Home // ICAS 2018, The Fourteenth International Conference on Autonomic and Autonomous Systems // View article

Key Parameter Identification for Faulty Wafer Detection Using Image Processing

Authors:
Shu-Kai S. Fan
Du-Ming Tsai
Chih-Hung Jen
Rui-Yu Huang
Kuan-Lung Chen

Keywords: Semiconductor manufacturing, Key parameter identification, Image Processing, Fisher’s criterion

Abstract:
Nowadays, the semiconductor industry has become fully automated during the manufacturing process where abundant process parameters are collected on-line by sensor for the Fault Detection and Classification (FDC) purpose. To analyze these parameters and identify a smaller set of key parameters that have crucial influence on wafer quality must bring great benefits in stabling the manufacturing process and enhancing the production yield. Therefore, this article considers an alternative approach to use image processing techniques for analyzing the raw trace data. First, the one-dimensional time series data of a wafer batch was transformed into a two-dimensional image. Fisher’s Criterion (FC) ratios of the labelled good and defect wafer images are computed. The parameters that have high FC ratios are deemed the key parameters. The nine key parameters were identified by using the proposed image processing technique, which concurs with the technical experiences from the process engineers.

Pages: 19 to 22

Copyright: Copyright (c) IARIA, 2018

Publication date: May 20, 2018

Published in: conference

ISSN: 2308-3913

ISBN: 978-1-61208-634-7

Location: Nice, France

Dates: from May 20, 2018 to May 24, 2018