Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants

Ozdemir, H.C.; Bedir, E.; Yilmaz, R.; Yağcı, M. Barış; Canadinç, D.

doi:10.1007/s10853-022-07363-w

Cited by 9 publications

(1 citation statement)

References 77 publications

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“…The development of multicomponent solders requires extensive research since binary Sn-based solders can no longer keep up with the rapid pace of technological advancement. In the form of HEAs, multicomponent alloys have exceptional qualities, particularly in terms of creep [315], magnetic behavior [316], biocompatibility [317], wear [318], and slow diffusion effect [311]. The use of HEA solder may impart these special characteristics to TSV/solder interconnections, opening a new design space and expanding the uses of solder bumps.…”

Section: Emerging Solder Bumps-heasmentioning

confidence: 99%

Advanced 3D Through-Si-Via and Solder Bumping Technology: A Review

Jang,

Sharma,

Jung

2023

Materials

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Three-dimensional (3D) packaging using through-Si-via (TSV) is a key technique for achieving high-density integration, high-speed connectivity, and for downsizing of electronic devices. This paper describes recent developments in TSV fabrication and bonding methods in advanced 3D electronic packaging. In particular, the authors have overviewed the recent progress in the fabrication of TSV, various etching and functional layers, and conductive filling of TSVs, as well as bonding materials such as low-temperature nano-modified solders, transient liquid phase (TLP) bonding, Cu pillars, composite hybrids, and bump-free bonding, as well as the role of emerging high entropy alloy (HEA) solders in 3D microelectronic packaging. This paper serves as a guideline enumerating the current developments in 3D packaging that allow Si semiconductors to deliver improved performance and power efficiency.

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Section: Emerging Solder Bumps-heasmentioning

confidence: 99%

Advanced 3D Through-Si-Via and Solder Bumping Technology: A Review

Jang,

Sharma,

Jung

2023

Materials

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Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Wan,

Li,

et al. 2023

Advanced Materials

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Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HEC), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic‐level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross‐validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of artificial intelligence‐assisted material exploration.This article is protected by copyright. All rights reserved

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