臺灣博碩士論文加值系統

本研究提出液晶顯示器之直下型背光光學效能最佳化之研究，主要在訴求如何得到直下型背光板在顯示面光學的最大輝度與最佳光學均勻性。本研究具有光學輝度的內隱式限制條件(implicit constraint)及光學均勻性的內隱式目標函數(implicit objective function)的特性，由本研究來探討液晶顯示器之直下型背光光學效能的最佳化問題，藉由幾何尺寸的變數變化，來得到顯示面光學上的最佳品質，由於尺寸變數為離散型，因此將利用“Sequential neural network approximation method”(SNA法)，來解這種具有離散變數特性的問題。
在此研究中只要利用少數的訓練資料，來訓練二個倒傳遞類神經網路(back-propagation neural network)來模擬目標函數及設計點的可行領域，再利用搜尋的演算法在類神經網路的可行領域與目標函數值的領域中搜尋最佳點，這個新的設計點經由Speos光學模擬軟體的計算，再與內隱式限制條件比對，再加入這個新的訓練資料，且再一次的訓練，這個過程以迭代的模式運作，直到我們反覆得到相同的設計點，也就是沒有新的訓練點被產生，稱為最佳點。本論文中採用了一個2變數及一個3變數的液晶顯示器之直下型背光光學效能最佳化的問題，來展示本研究所發展出的方法。

This thesis is on optical efficiency optimization for a direct type backlight of the liquid crystal display. The goal is to get the greatest uniformity in a direct type backlight, while the brightness is maintained in a satisfactory level. Both uniformity and brightness are implicit functions that have to be evaluated by optical simulation software Speos. This research will adjust the geometric dimensions, which are discrete design variables, to get the best optical efficiency.
The Sequential Neural Network Approximation Method (the SNA method) is used in this research. In the SNA method, two back-propagation neural network are trained to simulate the rough maps of the feasible domain and the objective function of this optimization problem using a few representative training data. A search algorithm then searches for the “optimal point” in the feasible domain and the objective function simulated by the neural network. This new design point is simulated by the optical simulation software to check its true objective values and whether it is feasible. This new information is then added to the training set and the neural network is trained again. Then we search for the “optimal point” in this new approximated feasible domain again. This process continues in an iterative manner until the approximate model insists the same “optimal point” in consecutive iterations.
In this thesis, a two-variable example is used to illustrate the process of SNA. A four-variable optical efficiency optimization problem for a direct type backlight of the liquid crystal display is then solved using the SNA method. In both examples, the number of optical simulations required is greatly reduced.

書名頁 ………………………………………………………………… i
論文口試委員審定書 ………………………………………………… ii
授權書 ……………………………………………………………… iii
中文提要 ……………………………………………………………… iv
英文提要 ……………………………………………………………… v
誌謝 …………………………………………………………………… vi
目錄 …………………………………………………………………… vii
表目錄 ………………………………………………………………… viii
圖目錄 ………………………………………………………………… ix
第一章　研究背景與目的 …………………………………………… 1
第二章　液晶顯示器之直下型背光光學結構設計 ………………… 4
2.1 大型液晶電視面板的基本架構 …………………………… 4
2.2 直下型背光的基本架構 …………………………………… 8
2.3 直下型背光光學結構設計及光學量測與檢證 …………… 13
第三章　Speos光學模擬軟體與光學原理簡介……………………… 16
3.1 Speos光學模擬軟體的運算流程…………………………… 16
3.2 光學原理……………………………………………………… 17
3.3 Speos光學模擬誤差定義…………………………………… 22
第四章　液晶顯示器之直下型背光光學效能最佳化設計數學模型 ………………………………………………………………… 24
4.1 參數、變數的定義…………………………………………… 24
4.2 最佳化設計數學模型………………………………………… 27
第五章　使用SNA法進行直下型背光光學效能最佳化設計………… 28
5.1 SNA基本架構………………………………………………… 28
5.2 利用直交表建立初始訓練資料……………………………… 31
5.3 訓練類神經網路……………………………………………… 34
5.4 搜尋具有均勻性最大值的可行設計點……………………… 37
5.5 Speos光學模擬流程………………………………………… 40
5.6 目標函數與限制條件的訓練過程…………………………… 43
5.7 使用Restart strategy……………………………………… 48
5.8 以4個離散變數進行直下型背光光學效能最佳化設計…… 53
第六章　結論與未來展望……………………………………………… 59
6.1 結論…………………………………………………………… 59
6.2 未來展望……………………………………………………… 59
參考文獻 …………………………………………………………… 61

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