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Fatima SA, Zabiri H, Taqvi SAA, Ramli N, Maulud AS. Intelligent Control of an Industrial Debutanizer Column. Chem Eng Technol 2022. [DOI: 10.1002/ceat.202100039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Syeda Anmol Fatima
- Chemical Engineering Department Universiti Teknologi PETRONAS Seri Iskandar 32610 Perak Darul Ridzuan Malaysia
| | - Haslinda Zabiri
- Chemical Engineering Department Universiti Teknologi PETRONAS Seri Iskandar 32610 Perak Darul Ridzuan Malaysia
- CO2 Research Center (CO2RES) Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Perak Malaysia
| | - Syed Ali Ammar Taqvi
- Department of Chemical Engineering NED University of Engineering & Technology Karachi Karachi Pakistan
- Neurocomputation Lab, National Centre of Artificial Intelligence NED University of Engineering and Technology Karachi 75270 Pakistan
| | - Nasser Ramli
- Centre for Process System Engineering, Institute of Autonomous System Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Perak Malaysia
| | - Abdulhalim Shah Maulud
- Chemical Engineering Department Universiti Teknologi PETRONAS Seri Iskandar 32610 Perak Darul Ridzuan Malaysia
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Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment. ELECTRONICS 2022. [DOI: 10.3390/electronics11020253] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%.
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Xiao T, Wu Z, Christofides PD, Armaou A, Ni D. Recurrent Neural-Network-Based Model Predictive Control of a Plasma Etch Process. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c04251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tianqi Xiao
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhe Wu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore
| | - Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095-1592, United States
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095-1592, United States
| | - Antonios Armaou
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Chemical Engineering, University of Patras, 26243 Patras, Greece
| | - Dong Ni
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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4
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Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process. Processes (Basel) 2021. [DOI: 10.3390/pr9010151] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving optimization method is applied to overcome the error accumulation problem when using RNN models. The optimization goal is to achieve the desired average etching depth and average bottom roughness within the least amount of time. The simulation results show that our prediction model is accurate enough and the optimization objectives can be completed well.
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