Top

The Journal of Supercomputing

Published in:

06-08-2022

Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study

Authors: Abubakar Abdussalam Nuhu, Qasim Zeeshan, Babak Safaei, Muhammad Atif Shahzad

Published in: The Journal of Supercomputing | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Industries are going through the fourth industrial revolution (Industry 4.0), where technologies like the Industrial Internet of things, big data analytics, and machine learning (ML) are extensively utilized to improve the productivity and efficiency of manufacturing systems and processes. This work aims to further investigate the applicability and improve the effectiveness of ML prediction models for fault diagnosis in the smart manufacturing process. Hence, we propose several methodologies and ML models for fault diagnosis for smart manufacturing process applications. A case study has been conducted on a real dataset from a semiconductor manufacturing (SECOM) process. However, this dataset contains missing values, noisy features, and class imbalance problem. This imbalance problem makes it so difficult to accurately predict the minority class, due to the majority class size difference. In the literature, efforts have been made to alleviate the class imbalance problem using several synthetic data generation techniques (SDGT) on the UCI machine learning repository SECOM dataset. In this work, to handle the imbalance problem, we employed, compared, and evaluated the feasibility of three SDGT on this dataset. To handle issues related to the missing values and noisy features, we implemented two missing values imputation techniques and feature selection techniques, respectively. We then developed and compared the performance of ten predictive ML models against these proposed methodologies. The results obtained across several evaluation metrics of performance were significant. A comparative analysis shows the feasibility and validate the effectiveness of these SDGT and the proposed methodologies. Some among the proposed methodologies could produce an accuracy in the range of 99.5% to 100%. Furthermore, based on a comparative analysis with similar models from the literature, our proposed models outpaced those proposed in the literature.

previous article Simulating hybrid SysML models: a model transformation approach under the DEVS framework

next article Traffic-aware dynamic controller placement in SDN using NFV

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Lee DH, Yang JK, Lee CH, Kim KJ (2019) A data-driven approach to selection of critical process steps in the semiconductor manufacturing process considering missing and imbalanced data. J Manuf Syst 52:146–156. https://doi.org/10.1016/J.JMSY.2019.07.001CrossRef

Pfingsten T, Herrmann DJL, Schnitzler T, Feustel A, Schölkopf B (2007) Feature selection for troubleshooting in complex assembly lines. IEEE Trans Autom Sci Eng 4:465–469. https://doi.org/10.1109/TASE.2006.888054CrossRef

Mccann M, Li Y, Maquire L, Johnston A (2010) Causality challenge: benchmarking relevant signal components for effective monitoring and process control. J Mach Learn Res Work Conf Proc 6:277–288

Shin CK, Park SC (2000) A machine learning approach to yield management in semiconductor manufacturing. Int J Prod Res 38:4261–4271. https://doi.org/10.1080/00207540050205073CrossRef

Kumar N, Kennedy K, Gildersleeve K, Abelson R, Mastrangelo CM, Montgomery DC (2006) A review of yield modelling techniques for semiconductor manufacturing. Int J Prod Res 44:5019–5036. https://doi.org/10.1080/00207540600596874CrossRefMATH

Chien CF, Wang WC, Cheng JC (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33:192–198. https://doi.org/10.1016/j.eswa.2006.04.014CrossRef

Çinar ZM, Nuhu AA, Zeeshan Q, Korhan O, Asmael M, Safaei B (2020) Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12:8211. https://doi.org/10.3390/su12198211CrossRef

Kang S, An D, Rim J (2019) Incorporating virtual metrology into failure prediction. IEEE Trans Semicond Manuf 32:553–558. https://doi.org/10.1109/TSM.2019.2932377CrossRef

Su AJ, Jeng JC, Huang HP, Yu CC, Hung SY, Chao CK (2007) Control relevant issues in semiconductor manufacturing: overview with some new results. Control Eng Pract 15:1268–1279. https://doi.org/10.1016/j.conengprac.2006.11.003CrossRef

10.

Mack CA (2011) Fiftyyears of Moore’ s law. IEEE Fellow 24:2008

11.

Ghahramani M, Qiao Y, Zhou MC, O’Hagan A, Sweeney J, Hagan AO, Sweeney J (2020) AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA J Autom Sin 7:1026–1037. https://doi.org/10.1109/JAS.2020.1003114CrossRef

12.

Zhang W, Yang D, Wang H (2019) Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst J 13:2213–2227. https://doi.org/10.1109/jsyst.2019.2905565CrossRef

13.

Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23–45. https://doi.org/10.1080/21693277.2016.1192517CrossRef

14.

Carbery CM, Woods R, Marshall AH (2019) A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems. Proc Inst Mech Eng Part C J Mech Eng Sci 233:6713–6726. https://doi.org/10.1177/0954406219866867CrossRef

15.

Munirathinam S, Ramadoss B (2016) Predictive models for equipment fault detection in the semiconductor manufacturing process. Int J Eng Technol 8:273–285. https://doi.org/10.7763/ijet.2016.v8.898CrossRef

16.

Kerdprasop K, Kerdprasop N (2011) A data mining approach to automate fault detection model development in the semiconductor manufacturing process. Int J Mech 5:336–344

17.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1002/eap.2043CrossRefMATH

18.

Susan S, Kumar A (2019) SSO Maj-SMOTE-SSO Min: three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets. Appl Soft Comput J 78:141–149. https://doi.org/10.1016/j.asoc.2019.02.028CrossRef

19.

Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning

20.

Nguyen HM, Cooper EW, Kamei K (2009) Borderline over-sampling for imbalanced data classification. Int J Knowl Eng Soft Data Paradig 3:4. https://doi.org/10.1504/ijkesdp.2011.039875CrossRef

21.

He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of international joint conference on neural networks, pp 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969

22.

Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 5476 LNAI, pp 475–482. https://doi.org/10.1007/978-3-642-01307-2_43

23.

Last F, Douzas G, Bacao F (2017) Oversampling for imbalanced learning based on K-means and SMOTE, pp 1–19. https://doi.org/10.1016/j.ins.2018.06.056

24.

Barua S, Islam MM, Yao X, Murase K (2014) MWMOTE - Majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26:405–425. https://doi.org/10.1109/TKDE.2012.232CrossRef

25.

Wang JB, Zou CA, Fu GH (2021) AWSMOTE: an SVM-based adaptive weighted SMOTE for class-imbalance learning. Sci Program. https://doi.org/10.1155/2021/9947621CrossRef

26.

Wang Q, Luo Z, Huang J, Feng Y, Liu Z (2017) A novel ensemble method for imbalanced data learning. Comput Intell Neurosci 2017:1–11

27.

Wang L, Wang Y (2020) Application of machine learning for process control in semiconductor manufacturing. In: The ACM international conference proceeding series, pp 109–111. https://doi.org/10.1145/3424311.3424326

28.

Mat Jizat JA, Abdul Majeed APP, Ahmad AF, Taha Z, Yuen E (2021) Evaluation of the machine learning classifier in wafer defects classification. ICT Express. https://doi.org/10.1016/j.icte.2021.04.007CrossRef

29.

Saqlain M, Abbas Q, Lee JY (2020) A deep convolutional neural network for wafer defect identification on an imbalanced dataset in semiconductor manufacturing processes. IEEE Trans Semicond Manuf 33:436–444. https://doi.org/10.1109/TSM.2020.2994357CrossRef

30.

Stich P, Wahl M, Czerner P, Weber C, Fathi M (2020) Yield prediction in semiconductor manufacturing using an AI-based cascading classification system. https://doi.org/10.1109/EIT48999.2020.9208250

31.

Moldovan D, Anghel I, Cioara T, Salomie I (2020) Particle swarm optimization based deep learning ensemble for manufacturing processes. In: Proceedings of 2020 IEEE 16th international conference on intelligent computer communication and processing ICCP 2020, pp 563–570. https://doi.org/10.1109/ICCP51029.2020.9266269

32.

Moldovan D, Anghel I, Cioara T, Salomie I (2020) Machine learning in manufacturing: processes classification using support vector machine and Horse optimization algorithm. In: Proceedings of the RoEduNet IEEE international conference 2020-Decem. https://doi.org/10.1109/ROEDUNET51892.2020.9324855

33.

Lee KB, Kim CO (2020) Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process. J Intell Manuf 31:73–86. https://doi.org/10.1007/s10845-018-1437-4CrossRef

34.

Fan SKS, Hsu CY, Jen CH, Chen KL, Juan LT (2020) Defective wafer detection using a denoising autoencoder for semiconductor manufacturing processes. Adv Eng Inform 46:101166. https://doi.org/10.1016/j.aei.2020.101166CrossRef

35.

Salem M, Taheri S, Yuan J-S (2018) An experimental evaluation of fault diagnosis from imbalanced and incomplete data for smart semiconductor manufacturing. Big Data Cogn Comput 2:30. https://doi.org/10.3390/bdcc2040030CrossRef

36.

Chazhoor A, Mounika Y, Vergin Raja Sarobin M, Sanjana MV, Yasashvini R (2020) Predictive maintenance using machine learning based classification models. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899X/954/1/012001CrossRef

37.

Anghel I, Cioara T, Moldovan D, Salomie I, Tomus MM (2018) Prediction of manufacturing processes errors: gradient boosted trees versus deep neural networks. In Proceedings of the 16th international conference on embedded and ubiquitous computing EUC 2018, pp 29–36. https://doi.org/10.1109/EUC.2018.00012

38.

Kao HA, Hsieh YS, Chen CH, Lee J (2017) Quality prediction modeling for multistage manufacturing based on classification and association rule mining. MATEC Web Conf. https://doi.org/10.1051/matecconf/201712300029CrossRef

39.

Moldovan D, Cioara T, Anghel I, Salomie I (2017) Machine learning for sensor-based manufacturing processes. https://doi.org/10.1109/ICCP.2017.8116997

40.

Kim J, Han Y, Lee J (2016) Data imbalance problem solving for SMOTE based oversampling: study on fault detection prediction model in semiconductor manufacturing process. Adv Sci Technol Lett 133:79–84. https://doi.org/10.14257/astl.2016.133.15CrossRef

41.

Ko YC, Fujita H (2019) An evidential analytics for buried information in big data samples: case study of semiconductor manufacturing. Inf Sci (NY) 486:190–203. https://doi.org/10.1016/j.ins.2019.01.079CrossRef

42.

Takahashi Y, Asahara M, Shudo K (2019) A framework for model search across multiple machine learning implementations. arXiv:1908.10310

43.

Moldovan D, Chifu V, Pop C, Cioara T, Anghel I, Salomie I (2018) Chicken swarm optimization and deep learning for manufacturing processes. In: Proceedings of the 17th RoEduNet IEEE international conference networking in education and research RoEduNet 2018, pp 18–23. https://doi.org/10.1109/ROEDUNET.2018.8514152

44.

Kim JK, Han YS, Lee JS (2017) Particle swarm optimization–deep belief network–based rare class prediction model for highly class imbalance problem. Concurr Comput. https://doi.org/10.1002/cpe.4128CrossRef

45.

Cho E, Chang TW, Hwang G (2022) Data preprocessing combination to improve the performance of quality classification in the manufacturing process. Electronics. https://doi.org/10.3390/ELECTRONICS11030477CrossRef

46.

UCI Machine Learning Repository: SECOM Data Set, (n.d.). https://archive.ics.uci.edu/ml/datasets/SECOM (accessed January 17, 2021)

47.

Batista GEAPA, Monard MC (2002) A study of k-nearest neighbour as an imputation method. Front Artif Intell Appl 87:251–260

48.

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525. https://doi.org/10.1093/bioinformatics/17.6.520CrossRef

49.

De Silva H, Perera AS (2017) Missing data imputation using Evolutionary k-Nearest neighbor algorithm for gene expression data. In: 16th international conference on advances in ICT for emerging regions ICTer 2016—conference proceeding, pp 141–146. https://doi.org/10.1109/ICTER.2016.7829911

50.

Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459. https://doi.org/10.1002/wics.101CrossRef

51.

Zhou Y, Mazzuchi TA, Sarkani S (2020) M-AdaBoost-A based ensemble system for network intrusion detection. Expert Syst Appl 162:113864. https://doi.org/10.1016/j.eswa.2020.113864CrossRef

52.

Josse J, Prost N, Scornet E, Varoquaux G, Josse J, Prost N, Scornet E, Varoquaux G, Josse J (2020) On the consistency of supervised learning with missing values

53.

El Mourabit Y, El Habouz Y, Zougagh H, Wadiai Y (2020) Predictive system of semiconductor failures based on machine learning approach. Int J Adv Comput Sci Appl 11:199–203. https://doi.org/10.14569/IJACSA.2020.0111225CrossRef

54.

Twala BETH, Jones MC, Hand DJ (2008) Good methods for coping with missing data in decision trees. Pattern Recognit Lett 29:950–956. https://doi.org/10.1016/j.patrec.2008.01.010CrossRef

55.

Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin (accessed July 13, 2021)

56.

Arora M, Bhambhu L, Tech Scholar M (2014) Role of scaling in data classification using SVM. Int J Adv Res Comput Sci Softw Eng 4:2277

Title: Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study
Authors: Abubakar Abdussalam Nuhu
Qasim Zeeshan
Babak Safaei
Muhammad Atif Shahzad
Publication date: 06-08-2022
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 2/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-04730-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2023

AdaInNet: an adaptive inference engine for distributed deep neural networks offloading in IoT-FOG applications based on reinforcement learning

Hamiltonian properties of HCN and BCN networks

Toward a context-driven deployment optimization for embedded systems: a product line approach

Traffic-aware dynamic controller placement in SDN using NFV

Enabling efficient and reliable IoT deployment in 5G and LTE cellular areas for optimized service provisioning

Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster–Shafer theory (DS-1DCNN)

Premium Partner