Evidence map›Paper›PMID 41622243›Full record

ArticleMicrosystems & nanoengineering2026

Long-term high-temperature aging mechanism of copper-metallized through-glass vias: a combined nanoindentation test and hybrid Potts-phase field simulation study.

Junwei Chen, Zezhan Li, Bin Yang, Xiao Hu, Wenyu Li, Zichuan Li, Xuyang Yan, Zhoudong Yang, Jiao Liang, Guannan Yang and 5 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Junwei ChenShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Zezhan LiShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Bin YangState Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China.
Xiao HuEEMCS Faculty, Delft University of Technology, Delft, the Netherlands. x.hu-1@tudelft.nl.ORCID http://orcid.org/0000-0002-6139-2189
Wenyu LiShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Zichuan LiEEMCS Faculty, Delft University of Technology, Delft, the Netherlands.
Xuyang YanShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Zhoudong YangShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Jiao LiangShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Guannan YangState Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China.
Chao GuShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Changran ZhengShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China.
Chengqiang CuiState Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China. cqcui@gdut.edu.cn.
Guoqi ZhangEEMCS Faculty, Delft University of Technology, Delft, the Netherlands.
Jiajie FanShanghai Engineering Technology Research Center of SiC Power Device, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China. jiajie_fan@fudan.edu.cn.ORCID http://orcid.org/0000-0001-5400-737X

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52275559
6 · The paper itself

Abstract

The reliability of through-glass via (TGV) interconnects is critical for advanced semiconductor packaging. This work investigates microstructural and mechanical evolution in electroplated TGV-Cu subjected to long-term aging at 250 °C. TGV samples were fabricated via laser-induced etching and double-sided copper electroplating, then aged for up to 1008 h. Nanoindentation revealed region-dependent reductions in hardness (from 2.0-2.5 GPa to below 0.5 GPa) and modulus (from 110-130 GPa to 40-90 GPa), with surface-near regions most affected. The glass substrate maintained stable mechanical properties until microcracks formed after 1008 h. EBSD quantification showed grain-size enlargement from 0.46 µm to 1.86 µm and a concurrent decrease in dislocation density. Molecular dynamics simulations of 3, 4, 5 nm grains corroborated the inverse relationship between grain size and micro-mechanical properties. A hybrid Potts-phase field model further linked grain coarsening to stress relaxation and elastic-energy minimization, revealing that as grains grow, the overall von Mises stress in the structure decreases; high-modulus grains retain relatively higher local stresses, while low-modulus, low-stress grains exhibit faster growth rates. Electrical I-V measurements confirmed stable ohmic behavior, despite a drop in insulation resistance. These integrated experimental and computational insights provide theoretical guidance for optimizing TGV interposer design and ensuring long-term operational reliability in heterogeneous integration technologies.

Identifiers

PMID41622243
PMCPMC12862051

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.