Conference proceeding
A supervised machine learning application in volume diagnosis
2019 IEEE European Test Symposium (ETS), Vol.2019-, pp.1-6
05/2019
DOI: 10.1109/ETS.2019.8791553
Abstract
Volume diagnosis has been used effectively to identify systematic defects for yield learning. Root cause deconvolution (RCD), an unsupervised machine learning technique which uses volume diagnosis data, has proven very effective for identifying root causes. As we march towards more advanced technology nodes, defects have more complicated behaviors rendering some model parameters used in RCD are not precise enough to be effective. In this paper we use a supervised machine learning technique to accurately learn these model parameters from training data. Controlled experiments using simulation data on several industrial designs show that our approach improves RCD accuracy. We also demonstrate that the approach correctly predicts 71% of the systematic defects in 21 cases validated by physical failure analysis of real silicon, which is a significantly better result compared to using the original parameters.
Details
- Title: Subtitle
- A supervised machine learning application in volume diagnosis
- Creators
- Yue Tian - University of IowaGaurav Veda - Mentor GraphicsWu-Tung Cheng - Mentor GraphicsManish Sharma - Mentor GraphicsHuaxing Tang - Mentor GraphicsNeerja Bawaskar - GlobalFoundriesSudhakar M Reddy - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE European Test Symposium (ETS), Vol.2019-, pp.1-6
- DOI
- 10.1109/ETS.2019.8791553
- ISSN
- 1530-1877
- eISSN
- 1558-1780
- Publisher
- IEEE
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197454902771
Metrics
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