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"Artificial neural network"

A Review on Evaluation of Elastic Modulus Using Nanoindentation
Seo Hyeon Jang, Oh Min Kwon, Si Hyun Park, Hyun Wook Cho, Jong-hyoung Kim
J Korean Inst Electr Electron Mater Eng 2025;38(3):247-253.   Published online May 1, 2025
DOI: https://doi.org/10.4313/JKEM.2025.38.3.2
This review examines the principles, limitations, and recent advancements in elastic modulus measurement using nanoindentation. The importance of accurate contact area prediction is discussed, along with the Oliver-Pharr method and its limitations. The Continuous Stiffness Measurement (CSM) technique is presented as a significant improvement, allowing continuous measurement of mechanical properties throughout the indentation process. For ultra-thin films, the Li and Vlassak method, which incorporates Yu's solution and the concept of effective thickness, is highlighted as a means to correct for substrate effects. Recent developments in artificial neural network-based models for elastic modulus prediction are also explored. These advancements have greatly expanded the applicability of nanoindentation in semiconductor and MEMS device reliability assessment.

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  • Material characterization and local mechanical study with nanoindentation on the stainless steel 316L part with embedded optical fiber sensor fabricated with electrical field-assisted sintering technique for severe environment application
    Wei Li, Minoo Tayefeh Kazemi, Afreeda Hossain, Hongbing Lu
    Materials Characterization.2026; 240: 116959.     CrossRef
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