這些年光電科技迅速發展，使得高品質的光學元件需求越來越高，而元件上的光學薄膜為了達到這些目標，隨著電腦科學日漸普及，能處理大量數學演算法也變得很重要。一個優異的模擬軟體可以在鍍膜前對於想要的目標值進行優化，也能夠在鍍膜後針對失敗的膜層分析其實際值與理想值的差異。
本實驗藉著簡型演算法來對光學薄膜進行優化，由簡型演算法文獻中可得知收斂至最佳解的辦法主要由反射、收縮與擴張這三個機制來主導，其如何移動頂點進行收斂影響著找尋最佳解的能力，因此針對這些機制的移動方法使用變動參數進行改善，修改後的簡型優化成功率提高10%，且優化結果與目標值的標準差最低可至0.08。當實際鍍膜的結果與模擬結果的光譜不同時，可以利用反向工程來找到原因，本實驗中反向工程的最佳結果能達到回推結果的光譜與實際鍍膜的光譜相關係數為1。若是初始值和目標值差異較大時，利用智能加層優化方法，此方法為新概念的加層辦法，除了能夠有效地優化光譜，達到優化結果與目標值的標準差低至0.23，還能避免薄膜厚度出現太薄的情況(小於5 nm)，優化出的膜層結構無不合理的膜層或厚度，可智能化的選擇最佳結構優化並達到使用者要求的目標值。

In recent years, the rapid development of optoelectronic technology has made the demand for high-quality optical components higher and higher. In order to achieve these goals, optical films on components have become more and more important. Therefore, an excellent simulation software can not only optimize for the desired target value before coating, but also analyze the difference between the actual value and the ideal value for the failed film after coating.
In this research, simplex algorithm is selected to optimize optical thin films. It is well known that the simplex algorithm mainly converges to the optimal solution based on the three mechanisms: reflection, contraction and expansion. Therefore, the movement of vertices has a great effect on finding an optimizing solution. In this thesis, using the adaptive parameters which is the most effective way to improve it. The rate of successful optimization can enhance 10% by modified simplex algorithm (MSA). Besides, the optimized value can be situated in 0.08 deviation of the target.
When the spectrum of real coating is different from simulation, reverse engineering (RE) can effectively estimate the film thickness difference between these two. A simple spectral fitting can achieve the coefficient of determination, denoted R2 or r2 and pronounced R squared (RSQ) equal to 1.  
Intelligent layering simplex algorithm (ILSA) can be used in the problems that the difference between the initial spectrum and the target is large. Its layering method is a new concept that has higher accuracy of optimization in the desired spectrum. Most important of all, this way can avoid unreasonable thickness that thinner than 5nm and the optimized value can be situated in 0.23 deviation of the target. ILSA is a more realistic algorithm within thin film coating regime.

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