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Using neural network to establish manufacture production performance forecasting in IoT environment

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Abstract

Under the complex environmental of the front-end manufacturing process of semiconductors, on-site managers are concerned about the production performance of the bottleneck machine, hoping to predict the future production volume as a reference for future production decisions in Internet of Things (IoT). In this study, we proposed neural network methods were used to predict production performance by using genetic programming (GP) and backward propagation neural network (BPN). GP is applied to numerical prediction to calculate the difference between the actual output value and the predicted output value. BPN can be divided into an input layer processing unit, a hidden layer processing unit, and an output layer processing unit, which can be divided into three different functional layers. This study uses the production data of semiconductor manufacture factory to compare the results of bottleneck machine production performance predictions carried out by GP and BPN methods. The result show that the average prediction accuracy of the two methods are reaching the high level of practical requirements and indicates BPN is better than GP method. Both methods can provide a reference for manager practical to make decisions to improve production performance in semiconductor industry.

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References

  1. Kim D, Kim M, Kim W (2020) Wafer edge yield prediction using a combined long short-term memory and feed- forward neural network model for semiconductor manufacturing. IEEE Access 8

  2. Kang S (2018) On effectiveness of transfer learning approach for neural network-based virtual metrology modeling. IEEE Trans Semicond Manuf 31(1)

  3. Chakravorty S, Nagarur N (2020) An artificial neural network based algorithm for real time dispatching decisions. In: 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

  4. Wang J, Zhang J, Wang X (2018) A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system. IEEE Trans Semicond Manuf 31(1)

  5. Lee K, Cheon S, Kim C (2017) A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Trans Semicond Manuf 30(2)

  6. Huang C, Fu S, Parashar P, Chen C, Akbar C, Lin A (2018) Intelligent manufacturing: TCAD-assisted adaptive weighting neural networks. IEEE Access 6

  7. Meidan Y, Lerner B, Rabinowitz G, Hassoun M (2011) Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Trans Semicond Manuf 24(2)

  8. Mevawalla Z, May G, Honjo M, Kiehlbauch M (2011) Neural network modeling of fabrication yield using manufacturing data. In: 2011 IEEE/SEMI Advanced Semiconductor Manufacturing Conference

  9. Tirkel I (2011) Cycle time prediction in wafer fabrication line by applying data mining methods. In: 2011 IEEE/SEMI Advanced Semiconductor Manufacturing Conference

  10. Azimlu F, Rahnamayan S, Makrehchi M, Kalra N (2019) Comparing genetic programming with other data mining techniques on prediction models. In: 2019 14th International Conference on Computer Science & Education (ICCSE)

  11. Nalini C, Krishna T (2020) An efficient software defect prediction model using neuro evalution algorithm based on genetic algorithm. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)

  12. Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61(5)

  13. Ekárt A, Patelli A, Lush V, Ilie-Zudor E (2020) Genetic programming with transfer learning for urban traffic modelling and prediction. In: 2020 IEEE Congress on Evolutionary Computation (CEC)

  14. Syu Y, Fanjiang Y, Kuo J, Ma S (2015) Applying genetic programming for time-aware dynamic QoS prediction. In: 2015 IEEE International Conference on Mobile Services

  15. Zhou H, Hirasawa K (2014) Traffic density prediction with time-related data mining using genetic network programming. Comput J 57(9)

  16. Rathore S, Kuamr S (2015) Comparative analysis of neural network and genetic programming for number of software faults prediction. In: 2015 National Conference on Recent Advances in Electronics & Computer Engineering (RAECE)

  17. Hara A, Kushida J, Takahama T (2019) Time series prediction using deterministic geometric semantic genetic programming. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

  18. Chen X, Li M, Zhong H, Ma Y, Hsu C (2021) DNNOff: offloading DNN-based intelligent IoT applications in mobile edge computing. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2021.3075464

    Article  Google Scholar 

  19. Chen X, Chen S, Ma Y, Liu B, Zhang Y, Huang G (2019) An adaptive offloading framework for android applications in mobile edge computing. SCIENCE CHINA Inf Sci 62(8):82102

    Article  Google Scholar 

  20. Huang G, Xu M, Lin X, Liu Y, Ma Y, Pushp S, Liu X (2017) ShuffleDog: characterizing and adapting user-perceived latency of android apps. IEEE Trans Mobile Comput 16(10):2913–2926

    Article  Google Scholar 

  21. Zhang Y, Huang G, Liu X, Zhang W, Mei H, Yang S (2012) Refactoring android Java code for on-demand computation offloading. In: ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications

  22. Lin B, Huang Y, Zhang J, Hu J, Chen X, Li J (2020) Cost-driven offloading for DNN-based applications over cloud, edge and end devices. IEEE Trans Ind Inf 16(8):5456–5466

    Article  Google Scholar 

  23. Chen X, Zhu F, Chen Z, Min G, Zheng X, Rong C (2021) Resource allocation for cloud-based software services using prediction-enabled feedback control with reinforcement learning. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2020.2992537

    Article  Google Scholar 

  24. Chen X, Lin J, Ma Y, Lin B, Wang H, Huang G (2019) Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model. SCIENCE CHINA Inf Sci 62(11):219101

    Article  Google Scholar 

  25. Chen X, Wang H, Ma Y, Zheng X, Guo L (2020) Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Future Gener Comput Syst 105:287–296

    Article  Google Scholar 

  26. Huang G, Chen X, Zhang Y, Zhang X (2012) Towards Architecture-based management of platforms in the cloud. Front Comp Sci 6(4):388–397

    Article  MathSciNet  Google Scholar 

  27. Chen X, Li A, Zeng X, Guo W, Huang G (2015) Runtime model based approach to IoT application development. Front Comput Sci 9(4):540–553

    Article  Google Scholar 

  28. Huang G, Ma Y, Liu X, Luo Y, Lu X, Blake M (2015) Model-based automated navigation and composition of complex service mashups. IEEE Trans Serv Comput 8(3):494–506

    Article  Google Scholar 

  29. Liu X, Huang G, Zhao Q, Mei H, Blake M (2014) iMashup: a mashup-based framework for service composition. SCIENCE CHINA Inf Sci 54(1):1–20

    Article  Google Scholar 

  30. Huang G, Liu X, Ma Y, Lu X, Zhang Y, Xiong Y (2019) Programming situational mobile web applications with cloud-mobile convergence: an internetware-oriented approach. IEEE Trans Serv Comput 12(1):6–19

    Article  Google Scholar 

  31. Huang G, Mei H, Yang F (2006) Runtime recovery and manipulation of software architecture of component-based systems. Autom Softw Eng 13(2):257–281

    Article  Google Scholar 

  32. Huang G, Liu T, Mei H, Zheng Z, Liu Z, Fan G (2004) Towards autonomic computing middleware via reflection. In: International Computer Software and Applications Conference

  33. Huang G, Luo C, Wu K, Ma Y, Zhang Y, Liu X (2019) Software-defined infrastructure for decentralized data lifecycle governance: principled design and open challenges. In: IEEE International Conference on Distributed Computing Systems

  34. Song H, Huang G, Chauvel F, Xiong Y, Hu Z, Sun Y, Mei H (2011) Supporting runtime software architecture: a bidirectional-transformation-based approach. J Syst Softw 84(5):711–723

    Article  Google Scholar 

  35. Chen CM, Chen L, Gan W, Qiu L, Ding W (2021) Discovering high utility-occupancy patterns from uncertain data. Inf Sci 546:1208–1229

    Article  MathSciNet  Google Scholar 

  36. Chen CM, Huang Y, Wang KH, Kumari S, Wu M (2020) A secure authenticated and key exchange scheme for fog computing. Enterp Inf Syst 1–16

  37. Geng S, Hu T (2020) Sports games modeling and prediction using genetic programming. In: 2020 IEEE Congress on Evolutionary Computation (CEC)

  38. Yao H, Jia X, Wang B, Guo B (2019) A new method for estimating lithium-ion battery capacity using genetic programming combined model. In: 2019 Prognostics and System Health Management Conference (PHM-Qingdao)

  39. Seo K, Hyeon B (2015) Evolutionary nonlinear Model Output Statistics for wind speed prediction using Genetic Programming. In: 2015 7th International Joint Conference on Computational Intelligence (IJCCI)

  40. Kusiak A (2001) Rough set theory: a data mining tool for semiconductor manufacturing. IEEE Trans Electron Packag Manuf 24(1):44–50

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Engineering, Nanyang Institute of Technology, Guangzhou, 510800, China

    Zhifang Liu

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  1. Zhifang Liu
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Correspondence to Zhifang Liu.

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Liu, Z. Using neural network to establish manufacture production performance forecasting in IoT environment. J Supercomput 78, 9595–9618 (2022). https://doi.org/10.1007/s11227-021-04210-8

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  • DOI: https://doi.org/10.1007/s11227-021-04210-8

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