應用基因演算法於光轉置技術矩陣轉移之研究

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姓名

孫浩倫(Hao-Lun Sun) 查詢紙本館藏

畢業系所

光機電工程研究所

論文名稱

應用基因演算法於光轉置技術矩陣轉移之研究
(Application of Genetic Algorithm in Optical Transposition Technology: A Study of Matrix Transfer)

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摘要(中)

近年來顯示器產業的發展飛速般成長，Mini/Micro LED 的巨量轉移技術仍尚未成熟，
故本研究比較不同的巨量轉移技術，並探討如何在光轉置技術中應用最佳化演算法進行
矩陣轉移。意旨利用雷射誘導前向轉移(Laser Induced Forward Transfer, LIFT)技術，開發
一種基於最小公倍數邏輯的面轉移方法，且可滿足隨意調整接收基板上 LED 之間的間
距。
本研究運用最佳化演算法解決面轉移中的優化問題，嘗試以較短的時間、較少的次
數完成轉移，並透過 GUI 介面顯示運算與模擬結果。這些演算法基於最小公倍數原理，
將路徑進行最佳化計算，以找到最佳的轉移路徑和參數設定。
研究結果表明，將最佳化演算法應用於光轉置技術的矩陣轉移之方法，相較於逐顆轉移
可以大幅提高轉移的效率。這種基於最小公倍數概念的面轉移方法在模擬中取得了良好
的效果，能夠在短時間內完成大面積的巨量轉移。
本研究的成果對於光轉置技術的應用具有重要意義，除了使用在 LED 巨量轉移上，
還可應用於其他不同的製程：如 LED 分選、微小矩陣元件之轉移等等。故本研究之貢
獻在於提出一套矩陣轉移邏輯結合最佳化演算法，用來解決目前 LIFT 在可調變節距的
需求下轉移過慢之問題，且為少數應用於提高 LIFT 速度的最佳化演算法。

摘要(英)

In recent years, the display industry has witnessed rapid growth and development. However,
the mass transfer technology for Mini/Micro LED is still in its nascent stage. Therefore, this
study aims to compare various mass transfer techniques and explore the application of
optimization algorithms in the field of optical transposition technology, specifically focusing
on Laser Induced Forward Transfer (LIFT). The goal is to develop a matrix transfer method
based on the principles of the least common multiple (LCM) logic, which allows for flexible
adjustment of the spacing between LEDs on the receiving substrate.
This research employs optimization algorithms to address optimization challenges in matrix
transfer. The aim is to achieve transfer with reduced time and minimal iterations through LIFT
technology, while presenting computation and simulation results through a graphical user
interface (GUI). These algorithms, rooted in the concept of least common multiple, optimize
the transfer path calculations to identify the optimal transfer path and parameter settings.
The research findings indicate that applying optimization algorithms to matrix transfer in
optical transposition technology significantly enhances transfer efficiency compared to
individual LED transfers. This LCM-based matrix transfer approach demonstrates promising
simulation results, achieving efficient mass transfer over a large area within a short time.
The outcomes of this research hold significant implications for the application of optical
transposition technology. Apart from its usage in mass transfer for LEDs, this approach can be
extended to various processes, such as LED sorting and transfer of micro-matrix components.
Thus, the contribution of this research lies in proposing a matrix transfer logic integrated with
optimization algorithms to address the issue of slow transfer speeds in LIFT under the
requirement of adjustable spacing. This approach stands as one of the few optimization
algorithms aimed at enhancing LIFT speed and holds relevance for its potential application
across diverse fields.

關鍵字(中)

★ 最佳化演算法
★ 矩陣轉移
★ 雷射前向誘導
★ 巨量轉移

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章緒論 1
1-1 研究動機與目的 1
1-2 文獻回顧 4
1-3 文章架構 7
第二章運作系統與原理 8
2-1 巨量轉移機台介紹 8
2-2 轉移基礎原理 9
第三章矩陣轉移 12
3-1 龜圖學模組 12
3-2 貢獻基板分布建立 13
3-2-1 比對結果分布 13
3-2-2 隨機與全滿分布 14
3-2-3 分Bin分布 15
3-3 最小公倍數邏輯 15
3-3-1 最大公因數-最小公倍數定理(GCD-LCM Thm.) 17
3-3-2 歐基里德演算法 18
3-4 分群邏輯 19
3-5 對位位置邏輯 20
3-6 距離計算方法 22
第四章最佳化演算法 23
4-1 研究背景 23
4-1-1 旅行銷售員問題 23
4-1-2 時間複雜度與空間複雜度(Big O notation) 23
4-1-3 P、NP問題 25
4-2 基因演算法 27
4-2-1 編碼 28
4-2-2 適應函數（Fitness Function） 32
4-2-3 選擇(Select)與複製(Reproduction) 34
4-2-4 交配 36
4-2-5 突變 37
4-3 粒子群演算法 38
4-4 程式架構 40
第五章模擬結果與討論 42
5-1 D6×6,0.3×0.2&R6×6,0.5×0.3 42
5-2 其他轉移結果 49
5-2-1 D30×30,0.3125×0.3125&R15×15,0.625×0.625 49
5-2-2 D15×15,0.5×0.5&R15×15,0.625×0.625 50
5-2-3 D192×96,0.15625×0.3125&R48×48,0.625×0.625 51
5-2-4 分bin轉移 52
第六章結論與未來展望 54
6-1 結論 54
6-2 未來展望 55
參考文獻 56

參考文獻

參考文獻
〔1〕台灣平面顯示器材料與元件產業協會經濟部工業局, 顯示器材料產業 2022-2024
專業人才需求推估調查, 110 年 12 月
〔2〕Byoung-Joo Lee, Dahl-Young Khang, Non-deterministic transfer-printing of LED chips
with controllable pitch using stretchable elastomeric stamps,Department of Materials Science
and Engineering, Yonsei University, Seoul 03722, Republic of Korea, 14 January 2021
〔3〕Kai Ding,Vitaliy Avrutin, Natalia Izyumskaya, Micro-LEDs, a Manufacturability
Perspective, Department of Electrical and Computer Engineering, Virginia Commonwealth
University, 601 West Main Street, Richmond, VA 23284, USA ,Appl. Sci. 2019, 9, 1206;
doi:10.3390/app9061206, 22 March 2019
〔4〕Jiayi Li, Bingqing Luo, Zhaojun Liu, Micro-LED Mass Transfer Technologies,
IEEE,2020 21st International Conference on Electronic Packaging Technology (ICEPT), 12-
15 August 2020
〔5〕Sheng-I Chen,Yen-Che Tseng, A Partitioning Column Approach For Solving Led
Sorter Manipulator Path Planning Problems,Journal of Industrial and Management
Optimization Vol. 18, No. 3, May 2022, pp. 2033–2047,doi:10.3934/jimo.2021055
〔6〕E. L. Lawler, D. E. Wood, Branch-and-Bound Methods: A Survey, Operations
ResearchVol. 14, No. 4, 1 Aug 1966
〔7〕You-Jin Park , Gilseung Ahn , Sun Hur, Optimization of Pick-and-Place in Die Attach
Process, Applied Soft Computing Volume 68, July 2018, Pages 856-865
〔8〕Pei-Chann Chang , Wei-Hsiu Huang ,Ching-Jung Ting , Developing a varietal GA with
ESMA strategy for solving the pick and place problem in printed circuit board assembly line,
Journal of Intelligent Manufacturing volume 23, pages1589–1602 (2012), 19 September 2010
〔9〕Pere Serra, Alberto Piqué, Laser-Induced Forward Transfer: Fundamentals and
Applications, Advanced Materials Technologies Volume4, Issue1, 08 August 2018
57
〔10〕Han Woong Yoo, Shingo Ito, Georg Schitter, High speed laser scanning microscopy by
iterative learning control of a galvanometer scanner, Automation and Control Institute
(ACIN), Vienna University of Technology, Gusshausstr. 27-29, 1040 Vienna, Austria
〔11〕ProofWiki, Product of GCD and LCM, 取自
https://proofwiki.org/wiki/Product_of_GCD_and_LCM
〔12〕Hensley D., The Number of Steps in the Euclidean Algorithm, JOURNAL OF
NUMBER THEORY 2, 414422 (1970)
〔13〕David L. Applegate , Robert E. Bixby , Vašek Chvátal and William J. Cook, The
Traveling Salesman Problem, Princeton University Press,2007
〔14〕Ian Chivers & Jane Sleightholme, An Introduction to Algorithms and the Big O
Notation, Introduction to Programming with Fortran pp 359–364, 01 January 2015
〔15〕陳嘉珮, 運用基因演算法及最佳化資源分配法求解隨機需求之長期車輛路線問
題, 國立交通大學運輸科技與管理學系碩士班碩士論文, 民國 101 年 7 月 21 日
〔16〕Omid Motamedi Sedeh, Bakhtiar Ostadi, Farank Zagia, A novel hybrid GA-PSO
optimization technique for multi-location facility maintenance scheduling problem, Journal of
Building Engineering Volume 40, August 2021, 102348
〔17〕王亮達, 使用 PSO-GA 演算法運用於 TSP 問題上做移動式機器人路徑最佳化,南
台科技大學電機工程研究所海外研習專班碩士學位論文,民國 100 年 7 月

指導教授

董必正(Pi-Cheng Tung)

審核日期

2023-8-17

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