機器學習和元啟發式最佳化演算法應用於TFT-LCD廠製程參數設定之研究_

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題名：	機器學習和元啟發式最佳化演算法應用於TFT-LCD廠製程參數設定之研究
作者：	熊碩成
作者(外文)：	Shou-Cheng Hsiung
校院名稱：	義守大學
系所名稱：	工業管理學系
指導教授：	曹以明
學位類別：	博士
出版日期：	2023
主題關鍵詞：	薄膜電晶體液晶顯示器；智慧製造；特徵選擇；機器學習；最佳化演算法；Thin film transistor liquid crystal display (TFT-LCD)；Intelligent manufacturing；Feature selection；Machine learning；Optimization algorithm
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:0

柱狀間隔物 (CS)是薄膜晶體管液晶顯示器 (TFT-LCD) 產業中彩色濾光片 (CF) 的重要組成。 CS 除了有助於保持 CF 和 TFT 基板之間的距離一致之外，還可以防止液晶 (LC) 洩漏和漏光。
CS數據集的特徵包括7個類別：CL(清潔)、CO(塗佈)、H(熱板)、U(紫外線)、A(校準器)、D(顯影)和O(烤箱)。CS數據集的特點是數據量少、維度高（同時具有類別及數值型態特徵），而且CS製程的結果不夠穩定。本研究旨在應用機器學習和元啟發式最佳化演算法來求解TFT-LCD產業耗時的參數設定問題。
利用半監督機器學習中“先聚類後標記”的概念，本研究提出了一個三階段的求解框架，依序為：構建預測模型、最佳化和新進指派。根據實證研究，得到五項結果。
1.本研究透過數據預處理和混合特徵選擇技術從223個不需要調整的特徵中選擇了30個特徵，用來分群及建模以避免過度擬合。
2.本研究利用“K-means++”演算法將數據分群為三組，並由三種眾所周知的評估方法確定分群個數。然後，為集群內的每個成員指派相同的類別標籤以進行建模。
3.本研究為每個類別群組建立了具有不同策略的多元線性回歸預測模型。
4.本研究比較了 14 個優化器，並確定Harris Hawks Optimizer (HHO) 是最有效率及最穩定的。能夠在1秒鐘內獲得與實際測量CS高度非常吻合的結果。
5.本研究比較了5個分類器，確定分類回歸樹 (CART) 分類器是最合適的。在選定不需要調整的熱板特徵中，發現，H2、H4、H5 和 H6都是分類的關鍵特徵(分割器)。預設分割器的值可以指派新進，以利用之前為各個類別群組建立的預測模型和優化器。
提出的方法可以擴展到解決其它具有少量高維數據集特徵的智慧製造問題，從而提升產業競爭力。

以文找文

Column spacer (CS) is an essential component of the color filter (CF) in the thin-film transistor liquid crystal display (TFT-LCD) industry. In addition to helping maintain a consistent distance between CF and TFT substrates, CS can also prevent liquid crystal (LC) leakage and light leakage.
The features of the CS dataset include 7 categories: CL (clean), CO (coater), H (hot plate), U (UV light), A (aligner), D (development), and O (oven). The characteristics of the CS data set are small amounts with high dimensionality (both categorical and numerical features), and the results of the CS fabrication process are not stable enough. This study aims to apply machine learning and metaheuristic optimization algorithms to solve the time-consuming problem of parameter setting in the TFT-LCD industry.
Leveraging the concept of “cluster-then-label” in semi-supervised machine learning, this research proposed a solution framework consisting of three sequence stages: constructing the prediction model, optimizing, and newcomer assignment. According to the empirical study, five results were obtained.
1. By adopting data preprocessing and hybrid feature selection techniques, this study selected 30 features out of 223 features that do not require adjustments for clustering and modeling to avoid overfitting.
2. This study used the “K-means++” algorithm to partition the data into three groups and determine the number of clusters by three well-known evaluation methods. Then, the same category label is assigned to each member within the cluster for modeling.
3. This study constructed distinct multiple linear regression prediction models with different strategies for each category group.
4. This study compared 14 optimizers and determined that Harris Hawks Optimizer (HHO) was the most efficient and stable. The result closely matches the actual measured CS height and can be obtained within 1 second.
5. This study compared five classifiers and determined that the Classification and Regression Tree (CART) classifier was the most suitable. Among the selected hot plate features that do not require adjustment, it has been discovered that H2, H4, H5, and H6 are all critical features for classification (splitters). Pre-setting the value of splitters can assign newcomers to take advantage of previously built prediction models and optimizers for each category group.
The proposed approach can be extended to solve other intelligent manufacturing problems characterized by a small number of high-dimensional datasets, thereby enhancing the competitiveness of the industry.

以文找文

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