Neural networks for advanced process control

Mevawalla, Z. N.; May, G.S.; Kiehlbauch, Mark

doi:10.1109/asmc.2010.5551439

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Because modern plasma etching process involves physical and chemical mechanisms, theoretical modeling based on principles is difficult to construct. In addition, it has some limit in the process control and optimization application, especially processes real-time control due to the model size and complexity [4][5][6]. So as Correspondence: hlyou@mail.xidian.edu.cn an alternative to first principle models, empirical models based on statistical design of experiment plays an important role in plasma etching control and optimization [7].…”

Section: Introductionmentioning

confidence: 99%

Combined D-optimal design and generalized regression neural network for modeling of plasma etching rate

You

Chen

Liu

et al. 2014

Int. J. Metrol. Qual. Eng.

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Abstract. Plasma etching process plays a critical role in semiconductor manufacturing. Because physical and chemical mechanisms involved in plasma etching are extremely complicated, models supporting process control are difficult to construct. This paper uses a 35-run D-optimal design to efficiently collect data under well planned conditions for important controllable variables such as power, pressure, electrode gap and gas flows of Cl2 and He and the response, etching rate, for building an empirical underlying model. Since the relationship between the control and response variables could be highly nonlinear, a generalized regression neural network is used to select important model variables and their combination effects and to fit the model. Compared with the response surface methodology, the proposed method has better prediction performance in training and testing samples. A success application of the model to control the plasma etching process demonstrates the effectiveness of the methods.

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Section: Introductionmentioning

confidence: 99%

Combined D-optimal design and generalized regression neural network for modeling of plasma etching rate

You

Chen

Liu

et al. 2014

Int. J. Metrol. Qual. Eng.

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“…At the same time intelligent (AI) strategy based control [8][9][10][11][12][13] is now a trend as process system is becoming complex which is difficult to cope with conventional control strategy that requires accurate mathematical model. Many of them use neural network based control strategy [14][15][16][17][18]. Distributed implementation is also necessary to make the software available across several machines [19].…”

Section: Introductionmentioning

confidence: 99%

Advanced control software framework for process control applications

Hossain¹,

Hussain²,

Omar³

2014

IJCIS

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Industries are now moving towards PC-based control from PLC (Programmable Logic Control) based control as the PC (personal computer) is easily available and capable of implementing various control strategies to improve productivity. Advances in both hardware and software technology are expediting this move. Absence of a user friendly, cost effective and easy to apply advanced control strategy based software is the motivation for this work. In this paper we reviewed issues of software application in process control systems. We present the development of an advance process control software tool and its application on a pH neutralization plant. Neural network model based DIC (Direct Inverse Control) strategy is implemented to control the process. The application and results shows the viability and robustness of the advanced control software and its superiority over the conventional PID (proportional integral derivative) controller.

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