Title:
MACHINE LEARNING-BASED SEMICONDUCTOR MANUFACTURING YIELD PREDICTION SYSTEM AND METHOD
Document Type and Number:
WIPO Patent Application WO/2018/101722
Kind Code:
A2
Abstract:
Provided is a machine learning-based semiconductor manufacturing yield prediction system and method. A result prediction method according to an embodiment of the present invention comprises: learning different neural network models by classifying different types of data according to their types and respectively inputting the classified different types of data to the different neural network models; and predicting result values by classifying input data according to their types and respectively inputting the classified input data to different neural network models. Therefore, it is possible to apply different neural network models to respective data according to their types, thereby ensuring a neural network model having a structure appropriate for the characteristics of each type of data and thus accurately predicting a result value.
Inventors:
JUNG HANG DUK (KR)
MOON YONG SIK (KR)
SON MYUNG SEUNG (KR)
LEE MIN HWAN (KR)
PARK JUN TAEK (KR)
MOON YONG SIK (KR)
SON MYUNG SEUNG (KR)
LEE MIN HWAN (KR)
PARK JUN TAEK (KR)
Application Number:
PCT/KR2017/013764
Publication Date:
June 07, 2018
Filing Date:
November 29, 2017
Export Citation:
Assignee:
SK HOLDINGS CO LTD (KR)
International Classes:
G05B15/02; H01L21/66; G05B17/02
Other References:
See references of EP 3514823A4
Attorney, Agent or Firm:
YANG, Sung Hwan (KR)
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