課程目標：
本課程「視覺感測粒子控制」旨在培養學生在半導體過程中視覺感測與粒子控制領域的專業知識與實踐能力。透過問題導向學習(PBL)的教學方式，引導學生從理論到實踐，全面掌握視覺感測系統的基本原理、圖像處理技術、顆粒檢測方法以及在半導體行業的應用。
課程首先建立學生對視覺感測系統基本構成的理解，包括光源系統、光學系統、感測元件、圖像處理系統等核心組件的工作原理和特性。接著深入探討影像處理與分析技術，從基本的點處理、區域處理到高階的頻域處理，讓學生掌握影像增強、復原、分割等關鍵技術。在此基礎上，課程聚焦於顆粒檢測技術，介紹從傳統光學方法到先進的激光衍射、動態光散射等多種粒徑分析原理，以及它們在半導體過程潔淨度控制中的應用。
隨著半導體製程技術的不斷進步，視覺檢測在製程中扮演越來越重要的角色。本課程將帶領學生了解視覺檢測在晶圓製造、光罩檢查、封裝測試等環節的具體應用，並探討AI與機器學習如何革新傳統視覺檢測方法，提升檢測精準度與效率。課程也將介紹視覺檢測系統的自動化整合技術，以及如何進行系統測試與驗證，以確保檢測結果的可靠性。
最後，課程透過Arduino軟硬件實踐專題，讓學生親手設計並實現PM2.5顆粒物檢測系統，將理論知識轉化為實際應用能力。學生將學習傳感器選擇、電路設計、程式編寫、數據處理與視覺化等全流程技能，培養解決實際工程問題的能力。
透過本課程的學習，學生將能夠理解並應用視覺感測與粒子控制的核心技術，具備在半導體產業相關崗位的專業素養，並能夠跟隨技術發展趨勢，持續學習與創新，為未來智慧製造領域的發展做出貢獻。
課程特色：
本課程「視覺感測粒子控制」旨在培養學生在半導體過程中視覺感測與粒子控制領域的專業知識與實踐能力。透過問題導向學習(PBL)的教學方式，引導學生從理論到實踐，全面掌握視覺感測系統的基本原理、圖像處理技術、顆粒檢測方法以及在半導體行業的應用。
課程首先建立學生對視覺感測系統基本構成的理解，包括光源系統、光學系統、感測元件、圖像處理系統等核心組件的工作原理和特性。接著深入探討影像處理與分析技術，從基本的點處理、區域處理到高階的頻域處理，讓學生掌握影像增強、復原、分割等關鍵技術。在此基礎上，課程聚焦於顆粒檢測技術，介紹從傳統光學方法到先進的激光衍射、動態光散射等多種粒徑分析原理，以及它們在半導體過程潔淨度控制中的應用。
隨著半導體製程技術的不斷進步，視覺檢測在製程中扮演越來越重要的角色。本課程將帶領學生了解視覺檢測在晶圓製造、光罩檢查、封裝測試等環節的具體應用，並探討AI與機器學習如何革新傳統視覺檢測方法，提升檢測精準度與效率。課程也將介紹視覺檢測系統的自動化整合技術，以及如何進行系統測試與驗證，以確保檢測結果的可靠性。
最後，課程透過Arduino軟硬件實踐專題，讓學生親手設計並實現PM2.5顆粒物檢測系統，將理論知識轉化為實際應用能力。學生將學習傳感器選擇、電路設計、程式編寫、數據處理與視覺化等全流程技能，培養解決實際工程問題的能力。
透過本課程的學習，學生將能夠理解並應用視覺感測與粒子控制的核心技術，具備在半導體產業相關崗位的專業素養，並能夠跟隨技術發展趨勢，持續學習與創新，為未來智慧製造領域的發展做出貢獻。
Course Target:
This course, "Visual Sensing Particle Control", aims to cultivate students' professional knowledge and practical abilities in the visual sensing and particle control domains during the semiconductor process. Through the teaching methods of problem-oriented learning (PBL), students are guided to learn from theory to practice and fully grasp the basic principles of visual sensing systems, image processing technology, particle detection methods and applications in the semiconductor industry.
The course first establishes students' understanding of the basic structure of visual sensing systems, including the working principles and characteristics of core components such as light source systems, optical systems, sensing components, and image processing systems. Then we will conduct in-depth research on image processing and analysis technologies, from basic point processing, regional processing to high-level domain processing, so that students can master key technologies such as image enhancement, recovery, and segmentation. On this basis, the course focuses on particle detection technology, introducing the principles of particle analysis from traditional optical methods to advanced laser diffraction and dynamic light scattering, as well as their application in semiconductor process purification control.
With the continuous advancement of semiconductor process technology, visual inspection plays an increasingly important role in the process. This course will lead students to understand the specific application of visual inspection in circular circle manufacturing, mask inspection, packaging testing and other aspects, and explore how AI and machine learning innovate traditional visual inspection methods and improve detection accuracy and efficiency. The course will also introduce the automated integration technology of the visual inspection system, as well as how to conduct system testing and verification to ensure the reliability of the inspection results.
Finally, the course uses Arduino software hardware implementation topics to allow students to manually design and implement the PM2.5 particulate detection system, and transform theoretical knowledge into practical application capabilities. Students will learn full-process skills such as sensor selection, circuit design, programming, data processing and visualization, and cultivate the ability to solve practical engineering problems.
Through the learning of this course, students will be able to understand and apply the core technologies of visual sensing and particle control, have professional cultivation in the relevant positions of semiconductor industry, and can follow the trends of technological development, continue to learn and innovate, and make contributions to the development of the future smart manufacturing field.
Course Features:
This course, "Visual Sensing Particle Control", aims to cultivate students' professional knowledge and practical abilities in the visual sensing and particle control domains during the semiconductor process. Through the teaching methods of problem-oriented learning (PBL), students are guided to learn from theory to practice and fully grasp the basic principles of visual sensing systems, image processing technology, particle detection methods and applications in the semiconductor industry.
The course first establishes students' understanding of the basic structure of visual sensing systems, including the working principles and characteristics of core components such as light source systems, optical systems, sensing components, and image processing systems. Then we will conduct in-depth research on image processing and analysis technologies, from basic point processing, regional processing to high-level domain processing, so that students can master key technologies such as image enhancement, recovery, and segmentation. On this basis, the course focuses on particle detection technology, introducing the principles of particle analysis from traditional optical methods to advanced laser diffraction and dynamic light scattering, as well as their application in semiconductor process purification control.
With the continuous advancement of semiconductor process technology, visual inspection plays an increasingly important role in the process. This course will lead students to understand the specific application of visual inspection in circular circle manufacturing, mask inspection, packaging testing and other aspects, and explore how AI and machine learning innovate traditional visual inspection methods and improve detection accuracy and efficiency. The course will also introduce the automated integration technology of the visual inspection system, as well as how to conduct system testing and verification to ensure the reliability of the inspection results.
Finally, the course uses Arduino software hardware implementation topics to allow students to manually design and implement the PM2.5 particulate detection system, and transform theoretical knowledge into practical application capabilities. Students will learn full-process skills such as sensor selection, circuit design, programming, data processing and visualization, and cultivate the ability to solve practical engineering problems.
Through the learning of this course, students will be able to understand and apply the core technologies of visual sensing and particle control, have professional cultivation in the relevant positions of semiconductor industry, and can follow the trends of technological development, continue to learn and innovate, and make contributions to the development of the future smart manufacturing field.
自編
Self-edited
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中、末考+期中、末小考期中、末考+期中、末小考 Midterm, final exam + midterm, final exam |
60 | |
| 兩週一次作業兩週一次作業 Two-weekly operation |
10 | |
| 課堂點名課堂點名 Classroom name |
10 | |
| 2週彈性課程（20%）2週彈性課程（20%） 2-week flexible course (20%) |
20 |
