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- Title
MICROSTRUCTURAL HIERARCHY DESCRIPTOR ENABLING INTERPRETATIVE AI FOR MICROELECTRONIC FAILURE ANALYSIS.
- Authors
Zhiheng Huang; Ziyan Liao; Kaiwen Zheng; Xin Zeng; Yuezhong Meng; Hui Yan; Yang Liu
- Abstract
This article proposes µSHD as a systematic and quantitative approach to spectra and image data in the field of MFA. The advantages of this approach lie in that it originates from the understanding of the mathematics behind AI and thus eliminates the large computation cost from DCNNs. Note that the focus of this article is only on understanding and benchmarking the role that µSHD plays in different MFA applications. Concrete routes for employing µSHD directly as the quantitative descriptor for supervised and unsupervised machine learning have been discussed. Manufacturers of MFA equipment can certainly utilize the µSHD tool to automate and improve their characterization techniques and image processing and analysis protocols. Moreover, the concept of structural hierarchy behind µSHD can help to gain deeper insights from the data from the perspective of systems.
- Subjects
FAILURE analysis; SUPERVISED learning; ARTIFICIAL intelligence; IMAGE analysis; IMAGE processing; MACHINE learning
- Publication
Electronic Device Failure Analysis, 2024, Vol 26, Issue 2, p10
- ISSN
1537-0755
- Publication type
Article
- DOI
10.31399/asm.edfa.2024-2.p010