臺灣博碩士論文加值系統

半導體測試資料STDF（Standards Test Data Format）檔案中的Data Log資料，無法如同一般半導體測試結果的等級別（Bin）資料，將它儲存進關聯式資料庫（RDBMS）後進行分析，原因為單一個測試資料STDF 檔案之中，包含的測試資料就有數百萬筆，半導體封裝測試工廠進行半導體晶圓或者積體電路的測試後，每日獲得之STDF 檔案超過數千個。如果建立傳統資料庫進行儲存將耗費大量成本且不彈性。本研究利用NoSQL Document-Oriented（MongoDB）資料庫，建構一個儲存半導體測試資料雲端資料系統，這是個將半導體測試資料STDF 轉為JSON 格式存入私有雲中的資料庫，並利用Docker Container 快速彈性的方式，進行佈署所需要的NoSQL 資料庫。讓半導體測試工程師可以方便連結到NoSQL 資料庫查詢測試結果，且公司可以在因應需要投入相對應的軟硬體資源。系統驗證部分則使用Yahoo Cloud Serving Benchmark (YCSB) 對MongoDB 進行壓力測試驗證。

The testing of the data log file in the STDF archive containing the current semiconductor test information cannot complete as Bin (levels) of chips stored in the traditional relational database for analysis, the reason being that a single STDF archive with test data contains test data with millions of record, and the STDF files acquired through semiconductor tests of chips and integrated circuits executed every day in the factory amount to several thousands. The building of a traditional database for storage is very costly and in no way elastic. This research uses the NoSQL Document-Oriented（MongoDB）database to construct a cloud data system for semiconductor chip test data, and transforms STDF into JSON format stored in a private cloud, uses Docker Container flexible and fast approach to rapid deployment of NoSQL database, with which it enables the semiconductor test engineers to make fast connections with semiconductor materials querying databases, and the company can invest in corresponding hardware and software resources. This is used to resolve the company’s problem of not being able to store the semiconductor test data log in a traditional relational database. The system is used to verify parts of Yahoo Cloud Serving Benchmark (YCSB) for stress testing to verify MongoDB.

摘要I
Abstract II
致謝詞III
Table of Contents IV
List of Figures VI
List of Tables VII
1 簡介1
1.1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 論文目標與貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 研究背景與相關研究5
2.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 雲端運算. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 NoSQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 Dockers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.4 MongoDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.5 YCSB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.6 YCSB 的架構. . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2 相關研究. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 系統設計與實作20
3.1 建置環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 Notebook 實驗環境(Hosted Hypervisor) . . . . . . . . . . . 21
3.1.2 Server 實驗環境(Bare-Metal Hypervisor) . . . . . . . . . . 21
3.1.3 Client 實驗環境. . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.4 Cloud Database 的研究. . . . . . . . . . . . . . . . . . . . 22
3.1.5 半導體測試資料軟體架構. . . . . . . . . . . . . . . . . . . 23
3.2 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.1 系統架構圖. . . . . . . . . . . . . . . . . . . . . . . . . . . 26
IV
TABLE OF CONTENTS V
3.3 研究材料. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 半導體測試. . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 半導體測試資料JSON . . . . . . . . . . . . . . . . . . . . . 31
3.4 系統實作. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4 實驗環境與結果35
4.1 實驗環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 實驗方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.1 實驗測試- Throughput . . . . . . . . . . . . . . . . . . . . 37
4.2.2 實驗測試- Update Latency . . . . . . . . . . . . . . . . . . 40
4.2.3 實驗測試- Throughput(不同數量之Shard) . . . . . . . . . 42
4.2.4 實驗測試- STDF 資料新增至MongoDB . . . . . . . . . . . 44
4.3 實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 結論與未來方向45
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 未來方向. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
參考文獻47
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

[1] B.G. Bucur C. Tudorica. A comparison between several nosql databases with comments and notes. In Roedunet International Conference (RoEduNet) 2011 10th. IEEE, 2011.
[2] United Software Associates. Comparative benchmarks: Mongodb vs. couchbase vs. cassandra https://www.mongodb.com/collateral/comparativebenchmarks-mongodb-vs-couchbase-vs-cassandra, March 2015.
[3] Wei Xu, Zhonghua Zhou, Hong Zhou, Wu Zhang, and Jiang Xie. Mongodb improves big data analysis performance on electric health record system. In Life System Modeling and Simulation, pages 350–357, 2014.
[4] Qile Wang, Zhu Shen, Long Ma, and Shi Yin. In W.Eric Wong and Tingshao Zhu, editors, Computer Engineering and Networking, volume 277 of Lecture Notes in Electrical Engineering, pages 403–409. Springer International Publishing, 2014.
[5] Enrico Barbierato, Marco Gribaudo, and Mauro Iacono. Performance evaluation of nosql big-data applications using multi-formalism models. Future Generation Computer Systems, 37:345 – 353, 2014.
[6] Seth Gilbert and Nancy Lynch. Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News, 33(2):51–59, June 2002.
[7] Eric A. Brewer. Towards robust distributed systems (abstract). In Proceedings of the Nineteenth Annual ACM Symposium on Principles of Distributed Computing, PODC ’00, pages 7–, New York, NY, USA, 2000. ACM.
[8] Theo Haerder and Andreas Reuter. Principles of transaction-oriented database recovery. ACM Comput. Surv., 15(4):287–317, December 1983.
[9] Teradyne Inc. Standard test data format specification version 4.0. Teradyne, 2007.
[10] A. Khoche, P. Burlison, J. Rowe, and G. Plowman. A tutorial on stdf fail datalog standard. In Test Conference, 2008. ITC 2008. IEEE International, pages 1–10, Oct 2008.
[11] A. Khoche, J. Katz, S. Landini, Kochen Liao, N. Agrawal, G. Plowman, Song lin Zuo, Liyang Lai, J. Rowe, and T. Zanon. Stdf memory fail datalog standard. In VLSI Test Symposium, 2009. VTS ’09. 27th IEEE, pages 209–214, May 2009.
[12] M. Seuring, M. Braun, A. Ma, G. Eide, K. Yang, and Huaxing Tang. Employing the stdf v4-2007 standard for scan test data logging. Design Test of Computers, IEEE, 29(6):91–99, Dec 2012.
[13] Ming-Ju Wu, J.-S.R. Jang, and Jui-Long Chen. Wafer map failure pattern recognition and similarity ranking for large-scale data sets. Semiconductor Manufacturing, IEEE Transactions on, 28(1):1–12, Feb 2015.
[14] Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. Benchmarking cloud serving systems with ycsb. In Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC ’10, pages 143–154,New York, NY, USA, 2010. ACM.
[15] Swapnil Patil, Milo Polte, Kai Ren, Wittawat Tantisiriroj, Lin Xiao, Julio López, Garth Gibson, Adam Fuchs, and Billie Rinaldi. Ycsb++: Benchmarking and performance debugging advanced features in scalable table stores. In Proceedings of the 2Nd ACM Symposium on Cloud Computing, SOCC ’11,pages 9:1–9:14, New York, NY, USA, 2011. ACM.
[16] V. Abramova, J. Bernardino, and P. Furtado. Testing cloud benchmark scalability with cassandra. In Services (SERVICES), 2014 IEEE World Congress on, pages 434–441, June 2014.
[17] Yahoo cloud serving benchmark(ycsb) http://labs.yahoo.com/news/yahoocloud-serving-benchmark, November 2015.
[18] Ycsb github repository https://github.com/brianfrankcooper/YCSB, November 2015.
[19] Your ultimate guide to the non-relational universe! (nosql)http://nosqldatabase.org, November 2015.
[20] Mongodb https://www.mongodb.org, November 2015.
[21] K. Chodorow and M. Dirolf. Mongodb: The definitive guide, November 2010.
[22] Mongodb https://www.mongodb.org, November 2015.
[23] Sebastian Voss. Docker and mongodb sharded cluster https://sebastianvoss.com/docker-mongodb-sharded-cluster.html, November 2015.
[24] Philipzheng. Docker —— 從入門到實踐https://www.gitbook.com/book/philipzheng/docker_practice/details, November 2015.
[25] introducing json http://json.org, November 2015.
[26] 余至浩. 台積電運用大資料分析創造半導體製程技術優勢http://www.ithome.com.tw/news/92290, September 2014.
[27] 簡禎富. 台積電以大數據提升製造智慧http://www.hbrtaiwan.com/article_content_AR0002794.html, June 2014.
[28] 黃珣. 植基於ycsb 系統之叢集資料庫效能分析. Master’s thesis, 國立中山大學, june 2012.
[29] 周耿達. Mongodb 與mysql cluster 效能分析. Master’s thesis, 國立臺北商業技術學院, june 2014.
[30] 呂欣汶. 雲端醫療紀錄之巨量資料存取與處理平台建置. Master’s thesis, 東海大學, june 2014.
[31] 洪健恆. Linux 網路應用框架之研究：以mongodb 為例之實作. Master’sthesis, 國立中山大學, July 2011.