Enhancing BEF Luminance for TFT-LCD Industries using the Hybrid Approach of Prediction and Optimization Techniques

A Backlight Module is the main source supply of liquid crystal display components. Its power consumption is about half the liquid crystal display. Among the Backlight Module, BEF / DBEF enhances luminance 50% to 100%. Therefore BEF (Brightness Enhancement Film) / DBEF (Dual Brightness Enhancement Film) becomes the main critical component of Backlight Module. The assurance of sustaining balance for both the high luminance with low consumption is a challenging research issue for industries. Although in the past there many research works have been done about the light guide plate and light source of the Backlight Module, however rare research has been reported about enhancing the brightness film based on constrained production costs. Since there are many manufacturing factors influencing BEF’s luminance, in this study we attempt to find out the suitable manufacturing control parameters by employing the hybrid approach of both prediction and optimization techniques. The neural network is used to learn the experimental data set from real-time collected operation data. After the BEF prediction model is constructed and tested with acceptable prediction performance, the genetic algorithms (GA) is applied to the prediction model to find out the most suitable operation control parameters that produce the expected BEF luminance. The experimental results show that the proposed approach is able to increase the BEF luminance up to 12% with competitive strength and potentials of manufacturing costs reduction.