臺灣博碩士論文加值系統

現今在市場中，有許多類型的顯示器，較常見且需求量較大的顯示器為薄膜電晶體液晶顯示器(thin film transistor liquid crystal display, 簡稱TFT-LCD)。TFT-LCD易受環境等因素影響，並在製程中會透過LCM老化檢測與光學檢測進行檢測。而在以往的TFT-LCD檢測方式，是透過人眼檢測，但該方式不僅耗費人力與時間成本，也造成產能和產品品質下降的問題。因此，本研究以導入物聯網(Internet of Things,簡稱IoT)技術來改善TFT-LCD檢測流程。

LCM檢測流程分為兩部分，第一部份為老化檢測，主要是將LCM放置在高溫的環境下運行，加速LCM老化。且透過量測LCM在特定畫面下的電壓電流值判斷是否異常。第二部分為光學檢測，透過量測到的色座標、色域、亮度、色溫、Gamma等光學參數判斷LCM的好壞。

本研究建置LCM光學與老化檢測之物聯網系統來改善LCM的檢測流程。其中，透過自行設計的複合式LCM驅動量測電路來驅動不同類型的LCM。該電路是透過STM32中的Cortex-M3系列微控制器來設計檢測流程的韌體。在老化檢測時，透過複合式LCM驅動量測電路來量測LCM在特定畫面時的電壓電流值，並採用無線韌體更新(Firmware Over-the-Air,簡稱FOTA)技術來解決傳統使用人力更新裝置韌體所造成的時間浪費。而在光學檢測時，是透過UP Squared Board運行自行開發的光學檢測應用程式，在搭配複合式LCM驅動量測電路和光學儀器CA-310來量測光學參數。最後，將檢測的電壓電流和光學資料，上傳至雲端平台，以可視化方式呈現並透過機器學習對LCM進行等級分類。

最後，本研究以LCM工廠中的產線做為實驗環境。經過實際的實驗與測試，驗證了系統的可行性與穩定性。其中，可減少老化檢測時的時間與人力成本，以及提升檢測準確率。其中，在更換複合式LCM驅動量測電路韌體所耗費的時間約為1分12秒。相對比人工操作，可節省約80%以上時間。而在光學檢測中，藉由量測LCM模組的光學特性參數來判斷該LCM模組是否符合規格，並將光學參數上傳至雲端平台進行等級分類。最後，在網路通訊部分，使用學校5G場域進行測試與驗證，測試其使用在系統上的可行性與前瞻性。

Nowadays, the transistor liquid crystal display (TFT-LCD) is the most widely used one among various types of displays which becomes the mainstream in the display industry currently. Due to environment or other factors that would affect TFT-LCD, there would be aging and optical detections conducted during the production. However, these tests were implemented through humans’ eyes. It not only caused a waste of human resources and time costs, but also made the problem of overall productivity and product quality degradation. Therefore, this study aims to improve the TFT-LCD detection process through the Internet of Things (IoT).

The process of testing is divided into two parts. The first part is the aging detections, which is mainly to place LCM in a high-temperature environment to accelerate the LCM aging. And by measure the voltage and current value in the LCM's particular picture to determine whether it is abnormal. The second part is optical detection, which judges the quality of LCM through the measure optical parameters such as Chromaticity Coordinates, color gamut, brightness, color temperature, and Gamma.

In this research, build a LCM optics and aging detection IoT system for improve the LCM detection process. Among them, a Composite LCM Drive Measurement Circuit is designed which could drive multiple types of LCM. This Composite LCM Drive Measurement Circuit mainly uses Cortex-M3 series microcontroller in STM32 to design the detection process firmware. During the aging detection, using the Composite LCM Drive Measurement Circuit to measure the voltage and current in a particular picture, and use Firmware Over-the-Air(FOTA) technology is to solve the traditional of through manpower update Time waste caused by device firmware. In the optical detection, the self-developed optical inspection application is run through the UP Squared Board, and the optical parameters are measured with the composite LCM driving measurement circuit and the optical instrument CA-310. Finally, upload the detected voltage, current and optical data to the cloud platform.The data would be presented in a visual way and classified LCM through machine learning.

Fially,In this research uses the production line in LCM factory as experimental environment. Through the experiments and testing, it has verified the feasibility and stability of the system. The aging detection not only reduces the waste of time and human resources, but also increases the accuracy of testing. Additionally, compared with manual operation, the replacing time of the firmware in the composite LCM drive measurement device takes about 1 minute and 12 seconds which could save more than 80% of time. In the optical detection, it would determine whether the LCM module meets the specifications through the optical parameters of LCM module, and upload these optical parameters to the cloud platform for classification. Finally, in the part of network communication, the school 5G field is used for testing and verification to examine the feasibility and forward-looking of its use on the system.

摘要...i
Abstract...iii
誌謝...v
目錄...vi
表目錄...viii
圖目錄...ix
第一章 緒論...1
1.1 研究背景...1
1.2 研究目的...2
1.3 國內外相關之研究情況...3
第二章 系統原理...6
2.1 STM32微控制器...6
2.1.1 STM32F103VET6...6
2.2 I2C通訊協定...7
2.2.1 I2C概念...7
2.2.2 I2C工作原理...8
2.3 MQTT...9
2.3.1 MQTT通訊原理...9
2.4 MD5訊息摘要演算法...10
2.4.1 MD5訊息摘要演算法介紹...10
2.4.2 MD5訊息摘要演算法說明...11
2.5 YMODEM傳輸協定...16
2.5.1 YMODEM符號定義...16
2.5.2 YMODEM資料傳輸格式...16
2.5.3 YMODEM協定交握機制...17
2.6 FOTA更新技術...20
2.6.1 IAP韌體更新技術...20
2.7 CA-310設備簡介...22
2.8 UP SQUARED BOARD...23
第三章 系統設計...25
3.1 系統架構...25
3.2 複合式LCM驅動量測電路設計...26
3.3 LCM老化檢測與遠端更新系統設計...29
3.3.1 複合式LCM驅動量測電路韌體設計...31
3.3.2 ESP8266韌體設計...34
3.4 LCM光學量測系統設計...36
3.4.1 光學檢測應用程式設計...43
3.5 雲端平台設計...44
第四章 系統實驗與驗證...50
4.1 I2C通訊協定驗證...52
4.2 LCM老化檢測與遠端更新系統測試...53
4.2.1 LCM背光電壓電流量測驗證...53
4.2.2 LCM老化電流數值比較...55
4.2.3 裝置基本韌體資訊上傳雲端平台測試...57
4.2.4 電壓電流值上傳雲端平台測試...58
4.2.5 FOTA更新測試...59
4.3 LCM光學量測系統驗證...65
4.3.1 光學檢測應用程式測試...65
4.3.2 LCM光學參數量測...72
4.4 4G和5G下載時間與速度測試與比較...73
4.4.1 4G與5G下載時間與速度測試...74
4.4.2 4G和5G下載測試結果分析...74
第五章 結論與未來展望...77
5.1 結論...77
5.2 未來展望...78
參考文獻...79
Extended Abstract...82

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