基於機器學習在半導體生產測試中提升品質方法

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研究生：	方日縈 Fang, Jih-Ying
論文名稱：	基於機器學習在半導體生產測試中提升品質方法 Machine Learning-Based Quality Enhancement in Semiconductor Production Test
指導教授：	吳誠文 Wu, Cheng-Wen
口試委員:	黃錫瑜 HUANG, SHI-YU 呂學坤 Lu, Shyue-Kung 李昆忠 Lee, Kuen-Jong
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	73
中文關鍵詞：	決策樹、缺陷等級、IC測試、機器學習、生產測試、隨機森林、半導體品質
外文關鍵詞：	Decision Tree, Defect Level, IC Testing, Machine Learning, Production Test, Random Forest, Semiconductor Quality
相關次數：	點閱：3 下載：0
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由於在半導體的製造中，產品的質量需要高度的可靠性，因此會生成更多的測試及檢測以保證準確地辨別晶片的優劣從而提高測試的品質。在這些測試中，我們傾向於發現哪些測試與最終結果之間的關係更有相關性。在這項工作中，我們提出了對於兩種機器學習方法進行修改，以判斷這些測試的重要性與晶片優劣的相關性以及改善測試的品質。
在機器學習中，要如何選取特徵是非常重要的一步，這是因為在大量的特徵數據與測試結果答案的關係不確定性。在大量的測試項目中，很多的測試項目可能是多餘的或是與最後晶片的質量並無太大的關聯性，其中只有少數測試項目足以區分特定產品的晶片質量。在這種情況下，我們傾向於找到與所需晶片分類有關的更重要的測試項目。
在本文中，我們的目標是找到一些可以使用機器學習工具大致預測好晶片和壞晶片之間結果的測試項目，從而減少測試項目的數量。本文實現了決策樹（DT）和隨機森林（RF）等機器學習方法。由於缺乏數據，我們還生成了具有延遲缺陷的數據，以觀察我們的方法捕獲到的重要特徵。

Since the quality of the silicon manufacturing needs high reliability, the test patterns are generated more to guarantee the category that the chips lie in. Among those tests, we tend to find which test patterns have tighter relationships with the final results. In this work, we modify two machine learning methods for identifying the importance of these test patterns with the quality of the chips.
In machine learning, feature selection is quite an important step due to the data set has uncertain relation to the labeled answer. The large amount of test items might be redundant and only few of them are enough to discriminate the quality of the chips for specific product. In this case, we tend to find the more important test items related to the classification for the required chips.
In this thesis, the goal is to find some test items that can roughly predict the results between good or bad chips by using machine learning tools, which can reduce the test items for production. Machine learning approach such as Decision Tree (DT) and Random Forest (RF) are implemented in this thesis. Due to the lack of data, we also generate data with delay defect to observe the important feature captured by this approach.

摘要    I
Abstract    II
List of Contents    III
List of Figures    V
List of Tables    VIII
Chapter 1.    Introduction    1
1.1.    Motivation    1
1.2.    Typical Production Test Flow    5
1.3.    Organization    6
Chapter 2.    Decision Tree (DT) and Random Forest (RF) Classification    8
2.1.    Problems of ML and Additional Techniques    8
2.1.1.    Data Imbalance and Overfitting    8
2.1.2.    Data Measurement Test    9
2.1.3.    Finding Outliers as Failed Chips    11
2.2.    Decision Tree (DT)    15
2.3.    Random Forest (RF)    20
2.4.    Proposed Enhanced DT and RF    23
A.    Limiting Max_depth    24
B.    Class Weight    25
C.    Root Condition    27
Chapter 3.    Generation of Data Used for Machine Learning Approach    31
3.1.    Product Case Studies    31
3.1.1.    ML Methods for Product Case I    32
A.    Decision Tree (DT)    32
B.    Random Forest (RF)    34
C.    Methods for Avoiding Overfitting    35
D.    Finding Important Features    39
3.1.2.    ML Methods for Product Case II    43
A.    Decision Tree (DT)    43
B.    Random Forest (RF)    46
C.    Important Features    47
3.2.    Introduction to Small Delay Defects    48
3.3.    Data Generation Flow    50
Chapter 4.    Experimental Results    52
4.1.    Experimental Case: Carry-Save Multiplier    52
4.2.    Results of Decision Tree (DT)    56
4.3.    Results of Random Forest (RF)    57
4.4.    Influence of Important Feature    58
4.5.    Different Dataset for Product Variation    61
Chapter 5.    Conclusion and Future Work    67
5.1.    Conclusion    67
5.2.    Future Work    68



                                

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