Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products

Yun-Wei Lin; Chia Ming Lin

doi:10.1007/978-3-030-69514-9_40

Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products

Yun-Wei Lin^*, Chia Ming Lin

^*Corresponding author for this work

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

Abstract

Dynamic random access memory (DRAM) products are the key parts in consumer products. To fulfill the current market’s strict specifications, various customers have asked DRAM manufacturers to continue improving the quality of DRAM products. The resistance of the Ti film directly affects the electrical quality of DRAM products. At present, the DRAM products developed by the case company have caused customer returns due to abnormal resistance value of Ti film. Process engineers always adjust the engineering parameters based on experience, which resulted in slow improvement and inability to determine the setting of engineering parameters. Consequently, shipments of DRAM products are delayed. This study adopts the Ti film resistance of DRAM products as the main research object for improvement and applies the response surface method, neural networks, and genetic algorithms to help process engineers analyze and improve DRAM products. This work assists the case company in achieving a significant improvement in Ti film resistance from 210.33 Ω (the origin made by the case company) to 185.28 Ω (the improvement made by this work) where the specified target value is 185 Ω. The results are effective in shortening the improvement time and reducing customer returns.

Original language	English
Title of host publication	Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings
Editors	Yi-Bing Lin, Der-Jiunn Deng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	527-542
Number of pages	16
ISBN (Print)	9783030695132
DOIs	https://doi.org/10.1007/978-3-030-69514-9_40
State	Published - 5 Dec 2021
Event	4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020 - TaiChung, Taiwan Duration: 5 Dec 2020 → 6 Dec 2020

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	354
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020
Country/Territory	Taiwan
City	TaiChung
Period	5/12/20 → 6/12/20

Keywords

DRAM
Genetic algorithms
Neural networks
Response surface method

Access to Document

10.1007/978-3-030-69514-9_40

Cite this

Lin, Y.-W., & Lin, C. M. (2021). Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products. In Y.-B. Lin, & D.-J. Deng (Eds.), Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings (pp. 527-542). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 354). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-69514-9_40

Lin, Yun-Wei ; Lin, Chia Ming. / Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products. Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings. editor / Yi-Bing Lin ; Der-Jiunn Deng. Springer Science and Business Media Deutschland GmbH, 2021. pp. 527-542 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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title = "Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products",

abstract = "Dynamic random access memory (DRAM) products are the key parts in consumer products. To fulfill the current market{\textquoteright}s strict specifications, various customers have asked DRAM manufacturers to continue improving the quality of DRAM products. The resistance of the Ti film directly affects the electrical quality of DRAM products. At present, the DRAM products developed by the case company have caused customer returns due to abnormal resistance value of Ti film. Process engineers always adjust the engineering parameters based on experience, which resulted in slow improvement and inability to determine the setting of engineering parameters. Consequently, shipments of DRAM products are delayed. This study adopts the Ti film resistance of DRAM products as the main research object for improvement and applies the response surface method, neural networks, and genetic algorithms to help process engineers analyze and improve DRAM products. This work assists the case company in achieving a significant improvement in Ti film resistance from 210.33 Ω (the origin made by the case company) to 185.28 Ω (the improvement made by this work) where the specified target value is 185 Ω. The results are effective in shortening the improvement time and reducing customer returns.",

keywords = "DRAM, Genetic algorithms, Neural networks, Response surface method",

author = "Yun-Wei Lin and Lin, {Chia Ming}",

note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020 ; Conference date: 05-12-2020 Through 06-12-2020",

year = "2021",

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Lin, Y-W & Lin, CM 2021, Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products. in Y-B Lin & D-J Deng (eds), Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 354, Springer Science and Business Media Deutschland GmbH, pp. 527-542, 4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020, TaiChung, Taiwan, 5/12/20. https://doi.org/10.1007/978-3-030-69514-9_40

Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products. / Lin, Yun-Wei; Lin, Chia Ming.
Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings. ed. / Yi-Bing Lin; Der-Jiunn Deng. Springer Science and Business Media Deutschland GmbH, 2021. p. 527-542 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 354).

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

TY - GEN

T1 - Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products

AU - Lin, Yun-Wei

AU - Lin, Chia Ming

PY - 2021/12/5

Y1 - 2021/12/5

N2 - Dynamic random access memory (DRAM) products are the key parts in consumer products. To fulfill the current market’s strict specifications, various customers have asked DRAM manufacturers to continue improving the quality of DRAM products. The resistance of the Ti film directly affects the electrical quality of DRAM products. At present, the DRAM products developed by the case company have caused customer returns due to abnormal resistance value of Ti film. Process engineers always adjust the engineering parameters based on experience, which resulted in slow improvement and inability to determine the setting of engineering parameters. Consequently, shipments of DRAM products are delayed. This study adopts the Ti film resistance of DRAM products as the main research object for improvement and applies the response surface method, neural networks, and genetic algorithms to help process engineers analyze and improve DRAM products. This work assists the case company in achieving a significant improvement in Ti film resistance from 210.33 Ω (the origin made by the case company) to 185.28 Ω (the improvement made by this work) where the specified target value is 185 Ω. The results are effective in shortening the improvement time and reducing customer returns.

AB - Dynamic random access memory (DRAM) products are the key parts in consumer products. To fulfill the current market’s strict specifications, various customers have asked DRAM manufacturers to continue improving the quality of DRAM products. The resistance of the Ti film directly affects the electrical quality of DRAM products. At present, the DRAM products developed by the case company have caused customer returns due to abnormal resistance value of Ti film. Process engineers always adjust the engineering parameters based on experience, which resulted in slow improvement and inability to determine the setting of engineering parameters. Consequently, shipments of DRAM products are delayed. This study adopts the Ti film resistance of DRAM products as the main research object for improvement and applies the response surface method, neural networks, and genetic algorithms to help process engineers analyze and improve DRAM products. This work assists the case company in achieving a significant improvement in Ti film resistance from 210.33 Ω (the origin made by the case company) to 185.28 Ω (the improvement made by this work) where the specified target value is 185 Ω. The results are effective in shortening the improvement time and reducing customer returns.

KW - DRAM

KW - Genetic algorithms

KW - Neural networks

KW - Response surface method

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SN - 9783030695132

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

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BT - Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings

A2 - Lin, Yi-Bing

A2 - Deng, Der-Jiunn

PB - Springer Science and Business Media Deutschland GmbH

T2 - 4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020

Y2 - 5 December 2020 through 6 December 2020

ER -

Lin YW, Lin CM. Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products. In Lin YB, Deng DJ, editors, Smart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 527-542. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-69514-9_40

Optimization of the Deposition Condition for Improving the Ti Film Resistance of DRAM Products

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