Journal
OPTIK
Volume 121, Issue 24, Pages 2245-2249Publisher
ELSEVIER GMBH, URBAN & FISCHER VERLAG
DOI: 10.1016/j.ijleo.2009.09.007
Keywords
Backlight module; Prism-pattern design; Light guide plate; Neural-network optical model; Luminance uniformity
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This paper proposes a neural-network optical model for a backlight module of a liquid crystal display (LCD) to expedite the design of the light-scattering prism-pattern of its light guide plate (LGP). First, the prism surface of a light guide plate is divided into several equal regions. Then the neural-network optical model is implemented using a back-propagation neural network to establish the relationship between the distribution density of the prism pattern and the exiting-light luminance of the LGP at each region. The input-output patterns for the neural network training and verification are generated using orthogonal arrays and ASAP simulation. Then a for-loop computational algorithm is executed to search an approximately optimal distribution density of the prism pattern using the neural-network optical model such that high luminance uniformity is achieved. It is demonstrated by the case study of a 13 in. LCD backlight module that luminance uniformity could reach 93.1%. Thus it can be concluded that the developed neural-network optical model effectively expedites the LGP prism-pattern design. (C) 2010 Elsevier GmbH. All rights reserved.
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