Next Article in Journal
Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements
Next Article in Special Issue
A Multi-Usable Cloud Service Platform: A Case Study on Improved Development Pace and Efficiency
Previous Article in Journal
Theoretical Analysis of Directly Modulated Reflective Semiconductor Optical Amplifier Performance Enhancement by Microring Resonator-Based Notch Filtering
Previous Article in Special Issue
Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(2), 224;

Kernel-Density-Based Particle Defect Management for Semiconductor Manufacturing Facilities

Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Those authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 2 January 2018 / Revised: 20 January 2018 / Accepted: 28 January 2018 / Published: 1 February 2018
(This article belongs to the Special Issue Smart Sustainable Manufacturing Systems)
Full-Text   |   PDF [5488 KB, uploaded 1 February 2018]   |  


In a semiconductor manufacturing process, defect cause analysis is a challenging task because the process includes consecutive fabrication phases involving numerous facilities. Recently, in accordance with the shrinking chip pitches, fabrication (FAB) processes require advanced facilities and designs for manufacturing microcircuits. However, the sizes of the particle defects remain constant, in spite of the increasing modernization of the facilities. Consequently, this increases the particle defect ratio. Therefore, this study proposes a particle defect management method for the reduction of the defect ratio. The proposed method provides a kernel-density-based particle map that can overcome the limitations of the conventional method. The method consists of two phases. The first phase is the acquisition of cumulative coordinates of the defect locations on the wafer using the FAB database. Subsequently, this cumulative data is used to generate a particle defect map based on the estimation of kernel density; this map establishes the advanced monitoring statistics. In order to validate this method, we conduct an experiment for comparison with the previous industrial method. View Full-Text
Keywords: kernel density estimation; particle defect management; particle map; semiconductor manufacturing process kernel density estimation; particle defect management; particle map; semiconductor manufacturing process

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Park, S.H.; Kim, S.; Baek, J.-G. Kernel-Density-Based Particle Defect Management for Semiconductor Manufacturing Facilities. Appl. Sci. 2018, 8, 224.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top