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Using Bayesian network technology to predict the semiconductor manufacturing yield rate in IoT

Abstract

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm’s requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage.

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References

  1. 1.

    Rosing M, Simon M. Polovina (2015) The value of ontology in the complete business process handbook.

  2. 2.

    Allemang D, Hendler J (2011) Ontologies on the web putting it all together in Semantic Web for the Working Ontologist, 2nd edn.

  3. 3.

    Luo F, Guo W, Yu Y et al (2017) A multi-label classification algorithm based on kernel extreme learning machine. Neurocomputing 260:313–320

    Article  Google Scholar 

  4. 4.

    Guo K, Qishan ZQ (2013) Fast clustering-based anonymization approaches with time constraints for data streams. Knowledge-Based Syst 46:95–108

    Article  Google Scholar 

  5. 5.

    Lin B, Guo W, Xiong N et al (2016)A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Trans Netw Service Manage 13(3):581–594.

  6. 6.

    Furlow G (2001) The case for building a data warehouse. IEEE IT Professional 3(4):31–34

    Article  Google Scholar 

  7. 7.

    Xia Y, Wang J (2016) A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans Neural Netw Learn Syst 27(2):214–224

    MathSciNet  Article  Google Scholar 

  8. 8.

    Xia Y, Wang J (2015) Low-dimensional recurrent neural network-based Kalman filter for speech enhancement. Neural Netw 67:131–139

    Article  Google Scholar 

  9. 9.

    Antoniou G, Harmelen FV (2004) Web ontology language: owl. Handbook on Ontologies.

  10. 10.

    Alani H, Kim S, Millard DE et al (2003) Automatic ontology-based knowledge extraction from Web documents. IEEE Intell Syst 18(1):14–21

    Article  Google Scholar 

  11. 11.

    Yang D, Liao X, Shen H et al (2017) Relative influence maximization in competitive social networks. Sci China Inf Sci 60(10):108101

    Article  Google Scholar 

  12. 12.

    Yang LH, Wang YM, Su Q et al (2016) Multi-attribute search framework for optimizing extended belief rule-based systems. Inf Sci 370:159–183

    Article  Google Scholar 

  13. 13.

    Solimana KS, Janzb BD (2004) An exploratory study to identify the critical factors affecting the decision to establish Internet-based inter organizational information systems. Inf Manage 41:697–706

    Article  Google Scholar 

  14. 14.

    Vargas-Vera M et al (2001) Knowledge extraction using an ontology-based annotation tool. Workshop on Knowledge Markup & Semantic Annotation, 2001

  15. 15.

    Poli R (2002) Ontological methodology. Int J Hum Comput Stud 56:639–664

    Article  Google Scholar 

  16. 16.

    Kittler R, Wang W (2000) Data mining for yield improvements. Yield Dynamics, Inc, Bloomberg

  17. 17.

    Guo K, Guo W, Chen Y et al (2015) Community discovery by propagating local and global information based on the MapReduce model. Inf Sci 323:73–93

    MathSciNet  Article  Google Scholar 

  18. 18.

    Guo L, Shen H, Zhu W (2017) Efficient approximation algorithms for multi-antennae largest weight data retrieval. IEEE Trans Mobile Comput 16(12):3320–3333

    Article  Google Scholar 

  19. 19.

    Guo L, Shen H (2017) Efficient approximation algorithms for the bounded flexible scheduling problem in clouds. IEEE Trans Parallel Distributed Syst 28(12):3511–3520

    Article  Google Scholar 

  20. 20.

    Guo WZ, Chen JY, Chen GL et al (2015) Trust dynamic task allocation algorithm with Nash equilibrium for heterogeneous wireless sensor network. Security Commun Netw 8(10):1865–1877

    Article  Google Scholar 

  21. 21.

    Guo W, Chen G (2015) Human action recognition via multi-task learning base on spatial–temporal feature. Inf Sci 320:418–428

    MathSciNet  Article  Google Scholar 

  22. 22.

    Guo W, Li J, Chen G et al (2015) A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. IEEE Trans Parallel Distributed Syst 26(12):3236–3249

    Article  Google Scholar 

  23. 23.

    Zhang S, Xia Y, Wang J (2015) A complex-valued projection neural network for constrained optimization of real functions in complex variables. IEEE Trans Neural Netw Learn Syst 26(12):3227–3238

    MathSciNet  Article  Google Scholar 

  24. 24.

    Hodges AP, Woolf P, He Y (2010) BN+1 Bayesian network expansion for identifying molecular pathway elements. Commun Integr Biol 3:549–554

    Article  Google Scholar 

  25. 25.

    Zaiyi Pu (2018) Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction. J Supercomputing 76:1342–1357

    Article  Google Scholar 

  26. 26.

    Tran D, Dusenberry M, van der Wilk M, Hafner D (2019) Bayesian layers: a module for neural network uncertainty. In: Advances in neural information processing systems, pp 14633–14645

  27. 27.

    Gao H, Zeng X, Yao C (2019) Application of improved distributed naive Bayesian algorithms in text classification. J Supercomput 75:5831–5847

    Article  Google Scholar 

  28. 28.

    Mehmood A, Mukherjee M, Ahmed SH, Song H, Malik KM (2018) NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J Supercomput 74:5156–5170

    Article  Google Scholar 

  29. 29.

    Guo W, Liu G, Chen G et al (2014) A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning. Front Comp Sci 8(2):203–216

    MathSciNet  Article  Google Scholar 

  30. 30.

    Guo W, Sun X, Niu Y (2014) Multi-scale saliency detection via inter-regional shortest colour path. IET Comp Vis 9(2):290–299

    Article  Google Scholar 

  31. 31.

    Yang Y, Ma M (2016) Conjunctive keyword search with designated tester and timing enabled proxy re-encryption function for E-Health clouds. IEEE Trans Inf Forens Security 11(4):746–759

    Google Scholar 

  32. 32.

    Liu G, Guo W, Niu Y et al (2015) A PSO-based timing-driven Octilinear Steiner tree algorithm for VLSI routing considering bend reduction. Soft Comput 19(5):1153–1169

    MATH  Article  Google Scholar 

  33. 33.

    Liu G, Huang X, Guo W et al (2015) Multilayer obstacle-avoiding x-architecture steiner minimal tree construction based on particle swarm optimization. IEEE Trans Cybern 45(5):989–1002

    Google Scholar 

  34. 34.

    Xia Y, Leung H (2014) Performance analysis of statistical optimal data fusion algorithms. Inf Sci 277:808–824

    MathSciNet  MATH  Article  Google Scholar 

  35. 35.

    Huang X, Guo W, Liu G et al (2016) FH-OAOS: a fast four-step Heuristic for obstacle-avoiding octilinear Steiner tree construction. ACM Trans Des Automation Electr Syst (TODAES) 21(3):48

    Google Scholar 

  36. 36.

    Huang X, Liu G, Guo W et al (2015) Obstacle-avoiding algorithm in X-architecture based on discrete particle swarm optimization for VLSI design. ACM Trans Des Automation Electr Syst (TODAES) 20(2):24

    Google Scholar 

  37. 37.

    Yang Y, Zheng X, Tang C (2017) Lightweight distributed secure data management system for health internet of things. J Netw Comp Appl 89:26–37

    Article  Google Scholar 

  38. 38.

    Yang Y (2014) Broadcast encryption based non-interactive key distribution in MANETs. J Comp Syst Sci 80(3):533–545

    MATH  Article  Google Scholar 

  39. 39.

    Ye D, Chen Z (2015) A new approach to minimum attribute reduction based on discrete artificial bee colony. Soft Comput 19(7):1893–1903

    Article  Google Scholar 

  40. 40.

    Huang Z, Yu Y, Gu J et al (2017) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 47(4):920–933

    Article  Google Scholar 

  41. 41.

    Lin G, Liu Y, Zhu W (2015) Speeding up a memetic algorithm for the max-bisection problem. Numer Algebra Control Optim 5(2):151–168

    MathSciNet  MATH  Article  Google Scholar 

  42. 42.

    Liu G, Guo W, Li R et al (2015) XGRouter: high-quality global router in X-architecture with particle swarm optimization. Front Comp Sci 9(4):576–594

    Article  Google Scholar 

  43. 43.

    Yang Y, Zheng X, Chang V, Ye S, Tang C (2018) Lattice assumption based fuzzy information retrieval scheme support multi-user for secure multimedia cloud. Multimedia Tools Appl 77:9927–9941

    Article  Google Scholar 

  44. 44.

    Yang Y, Zheng X, Chang V et al (2017) Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage. Concurrency Comput Pract Exp 29(19):e4211

    Article  Google Scholar 

  45. 45.

    Ye D, Chen et al (2013) A novel and better fitness evaluation for rough set based minimum; attribute reduction problem. Inform Sci 222(3):413–423

    MathSciNet  MATH  Article  Google Scholar 

  46. 46.

    Yu Y, Sun Z (2017) Sparse coding extreme learning machine for classification. Neurocomputing 261:50–56

    Article  Google Scholar 

  47. 47.

    Zhang Q, Qiu Q, Guo W et al (2016) A social community detection algorithm based on parallel grey label propagation. Comput Netw 107:133–143

    Article  Google Scholar 

  48. 48.

    Liu W, Lau RWH, Wang X et al (2016) Exemplar-AMMs: recognizing crowd movements from pedestrian trajectories. IEEE Trans Multimedia 18(12):2398–2406

    Article  Google Scholar 

  49. 49.

    Tu J, Xia Y, Zhang S (2017) A complex-valued multichannel speech enhancement learning algorithm for optimal tradeoff between noise reduction and speech distortion. Neurocomputing 267:333–343

    Article  Google Scholar 

  50. 50.

    Wang J, Zhang XM, Lin Y et al (2018) Event-triggered dissipative control for networked stochastic systems under non-uniform sampling. Inf Sci 2018:S0020025518301749

    Google Scholar 

  51. 51.

    Wang S, Guo W (2017) Robust co-clustering via dual local learning and high-order matrix factorization. Knowl-Based Syst 138:176–187

    Article  Google Scholar 

  52. 52.

    Wei J, Liao X, Zheng H, Chen G, Cheng X (2018) Learning from context: a mutual reinforcement model for Chinese microblog opinion retrieval. Front Comput Sci 12:714–724

    Article  Google Scholar 

  53. 53.

    Xia Y, Chen T, Shan J (2014) A novel iterative method for computing generalized inverse. Neural Comput 26(2):449–465

    MathSciNet  MATH  Article  Google Scholar 

  54. 54.

    Xia Y, Leung H, Kamel MS (2016) A discrete-time learning algorithm for image restoration using a novel L2-norm noise constrained estimation. Neurocomputing 198:155–170

    Article  Google Scholar 

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Acknowledgments

This work described in this paper is supported by the research and practice project of teaching reform of Higher Vocational Education in Guangdong Province (No. GDJG2019008), China.

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Correspondence to Xiaodong Fang.

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Cite this article

Fang, X., Chang, C. & Liu, G. Using Bayesian network technology to predict the semiconductor manufacturing yield rate in IoT. J Supercomput 77, 9020–9045 (2021). https://doi.org/10.1007/s11227-021-03649-z

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Keywords

  • Bayesian network
  • Semiconductor manufacturing industry
  • Ontology technology
  • Data mining
  • Yield rate
  • Internet of things (IoT)