Evidence map›Paper›PMID 39187505›Full record

ArticleMicrosystems & nanoengineering2024

Correction of thermal airflow distortion in warpage measurements of microelectronic packaging structures via deep learning-based digital image correlation.

Yuhan Gao, Yuxin Chen, Ziniu Yu, Chuanguo Xiong, Xin Lei, Weishan Lv, Sheng Liu, Fulong Zhu

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2024. 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. Article
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

8 authors.

Yuhan GaoInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Yuxin ChenInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Ziniu YuInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Chuanguo XiongInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Xin LeiInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Weishan LvInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Sheng LiuInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Fulong ZhuInstitute of Microsystems, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China. zhufulong@hust.edu.cn.ORCID http://orcid.org/0000-0002-0756-9051

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52075208National Natural Science Foundation of China (National Science Foundation of China) U20A6004
6 · The paper itself

Abstract

The projected speckle-based three-dimensional digital image correlation method (3D-DIC) is being increasingly used in the reliability measurement of microelectronic packaging structures because of its noninvasive nature, high precision, and low cost. However, during the measurement of the thermal reliability of packaging structures, the thermal airflow generated by heating introduces distortions in the images captured by the DIC measurement system, impacting the accuracy and reliability of noncontact measurements. To address this challenge, a thermal airflow distortion correction model based on the transformer attention mechanism is proposed specifically for the measurement of thermal warpage in microelectronic packaging structures. This model avoids the oversmoothing issue associated with convolutional neural networks and the lack of physical constraints in generative adversarial networks, ensuring the precision of grayscale gradient changes in speckle patterns and minimizing adverse effects on DIC calculation accuracy. By inputting the distorted images captured by the DIC measurement system into the network, corrected images are obtained for 3D-DIC calculations, thus allowing the thermal warpage measurement results of the sample to be acquired. Through experiments measuring topography with customized step block specimens, the effectiveness of the proposed method in improving warpage measurement accuracy is confirmed; this is particularly true when captured images are affected by thermal airflow at 140 °C and 160 °C, temperatures commonly encountered in thermal reliability testing of packaging structures. The method successfully reduces the standard deviation from 9.829 to 5.943 µm and from 12.318 to 6.418 µm, respectively. The results demonstrate the substantial practical value of this method for measuring thermal warpage in microelectronic packaging structures.

Identifiers

PMID39187505
PMCPMC11347651

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.