Advances in semiconductor technology and design automation methods have introduced a new era for electronic products. With design sizes in millions of logic gates and operating frequencies in GHz, defects-per-million rates continue to increase, and defects are manifesting themselves in subtle ways. / Diagnosing functional failures in complicated electronic boards is a challenging task, wherein debug technicians try to identify defective components by analyzing some syndromes obtained from the application of diagnostic tests. The diagnosis effectiveness and efficiency rely heavily on the quality of the in-house developed diagnostic tests and the debug technicians’ knowledge and experience, which, however, have no guarantees nowadays. To tackle this problem, this thesis proposes a novel agent-assisted diagnostic framework for board-level functional failures, namely AgentDiag, which facilitates to evaluate the quality of the diagnostic tests and bridge the knowledge gap between the diagnostic programmers who write diagnostic tests and the debug technicians who conduct in-field diagnosis with a lightweight model of the boards and tests. / Machine learning algorithms have been advocated for automated diagnosis of board-level functional failures due to the extreme complexity of the problem. Such reasoning-based solutions, however, remain ineffective at the early stage of the product cycle, simply because there are insufficient historical data for training the diagnostic system that has a large number of test syndromes. Guided by a proposed metric isolation capability, AgentDiag is able to leverage the knowledge from the lightweight model to selecting a reduced test syndrome set for diagnosis in an adaptive manner. / While AgentDiag is effective to improve the diagnostic accuracy, this technique, by excluding some test syndromes, may cause information loss for diagnosis. The thesis further presents a novel test syndrome merging methodology to address this problem. That is, by leveraging the domain knowledge of the diagnostic tests and the board structural information, we adaptively reduce the feature size of the diagnostic system by selectively merging test syndromes such that it can effectively utilize the available training cases. / Experimental results on real industrial boards and an OpenRISC design demonstrate the effectiveness of the proposed solutions. / 半導體技術和設計自動化的高速發展開啟了電子產品的新紀元。百萬級別的設計尺寸和上G赫茲的操作頻率使得每百萬次採樣數的缺陷率繼續上升，缺陷顯現方式也日益微妙。 / 複雜電子板的診斷是一項極具挑戰的工作。調試人員通常通過分析診斷測試所產生的症狀，甄別有缺陷的元件。診斷的有效性和效率就極大地依賴於診斷測試的質量和調試人員的知識經驗，但是現在這些都是沒有確定性的。為了解決這一問題，本文提出一個新穎的針對板級功能性故障的代理輔助診斷系統AgentDiag。它幫助評估診斷測試的質量，並架起編寫診斷測試的測試程式員和從事實際診斷工作的調試人員之間的橋樑。 / 因為板級診斷的極度複雜，機器學習算法已經被提出來解決這一問題。但是這些基於推導的方法在早期很難達到好的效果，原因是過大的測試數量和相對較少的訓練數據。在度量Isolation Capability的引導下，能夠適應性地利用來自輕量級模型的知識去選取一個症狀集進行診斷。 / AgentDiag可以有效地提高診斷準確率，但是由於是直接剔除一部分測試症狀，所以有可能造成信息的丟失。本文進一步提出了一個測試症狀合併的方法來解決這一問題。那就是利用診斷測試和電路板的結構描述，我們可以適應性地利用選擇性合併的測試症狀來減少測試症狀的數目，從而有效地利用已有的測試數據。 / 來自實際的工業電路板和OpenRisc設計的實驗數據驗證了提出的方法的有效性。 / Sun, Zelong. / Thesis M.Phil. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 47-51). / Abstracts also in Chinese. / Title from PDF title page (viewed on 12, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_1291511 |
| Date | January 2014 |
| Contributors | Sun, Zelong (author.), Xu, Qiang (thesis advisor.), Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. (degree granting institution.) |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography, text |
| Format | electronic resource, electronic resource, remote, 1 online resource (ix, 51 leaves) : illustrations (some color), computer, online resource |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-NoDerivatives 4.0 International" License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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