research-article

Research on TSV Void Defects Based on Machine Learning

Authors:

Shouhong Chen,

Huaiqiang Kang,

Jun Ma,

Ling Guo,

Xingna HouAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 75, Pages 1 - 4

https://doi.org/10.1145/3331453.3360965

Published: 22 October 2019 Publication History

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Abstract

With the rapid development of 3D TSV (through silicon via) technology, it is particularly important to improve the yield for TSV fault detection. Aiming at TSV void defects, the paper adopts supervised machine learning method to train S parameters in TSV model with void faults, and carries out classification processing, then predicts the size of void faults through stimulus signal and S parameters. The results show that for spherical void defects detection, the classification accuracy of ELM (Extreme Learning Machine) algorithm and KNN (K-Nearest Neighbor) algorithm is above 85%, while for TSV cylindrical void defects detection, the classification accuracy of ELM algorithm is 96%.

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Shang YGeng LLi CSong ZZhang JLi J(2025)Research on Multifault Testing Method for MIV Based on Grid Search and Random ForestIEEE Transactions on Components, Packaging and Manufacturing Technology10.1109/TCPMT.2025.353051915:2(319-327)Online publication date: Feb-2025
https://doi.org/10.1109/TCPMT.2025.3530519

Index Terms

Research on TSV Void Defects Based on Machine Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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Published In

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Shang YGeng LLi CSong ZZhang JLi J(2025)Research on Multifault Testing Method for MIV Based on Grid Search and Random ForestIEEE Transactions on Components, Packaging and Manufacturing Technology10.1109/TCPMT.2025.353051915:2(319-327)Online publication date: Feb-2025
https://doi.org/10.1109/TCPMT.2025.3530519

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Cited By

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Recommendations

TSV open defects in 3D integrated circuits: characterization, test, and optimal spare allocation

Comparing Through-Silicon-Via (TSV) Void/Pinhole Defect Self-Test Methods

TSV redundancy: architecture and design issues in 3D IC

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