Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.

[1]  A. A. Islier.  A genetic algorithm approach for multiple criteria facility layout design , 1998 .

[2]  Per A. Hassel,et al.  Nonlinear partial least squares , 2003 .

[3]  G. Geoffrey Vining.  A Compromise Approach to Multiresponse Optimization , 1998 .

[4]  Cliff T. Ragsdale,et al.  Combining a neural network with a genetic algorithm for process parameter optimization , 2000 .

[5]  Jean-Pierre Gauchi,et al.  Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data , 2001 .

[6]  James R. Evans,et al.  The management and control of quality , 1989 .

[7]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[8]  Thomas J. McAvoy,et al.  Nonlinear PLS Modeling Using Neural Networks , 1992 .

[9]  Conrad Bessant,et al.  Optimisation of a neural network model for calibration of voltammetric data , 2002 .

[10]  Patrick Gallinari,et al.  FEATURE SELECTION WITH NEURAL NETWORKS , 1999 .

[11]  V. Roshan Joseph,et al.  IMPROVING THE YIELD OF PRINTED CIRCUIT BOARDS USING DESIGN OF EXPERIMENTS , 1999 .

[12]  E. Degarmo.  Materials and Processes in Manufacturing , 1974 .

[13]  Hyunbo Cho,et al.  Partial least square-based model predictive control for large-scale manufacturing processes , 2002 .

[14]  Edward C. Malthouse,et al.  Nonlinear partial least squares , 1997 .

[15]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[16]  Miguel Ayala Botto,et al.  A comparison of nonlinear predictive control techniques using neural network models , 1998, J. Syst. Archit..

[17]  Z Daren,et al.  QSPR studies of PCBs by the combination of genetic algorithms and PLS analysis. , 2001, Computers & chemistry.

[18]  Kailin Tang,et al.  Combining PLS with GA-GP for QSAR , 2002 .

[19]  T. S. Lia,et al.  Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm , 2003 .

[20]  Primoz Potocnik,et al.  Nonlinear model predictive control of a cutting process , 2002, Neurocomputing.

[21]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[22]  Stephen J. Wright,et al.  Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming , 2004, Comput. Optim. Appl..

[23]  Theodora Kourti,et al.  Statistical Process Control of Multivariate Processes , 1994 .

[24]  Eduardo Fernandez-Camacho,et al.  Model Predictive Control in the Process Industry , 1995 .

[25]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[26]  Chul Kim.  Production of shadow-mask-improved technology , 2002 .

[27]  S.Joe Qin,et al.  Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions , 1997 .