A study on process control to improve yield in semiconductor manufacturing

Author

Choi, Mun-Kyu ; Kim, Hun-Mo

Author_Institution

Dept. of Mech. Eng., Sungkunkwan Univ., Suwon, South Korea

fYear

1999

fDate

36373

Firstpage

1215

Lastpage

1219

Abstract

We present the process analysis system that can analyze causes, like an expert, after processes. Also, the plasma etching process that affects yield is controlled using an artificial neural network to predict output before the process. In modeling, a method that uses history for input data is considered, it offers advantages in both learning and prediction capability. This research regards the critical dimension that is considerable in highly integrated circuits as the output variable of the model. Based on a model using this method, we propose an algorithm to analyze and control the effect of input variables for predicted defects. Both the weight of input variables and their historical trend are examined for this algorithm

Keywords

integrated circuit yield; neural nets; process control; sputter etching; artificial neural network; learning; plasma etching process; prediction capability; process analysis system; semiconductor manufacturing; yield; Algorithm design and analysis; Artificial neural networks; Etching; History; Input variables; Integrated circuit modeling; Integrated circuit yield; Plasma applications; Predictive models; Process control;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual, 1999. 38th Annual Conference Proceedings of the

Conference_Location

Morioka

Print_ISBN

4-907764-13-8

Type

conf

DOI

10.1109/SICE.1999.788727

Filename

788727