e-journal

A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics

---

Abstract
Root cause detecting and rapid yield ramping for advanced technology nodes are crucial to maintain competitive advantages for semiconductor manufacturing. Since the data structure is increasingly complicated in a fully automated wafer fabrication facility, it is difficult to diagnose the whole production system for fault detection. A number of approaches have been proposed for fault diagnosis and root cause detection. However, many constraints in real settings restrict the usage of conventional approaches, due to the big data with complicated data structure. In particular, a batch may not be
considered as a run in the present sub-batch processing system for wafer fabrication, in which the processing paths of the wafers in a batch could be different. Motivated by realistic needs, this paper aims to develop a root cause detection framework for the sub-batch processing system. Briefly, the proposed framework consists of three phases: data preparation, data dimension reduction, and the sub-batch processing model construction and evaluation. The proposed approach has been validated by a sequence of simulations and an empirical study conducted in a leading semiconductor manufacturing company in Taiwan. The results have shown practical viability of the proposed approach. Indeed, the developed approach is incorporated in the engineering data analysis system in this case company.

Index Terms—Sub-batch processing system, big data analytics, root cause detection, data mining, longitudinal data analysis.

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 27, NO. 4, NOVEMBER 2014

No. Panggil

-

Penerbit

: IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 27, NO. 4, NOVEMBER 2014

Bahasa

English

ISBN/ISSN

0894-6507

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

VOL. 27, NO. 4, NOVEMBER 2014

Subjek

ANALISIS DATA LONGITUDINAL
DATA MINING

Info Detail Spesifik

-

Pernyataan Tanggungjawab

ETY

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar