Abstract
High-temperature open tube furnaces are essential in semiconductor manufacturing process. This type of equipment requires periodic servicing for operational longevity and to comply with the requirements of microelectronics processes. This paper presents a comparative analysis of Proportional–Integral (PI) and Fuzzy-PI algorithms for controlling a three-zone open tube furnace. Initially, the furnace was identified using an AutoRegressive eXogenous (ARX) model. The model was tested using a cross-validation method with 10-steps-ahead prediction tests. The prediction showed results higher than 93.70% with Final Prediction Error (FPE) lower than 0.0007. The controllers were simulated and their parameters were tuned using the identified model. The tuned algorithms were implemented through a PC-based instrumentation in real-time. The Fuzzy-PI controller presented the best results regarding the steady-state error, controlling the temperature of the furnace with a variation less than \(\pm 1.06~^{\circ }\mathrm{C}\) in the flat zone at the process temperature of \(900~^{\circ }\mathrm{C}\) with fast settling time. This innovative result presents a major step toward the modernization of high-temperature furnaces to meet the growing demands in the electronics industry.
Similar content being viewed by others
References
Buele, J., Ríos-Cando, P., Brito, G., Moreno-P., & Salazar, F. W. (2020). Temperature controller using the Takagi–Sugeno–Kang fuzzy inference system for an industrial heat treatment furnace. In Computational science and its applications—ICCSA 2020, (pp. 351–366). Springer International Publishing.
Camacho, E. F., & Bordons, C. (2007). Model Predictive Control. Springer.
Chen, Y.-H. (2011). A new approach to the control design of Fuzzy dynamical systems. Journal of Dynamic Systems, Measurement, and Control, 133(6), 061019.
Chunduri, S. K. (2009). Doping to well: Market survey on diffusion furnaces. Photon International, 8, 144–171.
Dequan, S., Guili, G., Zhiwei, G., & Peng, X. (2012). Application of expert Fuzzy PID method for temperature control of heating furnace. Procedia Engineering, 29, 257–261.
Gani, M. M., Islam, M. S., & Ullah, M. A. (2019). Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm. SN Applied Sciences, 1(8), 880.
Grassi, E., & Tsakalis, K. (2000). PID controller tuning by frequency loop-shaping: Application to diffusion furnace temperature control. IEEE Transactions on Control Systems Technology, 8(5), 842–847.
Haklidir, M., & Tasdelen, I. (2009). Modeling, simulation and Fuzzy control of an anthropomorphic robot arm by using dymola. Journal of Intelligent Manufacturing, 20(2), 177–186.
Hui, K., & Lu, C. S. (2002). Parameter identification for model-based advanced process control of diffusion furnaces. In Semiconductor manufacturing technology workshop, (pp. 136–139). IEEE.
Kumar, V. B., Rao, K. S., Charan, G., & Pavan Kumar, Y. V. (2021). Industrial heating furnace temperature control system design through fuzzy-pid controller. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), (pp. 1–6). IEEE.
Liao, Y., She, J., & Wu, M. (2009). Integrated hybrid-PSO and fuzzy-NN decoupling control for temperature of reheating furnace. IEEE Transactions on Industrial Electronics, 56(7), 2704–2714.
Li, C., Zhang, Y., & Dai, H. (2013). Design and implementation of the control system to vacuum diffusion furnace. In Proceedings of the 32nd Chinese control conference, (pp. 6428–6432). IEEE.
Li, H., Li, R., & Wu, F. (2020). A new control performance evaluation based on LQG benchmark for the heating furnace temperature control system. Processes, 8(11), 1428.
Li, R., Wu, F., Hou, P., & Zou, H. (2020). Performance assessment of FO-PID temperature control system using a fractional order LQG benchmark. IEEE Access, 8, 116653–116662.
Liu, L., Tian, S., Xue, D., Zhang, T., & Chen, Y. (2019). Industrial feedforward control technology: A review. Journal of Intelligent Manufacturing, 30(8), 2819–2833.
Ljung, L. (Ed.). (1999). System Identification: Theory for the User (2nd ed.). Prentice Hall PTR.
Ogata, K. (2009). Modern Control Engineering (5th ed.). Prentice Hall.
Padula, F., & Visioli, A. (2012). On the stabilizing PID controllers for integral processes. IEEE Transactions on Automatic Control, 57(2), 494–499.
Phu, N. D., Tri, P. V., Ahmadian, A., Salahshour, S., & Baleanu, D. (2018). Some kinds of the controllable problems for Fuzzy control dynamic systems. Journal of Dynamic Systems, Measurement, and Control, 140(9), 091008.
Pitalúa Díaz, N., Herrera-López, E. J., Valencia-Palomo, G., González-Angeles, A., Rodríguez-Carvajal, R. A., & Cazarez-Castro, N. R. (2015). Comparative analysis between conventional PI and Fuzzy logic PI controllers for indoor benzene concentrations. Sustainability, 7(5), 5398–5412.
Raj, R., & Mohan, B. M. (2019). Analytical structures of Takagi–Sugeno fuzzy two-input two-output proportional–integral/proportional–derivative controllers with multiple fuzzy sets. Journal of Dynamic Systems, Measurement, and Control, 141(5), 051010.
Ramiller, C. L., Mo-Yuen Chow, & Kuehn, R. T. (1998). Fuzzy control of temperature in a semiconductor processing furnace. In IECON ’98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), vol. 3, (pp. 1774–1779). IEEE.
Ramırez, M., Haber, R., Peña, V., & Rodrıguez, I. (2004). Fuzzy control of a multiple hearth furnace. Computers in Industry, 54(1), 105–113.
Rao, K. A. G., Reddy, B. A., & Bhavani, P. D. (2010). Fuzzy PI and integrating type Fuzzy PID controllers of linear, nonlinear and time-delay systems. International Journal of Computer Applications, 1(6), 41–47.
Rodríguez Ramos, A., Domínguez Acosta, C., Rivera Torres, P. J., Serrano Mercado, E. I., Beauchamp Baez, G., Rifón, L. A., & Llanes-Santiago, O. (2019). An approach to multiple fault diagnosis using Fuzzy logic. Journal of Intelligent Manufacturing, 30(1), 429–439.
Sinlapakun, V., & Assawinchaichote, W. (2015). Optimized PID controller design for electric furnace temperature systems with nelder mead algorithm. In 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), (pp. 1–4). IEEE.
Skoczowski, S., Domek, S., Pietrusewicz, K., & Broel-Plater, B. (2005). A method for improving the robustness of PID control. IEEE Transactions on Industrial Electronics, 52(6), 1669–1676.
Sonar, A., Shinde, S., & Teh, S. (2013). Automation: Key to cycle time improvement in semiconductor manufacturing. In ASMC 2013 SEMI advanced semiconductor manufacturing conference, (pp. 93–98). IEEE.
SVCS. (2021). Horizontal diffusion furnace. Retrieved July 1, 2021, from https://www.svcs.com/.
Teodorescu, L., Gheorghe, A. S., & Brezeanu, G. (2017). Prediction algorithm to control the temperature inside a laboratory furnace used for semiconductor devices characterization. In 2017 International Semiconductor Conference (CAS), (pp. 257–260).
Thermco Systems. (2022). 5000 series furnace systems. Retrieved 6 July, 2022, from http://www.thermcosystems.com/
Thermcraft Incorporated. (2022). Industrial furnace: Industrial oven: Manufacturers. Retrieved 6 July, 2022, from https://thermcraftinc.com/
Thermo Systems, K. (2022). Koyo Thermo Systems. Retrieved July 6, 2021, from https://www.koyothermos.com/
Wang, Y., Jin, Q., & Zhang, R. (2017). Improved Fuzzy PID controller design using predictive functional control structure. ISA Transactions, 71, 354–363.
Yang, T., Chen, X., Hu, H., Chu, Y.-L., & Cheng, P. (2008). A Fuzzy PID thermal control system for casting dies. Journal of Intelligent Manufacturing, 19(4), 375–382.
Zadeh, L. (1965). Fuzzy sets. Information Control, 8, 338–353.
Acknowledgements
The authors acknowledge the support from the Laboratory of Microelectronics (LME-USP).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Beccaro, W., Ramos, C.A.S. & Duarte, S.X. Optimizing semiconductor processing open tube furnace performance: comparative analysis of PI and Mamdani fuzzy-PI controllers. J Intell Manuf 34, 3015–3024 (2023). https://doi.org/10.1007/s10845-022-01993-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-022-01993-2