TY - GEN
T1 - Parameterized display performances behavioral modeling and optimization for TFT-LCD panel
AU - Huang, Hsuan Ming
AU - Li, Yi-Ming
PY - 2009
Y1 - 2009
N2 - In this paper, we implement a systematical method for thin film transistor liquid-crystal display (TFT-LCD) design optimization and sensitivity analysis. Based upon a three-dimensional (3D) field solver, Simulation Program with Integrated Circuit Emphasis (SPICE), and a Design of Experiments, we construct a second-order response surface model (RSM) for the performances of an interested TFT-LCD panel. The constructed RSMs are reduced using a step-wise regression. The adequacy and accuracy using the normal residual plots and their residual of squares are verified. According to the constructed models, we then analyze the sensitivity of the performances by considering the design parameters as changing factors (i.e., the size variation, position shift and driving setting) under an assumption of Gaussian distribution. We also could apply the models to optimize the designed circuit. The designing parameters of these models are selected and optimized to fit the designing target of the examined circuit by the genetic algorithm in our unified optimization framework. This computational statistics method can predict the TFT-LCD performances and show the engineering practicability in display panel industry.
AB - In this paper, we implement a systematical method for thin film transistor liquid-crystal display (TFT-LCD) design optimization and sensitivity analysis. Based upon a three-dimensional (3D) field solver, Simulation Program with Integrated Circuit Emphasis (SPICE), and a Design of Experiments, we construct a second-order response surface model (RSM) for the performances of an interested TFT-LCD panel. The constructed RSMs are reduced using a step-wise regression. The adequacy and accuracy using the normal residual plots and their residual of squares are verified. According to the constructed models, we then analyze the sensitivity of the performances by considering the design parameters as changing factors (i.e., the size variation, position shift and driving setting) under an assumption of Gaussian distribution. We also could apply the models to optimize the designed circuit. The designing parameters of these models are selected and optimized to fit the designing target of the examined circuit by the genetic algorithm in our unified optimization framework. This computational statistics method can predict the TFT-LCD performances and show the engineering practicability in display panel industry.
KW - Design of Experiments
KW - Design optimization
KW - Liquid-crystal display
KW - Modeling and simulation
KW - Response surface model
KW - Sensitivity analysis
KW - Thin film transistor
UR - http://www.scopus.com/inward/record.url?scp=77958072244&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77958072244
SN - 9781439817834
SN - 9781439817841
T3 - Technical Proceedings of the 2009 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2009
SP - 379
EP - 382
BT - Technical Proceedings of the 2009 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2009
T2 - Nanotechnology 2009: Biofuels, Renewable Energy, Coatings, Fluidics and Compact Modeling - 2009 NSTI Nanotechnology Conference and Expo, NSTI-Nanotech 2009
Y2 - 3 May 2009 through 7 May 2009
ER -