Adaptive Online Time-Series Prediction for Virtual Metrology in Semiconductor Manufacturing

Simon Zabrocki, Pil Sung Jo, Chan Park, Dongkyun Yim, Sunghee Yun, Byung Jun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a novel highly accurate virtual metrology (VM) method based on adaptive online time-series learning. The method comprises a novel time-series prediction algorithm robust against data drifts and shifts observed in semi-conductor manufacturing. The prediction algorithm incorporates time-aware data normalization and adaptive online learning. The time-aware normalizer transforms the data to suppress the effect of drifts. The adaptive online learner captures the time-varying relationship between response and inputs caused by shifts. On both simulated and real process data our approach outperforms conventional VM approaches. When applied to advanced process control (APC) on high-volume production lines at a major semiconductor manufacturer, our VM method substantially reduced variability of process outcomes.

Original languageEnglish
Title of host publication2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665456395
DOIs
Publication statusPublished - 2023
Event34th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2023 - Saratoga Springs, United States
Duration: 2023 May 12023 May 4

Publication series

NameASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
Volume2023-May
ISSN (Print)1078-8743

Conference

Conference34th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2023
Country/TerritoryUnited States
CitySaratoga Springs
Period23/5/123/5/4

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • adaptive learning algorithm
  • advanced process control (APC)
  • data drifts and shifts
  • time-series prediction
  • virtual metrology (VM)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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