Package yield enhancement using machine learning in semiconductor manufacturing

Hyoung Gun Kim; Young Shin Han; Jee Hyong Lee

doi:10.1109/IAEAC.2015.7428567

Package yield enhancement using machine learning in semiconductor manufacturing

Hyoung Gun Kim
, Young Shin Han
, Jee Hyong Lee

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

The production difficulty of the DRAM family has rapidly increasing. Several factors can be adduced to explain. For example, increasing of step in the production process by the miniaturization of semiconductor process, challenges of Low-power designed due to the growth of mobile family. And the two are connected by a combination of the result. It may increase the difficulty of an analysis of the test area for the yield improvement. This study analyzed the Fab measurement Data, Wafer Test Data, Package Test Data and looked at Package Yield Improvement. It covered Data pre-processing, Feature Selection, and then verify the relevant data, to derive an optimized relationship, and the actual production process applied too.

Original language	English
Title of host publication	Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	316-320
Number of pages	5
ISBN (Electronic)	9781479919796
DOIs	https://doi.org/10.1109/IAEAC.2015.7428567
State	Published - 7 Mar 2016
Event	IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 - Chongqing, China Duration: 19 Dec 2015 → 20 Dec 2015

Publication series

Name	Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

Conference

Conference	IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015
Country/Territory	China
City	Chongqing
Period	19/12/15 → 20/12/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Feature Selection
High Speed Test
Machine Learning
Package Yield
Yield Prediction

Access to Document

10.1109/IAEAC.2015.7428567

Cite this

Gun Kim, H., Han, Y. S., & Lee, J. H. (2016). Package yield enhancement using machine learning in semiconductor manufacturing. In B. Xu (Ed.), Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 (pp. 316-320). Article 7428567 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAEAC.2015.7428567

Gun Kim, Hyoung ; Han, Young Shin ; Lee, Jee Hyong. / Package yield enhancement using machine learning in semiconductor manufacturing. Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 316-320 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015).

@inproceedings{9457b6bf0d5c428e9ee279f107859921,

title = "Package yield enhancement using machine learning in semiconductor manufacturing",

abstract = "The production difficulty of the DRAM family has rapidly increasing. Several factors can be adduced to explain. For example, increasing of step in the production process by the miniaturization of semiconductor process, challenges of Low-power designed due to the growth of mobile family. And the two are connected by a combination of the result. It may increase the difficulty of an analysis of the test area for the yield improvement. This study analyzed the Fab measurement Data, Wafer Test Data, Package Test Data and looked at Package Yield Improvement. It covered Data pre-processing, Feature Selection, and then verify the relevant data, to derive an optimized relationship, and the actual production process applied too.",

keywords = "Feature Selection, High Speed Test, Machine Learning, Package Yield, Yield Prediction",

author = "\{Gun Kim\}, Hyoung and Han, \{Young Shin\} and Lee, \{Jee Hyong\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 ; Conference date: 19-12-2015 Through 20-12-2015",

year = "2016",

month = mar,

day = "7",

doi = "10.1109/IAEAC.2015.7428567",

language = "English",

series = "Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "316--320",

editor = "Bing Xu",

booktitle = "Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015",

}

Gun Kim, H, Han, YS & Lee, JH 2016, Package yield enhancement using machine learning in semiconductor manufacturing. in B Xu (ed.), Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015., 7428567, Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 316-320, IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015, Chongqing, China, 19/12/15. https://doi.org/10.1109/IAEAC.2015.7428567

Package yield enhancement using machine learning in semiconductor manufacturing. / Gun Kim, Hyoung; Han, Young Shin; Lee, Jee Hyong.
Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 316-320 7428567 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Package yield enhancement using machine learning in semiconductor manufacturing

AU - Gun Kim, Hyoung

AU - Han, Young Shin

AU - Lee, Jee Hyong

PY - 2016/3/7

Y1 - 2016/3/7

N2 - The production difficulty of the DRAM family has rapidly increasing. Several factors can be adduced to explain. For example, increasing of step in the production process by the miniaturization of semiconductor process, challenges of Low-power designed due to the growth of mobile family. And the two are connected by a combination of the result. It may increase the difficulty of an analysis of the test area for the yield improvement. This study analyzed the Fab measurement Data, Wafer Test Data, Package Test Data and looked at Package Yield Improvement. It covered Data pre-processing, Feature Selection, and then verify the relevant data, to derive an optimized relationship, and the actual production process applied too.

AB - The production difficulty of the DRAM family has rapidly increasing. Several factors can be adduced to explain. For example, increasing of step in the production process by the miniaturization of semiconductor process, challenges of Low-power designed due to the growth of mobile family. And the two are connected by a combination of the result. It may increase the difficulty of an analysis of the test area for the yield improvement. This study analyzed the Fab measurement Data, Wafer Test Data, Package Test Data and looked at Package Yield Improvement. It covered Data pre-processing, Feature Selection, and then verify the relevant data, to derive an optimized relationship, and the actual production process applied too.

KW - Feature Selection

KW - High Speed Test

KW - Machine Learning

KW - Package Yield

KW - Yield Prediction

UR - https://www.scopus.com/pages/publications/84966440592

U2 - 10.1109/IAEAC.2015.7428567

DO - 10.1109/IAEAC.2015.7428567

M3 - Conference contribution

AN - SCOPUS:84966440592

T3 - Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

SP - 316

EP - 320

BT - Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

Y2 - 19 December 2015 through 20 December 2015

ER -

Gun Kim H, Han YS, Lee JH. Package yield enhancement using machine learning in semiconductor manufacturing. In Xu B, editor, Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 316-320. 7428567. (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015). doi: 10.1109/IAEAC.2015.7428567

Package yield enhancement using machine learning in semiconductor manufacturing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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