Evidence map›Paper›PMID 38931717›Full record

ArticleSensors (Basel, Switzerland)2024

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure.

Yuan Zhang, Chenghan Pu, Yanming Zhang, Muyuan Niu, Lifeng Hao, Jun Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yuan ZhangCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Chenghan PuCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0000-0003-1825-0393
Yanming ZhangThe 29th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China.
Muyuan NiuCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Lifeng HaoThe 29th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China.
Jun WangCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.ORCID 0000-0001-9223-2615

Funding

National Natural Science Foundation of China 92367301
6 · The paper itself

Abstract

Bonding distance is defined by the projected distance on a substrate plane between two solder points of a bonding wire, which can directly affect the morphology of the bonding wire and the performance between internal components of the chip. For the inspection of the bonding distance, it is necessary to accurately recognize gold wires and solder points within the complex imagery of the chip. However, bonding wires at arbitrary angles and small-sized solder points are densely distributed across the complex background of bonding images. These characteristics pose challenges for conventional image detection and deep learning methods to effectively recognize and measure the bonding distances. In this paper, we present a novel method to measure bonding distance using a hierarchical measurement structure. First, we employ an image acquisition device to capture surface images of integrated circuits and use multi-layer convolution to coarsely locate the bonding region and remove redundant background. Second, we apply a multi-branch wire bonding inspection network for detecting bonding spots and segmenting gold wire. This network includes a fine location branch that utilizes low-level features to enhance detection accuracy for small bonding spots and a gold wire segmentation branch that incorporates an edge branch to effectively extract edge information. Finally, we use the bonding distance measurement module to develop four types of gold wire distribution models for bonding spot matching. Together, these modules create a fully automated method for measuring bonding distances in integrated circuits. The effectiveness of the proposed modules and overall framework has been validated through comprehensive experiments.

Indexed as

bonding distancebonding spot detectiongold wire segmentationhierarchical measurementwire bonding

Identifiers

PMID38931717
PMCPMC11207810

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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.