Optimization of an Artificial Neural Network System for the Prediction of Failure Analysis Success
| Autor: | B.L. Yeoh, S.H. Goh, Hao Hu, MH Thor, Lin Zhao, Alan Tan, Jeffrey Lam, Y.H. Chan |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science 020208 electrical & electronic engineering Work (physics) Process (computing) Supply current 02 engineering and technology Condensed Matter Physics 01 natural sciences Atomic and Molecular Physics and Optics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Reliability engineering Power (physics) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Safety Risk Reliability and Quality Selection (genetic algorithm) |
| Zdroj: | Microelectronics Reliability. 92:136-142 |
| ISSN: | 0026-2714 |
| Popis: | It is well known that fail dies that exhibit obvious static power supply leakage current have a higher success of finding a defect, hence, a higher likelihood to be selected for failure analysis. When presented with choices, fail dies that exhibit similar supply current to reference are omitted. Valuable defect learnings are lost as a result. The feasibility of applying an Artificial Neural Network to predict failure analysis success has been demonstrated in a previous report. Besides automating the fail dies selection process, more importantly, dies which could yield valuable findings but neglected otherwise by convention, can be identified. We extend the previous proof-of-concept to study the effects of learning iterations, learning rate and the number of nodes on prediction accuracy in this work. More experimental results which include an actual case study will also be presented to substantiate the value of machine learning in this application. |
| Databáze: | OpenAIRE |
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