Abstract

This paper proposes an optimization method for designing a prism-pattern LCD light guide plate (LGP) using a neural-network optical model and a real-valued genetic algorithm to achieve excellent luminance uniformity in the exiting light. This newly developed method is proposed as a way of solving the complicated optimization work for non-image optics due to the numbers of ray tracing. First, a neural-network optical model is based on a back-propagation neural network. Then the neural-network optical model is incorporated into a real-valued genetic algorithm to optimize the distribution density of the prism pattern. The results show that the 13-point luminance uniformity reaches an outstanding 92.09%.

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References

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  1. H.-S. Ha, “Backlight Unit for Liquid Crystal Display,” US Patent Application Publication, Pub. No.: US 2005/0073828 A1 (2005).
  2. T. Ishikawa, “Surface Light Source Device of Side Light Type Having Diffusing Element with Improved Distribution Pattern of Light,” US Patent 5921651 (1999).
  3. E. Yagani, T. Fukunishi, O. Shoji, and N. Yoshida, “Back Lighting Device for a Liquid Crystal Panel,” US Patent 4937709 (1990).
  4. T.-C. Yu, C. Leu, and G.-L. Chen, “Light Guide Plate,” US Patent 6979112 (2005).
  5. J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).
  6. C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
    [Crossref]
  7. R. L. Haupt, and S. E. Haupt, Practical Genetic Algorithms (Wiley John & Sons, 2004).
  8. L. Davis, Handbook of Genetic Algorithms (Van Nostrand Reinhold, 1991).
  9. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, 1999).
  10. A. A. Adewuya, New Methods in Genetic Search with Real-valued Chromosomes, Master’s Thesis (MIT, 1996).
  11. C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
    [Crossref]
  12. D. E. Rumelhart, and J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations (MIT Press, 1986).

2008 (2)

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

2003 (1)

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

Chang, J.-G.

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

Cheng, M.-C.

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

Chu, W.-T.

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

Fang, Y.-C.

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

Huang, C.-C.

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

Li, C.-J.

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

Lin, C.-Y.

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

Yang, R.-J.

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

J. Soc. Inf. Disp. (1)

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Design of a Prism Light-guide Plate for an LCD Backlight Module,” J. Soc. Inf. Disp. 16(4), 545–550 (2008).
[Crossref]

Jpn. J. Appl. Phys. (1)

C.-J. Li, Y.-C. Fang, W.-T. Chu, and M.-C. Cheng, “Optimization of Light Guide Plate with Microstructures for Extra Light Modern Backlight Module,” Jpn. J. Appl. Phys. 47(8), 6683–6687 (2008).
[Crossref]

Opt. Eng. Mag. (1)

J.-G. Chang, C.-Y. Lin, C.-C. Huang, and R.-J. Yang, “Optical Design and Analysis of LCD Backlight Units Using ASAP,” Opt. Eng. Mag. 82, 75–89 (2003).

Other (9)

D. E. Rumelhart, and J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations (MIT Press, 1986).

R. L. Haupt, and S. E. Haupt, Practical Genetic Algorithms (Wiley John & Sons, 2004).

L. Davis, Handbook of Genetic Algorithms (Van Nostrand Reinhold, 1991).

S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, 1999).

A. A. Adewuya, New Methods in Genetic Search with Real-valued Chromosomes, Master’s Thesis (MIT, 1996).

H.-S. Ha, “Backlight Unit for Liquid Crystal Display,” US Patent Application Publication, Pub. No.: US 2005/0073828 A1 (2005).

T. Ishikawa, “Surface Light Source Device of Side Light Type Having Diffusing Element with Improved Distribution Pattern of Light,” US Patent 5921651 (1999).

E. Yagani, T. Fukunishi, O. Shoji, and N. Yoshida, “Back Lighting Device for a Liquid Crystal Panel,” US Patent 4937709 (1990).

T.-C. Yu, C. Leu, and G.-L. Chen, “Light Guide Plate,” US Patent 6979112 (2005).

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Figures (12)

Fig. 1
Fig. 1

A schematic diagram for an LCD backlight module with a side-type LGP.

Fig. 2
Fig. 2

Prism-pattern characteristics: angle θ, spacing P, depth H, and top length a defined for a trapezoid prism pattern of an LGP.

Fig. 3
Fig. 3

(a) An LGP model is divided into thirteen equal regions with a cold cathode fluorescent lamp (CCFL) adjacent to region No. 1; (b) the luminance value of the central point in each region is considered.

Fig. 4
Fig. 4

Structure of a neural network model with one hidden layer, where ωxh and ωhy are the weights, and θh and θyj are the bias values.

Fig. 5
Fig. 5

The flowchart of the genetic algorithm for the prism-pattern design of an LGP.

Fig. 6
Fig. 6

Luminance plots by ASAP with prism spacing P varying from 0.5mm to 1.5mm for (a) a trapezoid prism pattern; (b) a triangular prism pattern.

Fig. 7
Fig. 7

Total light flux vs. prism angle θ.

Fig. 8
Fig. 8

Luminance values with prism depth H varying from 0.01mm to 0.05mm.

Fig. 9
Fig. 9

Training errors vs. training epochs.

Fig. 10
Fig. 10

Convergence plot for the evolution of the LGP fitness values.

Fig. 11
Fig. 11

ASAP Luminance plot for the LGP with the optimal distribution density of the prism pattern obtained from the genetic algorithm.

Fig. 12
Fig. 12

Luminance values at the central points of 13 regions using the neural-network optical model versus ASAP simulation for the LGP with the optimal distribution density of the prism pattern.

Tables (2)

Tables Icon

Table 1. Analysis of luminance values at 30 points of each region

Tables Icon

Table 2 The output errors of the trained neural-network optical model for 9 verification sets of input-output patterns.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

Hh=f(neth)=11+eneth
Yj=f(netj)=11+enetj
neth=i=1NinpωihXiθh
netj=h=1NhidωhjHhθj

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