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Abstract: 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 13in. 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. [ABSTRACT FROM AUTHOR]

Abstract: This research uses spiral microstructures to measure both the residual mean normal compressive/tensile stress and gradient stress of the thin films for MEMS devices. The relation between these two residual stresses and the resultant geometric or dimensional deformations is first established using the analytical solutions. Then based on this relation the deformations are measured and utilized to determine these two residual stresses. ANSYS simulation and experiments are conducted to verify the measurement. The deformations from ANSYS simulation for a microspiral subjected to the measured mean and gradient residual stresses agree well with those from experiments (less than 6% errors). Therefore, it can be concluded that the proposed measurement method is validated by the experimental results. [Copyright &y& Elsevier]

Abstract: Micro-injection molding, with advantages of easy mass production and low cost, is a key technology for producing micro components. Nevertheless, a low yield rate of high-quality molded parts is common due to problems associated with geometric precision, molecular orientation, and optical properties. Solutions to such problems must consider the machine, mold design, and process parameter settings. However, optimal performance becomes relatively less attainable when process parameters deviate due to inevitable process tolerances and change in an operation environment. This study has two goals: (1) fabrication of high-precision mold inserts using UV-LIGA; and, (2) identification of robust parameters that ensure production quality. Two gear mold-inserts with outside diameters of approximate 6mm and 4mm, named as the 6mm and 4mm gears, are introduced. First, lithography of thick photoresist SU8-50 is utilized as the initial structure seed layer to electroform the Ni gear mold insert. Second, this study investigates two key geometrical dimensions-the outside diameter and tooth thickness of the molded gears. The robust optimization of multiple objectives is introduced to identify robust parameter settings with high dimensional accuracy and high yield rates for molded gears despite process parameter deviations. Experimental results indicate that electroformed Ni mold inserts have good quality and are successfully utilized in the subsequent micro-injection molding process, demonstrating the feasibility of the mold inserts fabricated using UV-LIGA. The micro-injection molding experiments suggest that mold temperature, holding pressure, and injection speed have significant effects on dimensional quality characteristics of molded gears. The robust parameters derived from the proposed method increase yield rates of the 6mm gear from 10% to 91%, and those of the 4mm gear from 38% to 93% in comparison with the initial point, thereby demonstrating application effectiveness. [Copyright &y& Elsevier]