Abstract
Process monitoring and profile analysis are crucial in detecting various abnormal events in semiconductor manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield enhancement and quality control. To address real requirements, this study aims to develop a framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of correlated process variables to eliminate the cause of the faults and thus reduce abnormal yield loss. Multi-way principal component analysis and data mining are used to construct the model to detect faults and to derive the rules for fault classification. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the model. Use of the proposed framework can effectively detect abnormal wafers based on a controlled limit and the derived simple rules. The extracted information can be used to aid fault diagnosis and process recovery. The proposed solution has been implemented in the semiconductor company. This has simplified the monitoring process in the FDC system through the fewer key variables. The results demonstrate the practical viability of the proposed approach.
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References
Chen L-F, Chien C-F (2011) Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries. Flex Serv Manuf J 23(3):263–289
Chien C-F, Hsu C (2011) UNISON analysis to model and reduce step-and-scan overlay errors for semiconductor manufacturing. J Intell Manuf 22(3):399–412
Chien C-F, Wang W, Cheng J (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192–198
Chien C-F, Chen Y-J, Peng J-T (2010) Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and production life cycle. Int J Prod Econ 128(2):496–509
Chien C-F, Hsu C, Hsiao C (2011) Manufacturing intelligence to forecast and reduce semiconductor cycle time. J Intell Manuf. doi:10.1007/s10845-011-0572-y
He QP, Wang J (2007) Fault detection using the k-nearest neighbor rule for semiconductor manufacturing process. IEEE Trans Semicond Manuf 20(4):345–354
Isermann R (1995) Model based fault detection and diagnosis methods. Proc Am Contr Conf 3:1605–1609
Jackson JE, Mudholkar GS (1979) Control procedures for residuals associated with principal component analysis. Technometrics 21(3):341–349
Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119–127
Kourti T, MacGregor JF (1996) Multivariate SPC methods for process and product monitoring. J Qual Technol 28(4):409–428
Kuo C, Chien C-F, Chen C (2010) Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE Trans Autom Sci Eng 8(1):103–111
MacGregor JF, Jacckle C, Kiparissdes C, Kourti M (1994) Process monitoring and diagnosis by multiblock PLS methods. AIChE J 40(5):826–838
Montgomery DC (2005) Introduction to statistical quality control, 5th edn. John Wiley & Sons, Inc., New York
Nomikos P (1996) Detection and diagnose of abnormal batch operations based on multi-way principal component analysis. ISA Trans 35(3):259–266
Nomikos P, MacGregor JF (1994) Monitoring the processes using multiway principal component analysis. AIChE J 40(8):1361–1375
Raich A, Cinar A (1996) Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE J 42(4):905–1009
Ralson P, DePuy G, Graham JH (2001) Computer-based monitoring and fault diagnosis: a chemical process case study. ISA Trans 40(1):85–98
Skagerberg B, MacGregor JF, Kiparissides C (1992) Multivariate data analysis applied to low-density polyethylene reactors. Chemom Intell Lab Syst 14(1–3):341–356
Spitzlsperger G, Schmidt C, Ernst G, Strasser H, Speil M (2005) Fault detection for a via etch process using adaptive multivariate methods. IEEE Trans Semicond Manuf 18(4):528–533
Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN (2003) A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput Chem Eng 27(3):293–311
Verdier G, Ferreira A (2011) Adaptive Mahalanobis distance and k-nearest neighbor rule for fault defection in semiconductor manufacturing. IEEE Trans Semicond Manuf 24(1):59–68
Wikströma C, Albano C, Eriksson L, Fridén H, Johansson E, Nordahl A, Rännar S, Sandberg M, Kettaneh-Wold N, Wold S (1998) Multivariate process and quality monitoring applied to an electrolysis process part I. Process supervision with multivariate control charts. Chemom Intell Lab Syst 42(1–2):221–231
Wise BM, Gallagher NB (1996) The process chemometrics approach to process monitoring and fault detection. J Process Contr 6(6):329–348
Wise BM, Gallagher NB, Bulter SW, White DD Jr, Barna GG (1999) A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. J Chemom 13(3–4):379–396
Wold S, Esbensen K, Geladi P (1987a) Principal components analysis. Chemom Intell Lab Syst 2(1–3):37–52
Wold S, Geladi P, Esbensen K, Ohman J (1987b) Multi-way principal components and PLS analysis. J Chemom 1(1):41–56
Yue HH, Qin SJ, Markle RJ, Nauert C, Gatto M (2000) Fault detection of plasma etchers using optical emission spectra. IEEE Trans Semicond Manuf 13(3):374–385
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This research is supported by National Science Council, Taiwan (NSC 99-2221-E-007-047-MY3; NSC 100-2410-H-155-048), and the Ministry of Education (101N2073E1).
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Chien, CF., Hsu, CY. & Chen, PN. Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flex Serv Manuf J 25, 367–388 (2013). https://doi.org/10.1007/s10696-012-9161-4
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DOI: https://doi.org/10.1007/s10696-012-9161-4