Early product learning
The Early Product Learning (EPL) was designed to improve the yield for ICs by understanding the current reject trends and thus minimize the customer return spills. This project mainly focuses on the analysis of electrical failure. There are mainly four main phases in every failure analysis project....
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sg-ntu-dr.10356-760832023-07-04T15:39:54Z Early product learning Chandran, Karthik Zhou Xing School of Electrical and Electronic Engineering Technical University of Munich DRNTU::Engineering::Electrical and electronic engineering The Early Product Learning (EPL) was designed to improve the yield for ICs by understanding the current reject trends and thus minimize the customer return spills. This project mainly focuses on the analysis of electrical failure. There are mainly four main phases in every failure analysis project. The first phase is non-destructive analysis where the analyst receive the reject sample and perform analysis such as external visual inspection, X ray imaging, acoustic imaging etc. which helps to learn as much about the sample without altering it in anyway. The next phase is the defect localization. Here the analyst replicates the failing condition reported by the customer. Without localizing the failure, the project cannot continue, since there is no evidence to show that a defect even exists. Once the fault is confirmed, the next phase is fault isolation, where the analyst do an in-depth destructive physical analysis and examination. Finally, destructive analysis and documentation which serve as the culmination of all the data collection from which an analyst will perform delayering, a cross-section, or other destructive technique to reveal the defect in its entirety and identify its most likely cause. Various advanced semiconductor failure analysis techniques were used, including Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), Curve Tracer, Scanning Acoustic Topography (SAT) and X-ray inspection among others to isolate the root cause of electrical failure in multiple types of devices based on varied process technologies. Master of Science (Green Electronics) 2018-10-24T03:44:10Z 2018-10-24T03:44:10Z 2018 Thesis http://hdl.handle.net/10356/76083 en 81 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Chandran, Karthik Early product learning |
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The Early Product Learning (EPL) was designed to improve the yield for ICs by understanding the current reject trends and thus minimize the customer return spills. This project mainly focuses on the analysis of electrical failure. There are mainly four main phases in every failure analysis project. The first phase is non-destructive analysis where the analyst receive the reject sample and perform analysis such as external visual inspection, X ray imaging, acoustic imaging etc. which helps to learn as much about the sample without altering it in anyway. The next phase is the defect localization. Here the analyst replicates the failing condition reported by the customer. Without localizing the failure, the project cannot continue, since there is no evidence to show that a defect even exists. Once the fault is confirmed, the next phase is fault isolation, where the analyst do an in-depth destructive physical analysis and examination. Finally, destructive analysis and documentation which serve as the culmination of all the data collection from which an analyst will perform delayering, a cross-section, or other destructive technique to reveal the defect in its entirety and identify its most likely cause.
Various advanced semiconductor failure analysis techniques were used, including Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), Curve Tracer, Scanning Acoustic Topography (SAT) and X-ray inspection among others to isolate the root cause of electrical failure in multiple types of devices based on varied process technologies. |
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Zhou Xing |
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Zhou Xing Chandran, Karthik |
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Theses and Dissertations |
author |
Chandran, Karthik |
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Chandran, Karthik |
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Early product learning |
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Early product learning |
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Early product learning |
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Early product learning |
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Early product learning |
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early product learning |
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2018 |
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http://hdl.handle.net/10356/76083 |
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1772825601081606144 |