del Rosso
Keyword(s):  
Power System ◽  
Root Cause ◽  
Power System Faults ◽  
Root Cause Identification

Analysis of Leakage Failures in Flash Memory Devices and Root Cause Identification

2000 ◽  
Author(s):  
J. N. C. de Luna ◽  
M. O. del Fierro ◽  
J. L. Muñoz
Keyword(s):  
Flash Memory ◽  
Electromagnetic Interference ◽  
Yield Loss ◽  
High Amplitude ◽  
Physical Analysis ◽  
Input Buffer ◽  
Automated Test Equipment ◽  
Root Cause ◽  
Root Cause Identification ◽  
Test Process

Abstract An advanced flash bootblock device was exceeding current leakage specifications on certain pins. Physical analysis showed pinholes on the gate oxide of the n-channel transistor at the input buffer circuit of the affected pins. The fallout contributed ~1% to factory yield loss and was suspected to be caused by electrostatic discharge or ESD somewhere in the assembly and test process. Root cause investigation narrowed down the source to a charged core picker inside the automated test equipment handlers. By using an electromagnetic interference (EMI) locator, we were able to observe in real-time the high amplitude electromagnetic pulse created by this ESD event. Installing air ionizers inside the testers solved the problem.

A New Approach for SRAM Soft Defect Root Cause Identification

2007 ◽  
Author(s):  
Peter Egger ◽  
Stefan Müller ◽  
Martin Stiftinger
Keyword(s):  
Integrated Circuits ◽  
Top Down ◽  
Feature Size ◽  
Design Rules ◽  
New Approach ◽  
Root Cause ◽  
Wafer Fab ◽  
Sram Cell ◽  
Tcad Simulation ◽  
Root Cause Identification

Abstract With shrinking feature size of integrated circuits traditional FA techniques like SEM inspection of top down delayered devices or cross sectioning often cannot determine the physical root cause. Inside SRAM blocks the aggressive design rules of transistor parameters can cause a local mismatch and therefore a soft fail of a single SRAM cell. This paper will present a new approach to identify a physical root cause with the help of nano probing and TCAD simulation to allow the wafer fab to implement countermeasures.

Fuzzified time-frequency method for identification and localization of power system faults

2021 ◽  
pp. 1-13
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Power System ◽  
High Accuracy ◽  
Fuzzy Decision ◽  
Frequency Method ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Different Types ◽  
Power System Faults ◽  
Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

Advancement in Data Engineering and Feature Processing Workflow by Using Deep Learning Techniques for the Automation of ESP Failure Root Cause Analyses

2021 ◽  
Author(s):  
Saniya Karnik ◽  
Navya Yenuganti ◽  
Bonang Firmansyah Jusri ◽  
Supriya Gupta ◽  
Prasanna Nirgudkar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Failure Analysis ◽  
Language Processing ◽  
Turnaround Time ◽  
Process Efficiency ◽  
Root Cause ◽  
Feature Processing ◽  
Learning Techniques ◽  
Electrical Submersible Pump ◽  
Root Cause Identification

Abstract Today, Electrical Submersible Pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity involving dismantle, inspection, and failure analysis (DIFA) for each failure. This paper presents a novel artificial intelligence workflow using an ensemble of machine learning (ML) algorithms coupled with natural language processing (NLP) and deep learning (DL). The algorithms outlined in this paper bring together structured and unstructured data across equipment, production, operations, and failure reports to automate root cause identification and analysis post breakdown. This process will result in reduced turnaround time (TAT) and human effort thus drastically improving process efficiency.

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