本研究主要以近接曝光微影術（UV-proximity printing）來進行製作微光學元件：微透鏡或微透鏡陣列、光波導。另外，以田口方法穩健化設計結合類神經網路(ANN)和基因演算法(GA)建立高填充率微透鏡陣列之製程模型，尋找出微透鏡焦距均一性之最佳參數組合。在光學元件製作中，係利用曝光時光罩和光阻之間的間距與光罩的設計產生光學元件的結構。微透鏡陣列得以使用PDMS之光學材料進行複製。新光波導結構為具有垂直和水平錐度之平躺半圓錐光波導，則製作需再搭配雙光罩之方式，使得光源波長在1550nm與光纖之間耦合效率可達到52%。高填充率微透鏡陣列使用穩健化設計與類神經網路/基因演算法降低微透鏡陣列的焦距變異。以直交表 作為實驗配置的依據，計算出品質特性的訊噪比，類神經網路以 直交表來作為學習的樣本建構製程模型，可預期任意參數的焦距，再以基因演算法尋找出微透鏡焦距均一性最佳的參數組合，與實驗結果相比較；ANN模型的預測誤差大約2%，實驗結果證明田口方法與ANN/GA可分別降低變異敏感性34% 和56%。

This thesis aims to develop the micro optical component production using UV-proximity printing, such as microlens array molds and optical waveguides are illustrated. In addition, artificial neural network and genetic algorithm technologies are combined with the Taguchi method to create a robust design for the high fill factor microlens array modeling. The finding of the robust parameters combination can result in the uniform microlens array fabrication. The optical component profiles were produced by utilizing a printing gap between the mask and photoresist substrate. The microlens array can use PDMS optical material of to replicate in mass production. A horizontal frustum optical waveguide with a both lateral and vertical taper structure was produced. The orthogonal and inclined masks with the diffraction effect were employed in the lithography process. A horizontal frustum optical waveguide provides a coupling efficiency higher than 52% from laser diode to the single-mode fiber when using 1550 nm laser diode. The artificial neural network and genetic algorithm technologies are combined with the robust design to reduce the variations in the focal length of the high fill factor microlens array. The orthogonal array was used for these experiments and calculated the S/N ratio of quality characteristic. The orthogonal array was used as the learning data for the artificial neural network to construct system model that can predict the focal length for arbitrary parameters setting. Then, the genetic algorithm was applied to obtain the parameters setting. The ANN/GA model is compared with the experimental result, the predicted error is about 2%. The experimental results prove that 34% and 56% reductions in sensitivity variation can be achieved using the Taguchi method and the ANN/GA model, respectively.

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