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Appl. Sci. 2018, 8(2), 224; https://doi.org/10.3390/app8020224

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.
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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)
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Abstract

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

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