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研究生:林國裕
研究生(外文):Guo-Yu Lin
論文名稱:減少測試時間與增加測試質量之適性測試技術
論文名稱(外文):Test Time Reduction and Test Quality Improvement with Adaptive Testing Techniques
指導教授:黃俊郎黃俊郎引用關係
指導教授(外文):Jiun-Lang Huang
口試委員:黃錫瑜呂學坤李進福
口試委員(外文):Shi-Yu HuangShyue-Kung LuJin-Fu Li
口試日期:2015-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電子工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:27
中文關鍵詞:適性測試減少測試成本測試質量提升
外文關鍵詞:Adaptive TestingReducing Test CostTest Quality Improvement
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由於晶片面積日趨縮小,單晶片上的晶體數量日益增大,故測試成本也隨之急遽攀升,在可控的測試成本內提高測試質量也變成一種挑戰,因此適性測試成為一個很好的方法可以有效解決上述問題,本文透過適性測試技巧以”減少測試成本”與“增加測試質量”兩個面向去做研究與討論。在減少測試成本中,其中一種著名的適性測試方式為透過改動測試圖樣的排序,將測得較多故障的測試圖樣移至圖樣序列前端,優先去進行測試,然而這種作法會使許多潛在的高偵測力圖樣沒有機會上場進行測試,本文的研究,提出一個以圖樣上場次數多寡來排序的方式,進而找到潛在高偵測力的圖樣去減少測試成本,確保每個圖樣都上場過一定次數後,再依據每個圖樣的偵測錯誤率來排序,由我們的實驗結果顯示,相較於原始圖樣不做任何變動,我們的方法可減少 52%的測試成本,相較於單靠偵測到的錯誤率去排序,平均可再減少 12%的測試成本;至於測試質量提升的部分,我們提出一種適性測試技巧,儘管量產測試過程中缺陷種類不斷的發生變化,我們的方法仍能降低 DPPM 以及將測試圖樣數量控制在可接受的範圍內,我們的想法基本上是透過監測辨別出系統性缺陷種類的分布及變化,為其產生相對應適合的測試圖樣,並且確保在整體測試過程中,整體的測試圖樣序列其隨機缺陷的偵測覆蓋率要保持在一定的水準上,在此基礎上去找出更多系統性缺陷的種類,由模擬結果可看出,我們的方法可有效地辨別出系統性缺陷的種類以及找出高質量的適性測試圖樣去降低整體的測試成本。

Due to the continuous shrinking of the device feature sizes and the growing number of transistors on a single chip, test cost is increasing dramatically. As a result, improving test quality with reasonable test cost becomes a challenging task. One popular adaptive test approach for reducing test cost is to reorder the test patterns according to their fault detection performance — by applying the more effective patterns first, the total test time can be significantly reduced. While very effective, the detection performance based approach fails to identify some high-quality test patterns and leaves them unused throughout the test application process. In our work, we propose a test-application-count based learning technique to help identify high-quality test patterns. By ensuring that all patterns are applied for at least the specified number of times, the proposed technique finds more high-quality test patterns and moves them to the front of the test pattern list. Experimental results show that the proposed test-application-count based learning technique achieves 52% test time reduction (TTR) in average — a 12% improvement compared to the detection performance based approach. About test quality improvement, we propose an adaptive test technique that, in the existence of varying defect characteristics, helps keeping the DPPM and test pattern count within the acceptable range. The idea is to monitor the detected fault characteristics and identify the types of occurring systematic faults. Then, the proposed technique adjusts the test pattern set accordingly to ensure sufficient fault coverage on the identified systematic defect types as well as the random defects. Simulation results show that the proposed technique successfully identifies the shift of systematic defect types and produces high-quality test set to reduce the overall test cost.

口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Work 2
1.3 Contribution 4
Chapter 2 Test-Application-Count Based Learning Technique for Test Time Reduction 6
2.1 Observations 6
2.2 The Proposed Napp-Based Learning Technique 9
2.3 Determining tapp 10
2.4 Napp-Based Learning vs. Continue-on-Fail 10
2.5 Experiment Results 11
Chapter 3 Defect-Tracking Adaptive Testing 15
3.1 Prerequisites 16
3.2 Systematic Defect Type Identification 16
3.3 Test Pattern Modification 17
3.4 DPPM Estimation 18
3.5 Simulation Results 18
3.5.1 Defect Type Classification 20
3.5.2 Defect Tracking & Pattern Modification Results 21
Chapter 4 Conclusions 25
REFERENCE 26


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