반도체공정 이상탐지 및 클러스터링을 위한 심볼릭 표현법의 적용 = Application of Symbolic Representation Method for Fault Detection and Clustering in Semiconductor Fabrication Processes

국문 초록 (Abstract)

반도체(semiconductor) 기술은 1950년대에 집적 회로(integrated circuit, IC)가 발명된 이후 오늘날까지 급속한 발전을 거듭하고 있다. 하나의 완전한 반도체를 제조하기 위해서는 매우 다양하고 긴공정을 거쳐야 한다. 반도체 제조 생산성을 높이기 위하여 공정들이 종료되기 전에 미리 이상(fault)을 발견하기 위한 이상탐지 및 분류(fault detection and classification, FOC)에 대한 많은 연구가 진행되고 있다. 이를 위하여 다양한 반도체 장비에 갖가지 종류의 센서를 부착하여 일정한 시간 간격으로 원하는 값을 측정한다. 이러한 측정 값은 실수 값들의 연속이므로 시계열(time- seIies) 데이터의 일종이다. 본 논문에서는 반도체 공정에서의 이상탐지 및 클러스터링을 수행하는 알고리즘을 제안한다. 제안된 알고리즘은 시계열 데이터를 심불릭 표현법(symbolic representation) 으로 변환하여 이상을 탐지하는 기존의 알고리즘을 수정한 것이다. 본 논문의 공헌은 일반적인 시계열 데이터에 대한 기존의 이상탐지 알고리즘을 수정하여 반도체 공정 데이터에 대해서도 활용할 수 있음을 보일 뿐만 아니라, 이상탐지 및 클러스터링의 정확성을 높이는 실험 결과를 제시하는 것이다. 실험 결과, 본 논문에서 제안한 알고리즘은 긍정 오류(false positive) 및 부정 오류(false negative)를 모두 발생하지 않았다.

다국어 초록 (Multilingual Abstract)

Since the invention of the integrated circuit (IC) in 1950s, semiconductor technology has undergone dramatic development up to these days. A complete semiconductor is manufactured through a diversity of processes. For better semiconductor productivity, fault detection and classification (FDC) has been rigorously studied for finding faults even before the processes are completed. For FDC, various kinds of sensors are attached in many semiconductor manufacturing devices, and sensor values are collected in a periodic manner. The collection of scnsor values consists of sequences of real numbers, and hence is regarded as a kind of time-series data. In this paper, we propose an algorithm for dctecting and clustering faults in semiconductor processes. The proposed algorithm is a modification of the existing anomaly detection algorithm dealing with symbolically-represented time-series. The contributions of this paper are: (1) showing that a modification of the existing anomaly detection algorithm dealing with general time-series could be used for semiconductor process data and (2) presenting experimental results for improving correctness of fault detection and clustering. As a result of our expeliment, the proposed algorithm caused neither false positive nor false negative.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. 반도체 공정
• 3. 심볼릭 표현법
• 4. 이상탐지 알고리즘
• 5. 변수 선택 및 클러스터링 알고리즘
• 6. 실험 및 평가
• 7. 결론
• 참고문헌
• 부록

참고문헌 (Reference)

• 1 J.Shieh, "iSAX:Indexing and Mining Terabyte Sized Time Series" 623-632, 2008
• 2 E.Keogh, "Towards Parameter-Free Data Mining" 206-215, 2004
• 3 SEMI, "Silicon Run I, 2nd Edition, Training Video" Silicon Run Productions 1996
• 4 H. J. Hwang, "Semiconductor Process Technology" Saeng-Reung Publishers 2000
• 5 J.R.Moyne, "SEMI E133-The Process Control System Standard:Deriving a Software Interoperability Standard for Advanced Process Control in Semiconductor Manufacturing" 20 (20): 408-420, 2007
• 6 H.Ding, "Querying and Mining of Time Series Data:Experimental Comparison of Representations and Distance Measures" 1 (1): 1542-1552, 2008
• 7 B.Chiu, "Probabilistic Discovery of Time Series Motifs" 493-498, 2003
• 8 V.Anandarajah, "Precise Time Synchronization in Semiconductor Manufacturing" 78-84, 2007
• 9 P.Geurts, "Pattern Extraction for Time Series Classification" 115-127, 2001
• 10 P.Patel, "Mining Motifs in Massive Time Series Databases" 370-377, 2002
• 11 E. Keogh, "Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases" 151-162, 2001
• 12 D. C. Montgomery, "Introduction to Statistical Quality Control, 6th Edition" Wiley 2008
• 13 E.Keogh, "HOT SAX:Efficiently Finding the Most Unusual Time Series Subsequence" 226-233, 2005
• 14 C. Faloutsos, "Fast Subsequence Matching in Time- Series Databases" 419-429, 1994
• 15 W. L. Martinez, "Exploratory Data Analysis with MATLAB" Chapman & Hall 2004
• 16 J.Lin, "Experiencing SAX:a Novel Symbolic Representation of Time Series" 15 (15): 107-144, 2007
• 17 K.Chan, "Efficient Time Series Matching by Wavelets" 126-133, 1999
• 18 J. Han, "Data Mining: Concepts and Techniques Second Edition" Morgan Kaufmann 2005
• 19 B.A.Rashap, "Control of Semiconductor Manufacturing Equipment:Real-Time Feedback Control of a Reactive Ion Etcher" 8 (8): 286-297, 1995
• 20 S.J.Hong, "Automated Fault Detection and Classification of Etch Systems Using Modular Neural Networks" 134-141, 2004
• 21 L. Wei, "Assumption-Free Anomaly Detection in Time Series" 237-240, 2005
• 22 O.A.S.Youssef, "An optimised fault classification technique based on Support-Vector-Machines" 1-8, 2009
• 23 G.G.Barna, "APC in the semiconductor industry,history and near term prognosis" 364-369, 1996
• 24 J. Lin, "A Symbolic Representation of Time Series, with Implications for Streaming Algorithms" 2-11, 2003
• 25 S.-Y.Lin, "A Classification-Based Fault Detection and Isolation Scheme for the Ion Implanter" 19 (19): 411-424, 2006