scholarly journals Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks

2019 ◽  
Vol 207 ◽  
pp. 1230-1245 ◽  
Author(s):  
Grigoriy Kimaev ◽  
Luis A. Ricardez-Sandoval
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Thin Film Deposition ◽  
Deposition Process ◽  
Film Deposition ◽  
Nonlinear Model Predictive Control

Predictive control of surface mean slope and roughness in a thin film deposition process

2010 ◽  
Vol 65 (16) ◽  
pp. 4720-4731 ◽  
Author(s):  
Xinyu Zhang ◽  
Gangshi Hu ◽  
Gerassimos Orkoulas ◽  
Panagiotis D. Christofides
Keyword(s):  
Thin Film ◽  
Predictive Control ◽  
Thin Film Deposition ◽  
Deposition Process ◽  
Film Deposition

Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes

2004 ◽  
Vol 128 (1) ◽  
pp. 315-325 ◽  
Author(s):  
Jionghua Jin ◽  
Huairui Guo ◽  
Shiyu Zhou
Keyword(s):  
Thin Film ◽  
Process Control ◽  
Statistical Process Control ◽  
Predictive Control ◽  
Thin Film Deposition ◽  
Deposition Process ◽  
Film Deposition ◽  
Generalized Predictive Control ◽  
Statistical Process ◽  
Production Run

This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.

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