Machine Learning and Data Mining Methods in Testing and Diagnostics of Analog and Mixed-Signal Integrated Circuits: Case Study-Reference-Cited by-同舟云学术

Machine Learning and Data Mining Methods in Testing and Diagnostics of Analog and Mixed-Signal Integrated Circuits: Case Study

Published:2019 Issue: Volume: Page:240-255
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Mosin Sergey^ORCID

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-5758-9_21

Reference14 articles.

1. Chakrabarti, S., Chatterjee, A.: Compact fault dictionary construction for efficient isolation of faults in analog and mixed-signal circuits. In: Proceedings of 20th Anniversary Conference on Advanced Research in VLSI (ARVLSI 1999), Atlanta, GA, pp. 327–341 (1999). https://doi.org/10.1109/ARVLSI.1999.756057

2. Aminian, M., Aminian, F.: A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor. IEEE Trans. Instrum. Meas. 56(5), 1546–1554 (2007). https://doi.org/10.1109/TIM.2007.904549

3. Huang, K., Stratigopoulos, H.G., Mir, S.: Fault diagnosis of analog circuits based on machine learning. In: Proceedings of Design, Automation and Test in Europe Conference and Exhibition (DATE 2010), Dresden, pp. 1761–1766 (2010). https://doi.org/10.1109/DATE.2010.5457099

4. Lang, R., Xu, Z., Gao, F.: Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration. J. Syst. Eng. Electron. 24(4), 706–712 (2013). https://doi.org/10.1109/JSEE.2013.00082

5. Sun, Y., Chen, G., Li, H.: Fuzzy clustering and SVM method for diagnosing analog circuits single soft fault. J. Comput.-Aided Des. Comput. Graph. 20(5), 612–617 (2008)

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