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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationSmart Grid and Internet of Things - 4th EAI International Conference, SGIoT 2020, Proceedings
EditorsYi-Bing Lin, Der-Jiunn Deng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages527-542
Number of pages16
ISBN (Print)9783030695132
DOIs
StatePublished - 2021
Event4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020 - TaiChung, Taiwan
Duration: 5 Dec 20206 Dec 2020

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume354
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference4th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2020
CountryTaiwan
CityTaiChung
Period5/12/206/12/20

Keywords

  • DRAM
  • Genetic algorithms
  • Neural networks
  • Response surface method

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