Wafer Pattern Recognition for Detecting Process Abnormalities in NAND Flash Memory Manufacturing

Jeongin Choe, Taehyeon Kim, Saetbyeol Yoon, Sangyong Yoon, Ki Whan Song, Jai Hyuk Song, Myungsuk Kim, Woo Young Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We have adopted various defect detection systems in the front stage of manufacturing in order to effectively manage the quality of flash memory products. In this paper, we propose an intelligent pattern recognition methodology which enables us to discriminate abnormal wafer automatically in the course of NAND flash memory manufacturing. Our proposed technique consists of the two steps: pre-processing and hybrid clustering. The pre-processing step based on process primitives efficiently eliminates noisy data. Then, the hybrid clustering step dramatically reduces the total amount of computing, which makes our technique practical for the mass production of NAND flash memory.

Original languageEnglish
Title of host publicationISTFA 2021
Subtitle of host publicationProceedings from the 47th International Symposium for Testing and Failure Analysis Conference
PublisherASM International
Pages406-409
Number of pages4
ISBN (Electronic)9781627084215
DOIs
StatePublished - 2021
Event47th International Symposium for Testing and Failure Analysis Conference, ISTFA 2021 - Phoenix, United States
Duration: 31 Oct 20214 Nov 2021

Publication series

NameConference Proceedings from the International Symposium for Testing and Failure Analysis
Volume2021-October

Conference

Conference47th International Symposium for Testing and Failure Analysis Conference, ISTFA 2021
Country/TerritoryUnited States
CityPhoenix
Period31/10/214/11/21

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