科技的快速成長帶來很多的好處，例如電路面積的縮小、頻率的提升、以及功耗的降低等等，然而，這些改變也使得電路的可靠度越來越有挑戰性。除此之外，在一些特別注重安全的應用上常常需要更高的可靠度與使用壽命，譬如車用電子、生醫電子和航太設備常常需要十年以上的使用壽命。因此除了出廠前的檢測外，如何確保出廠後的晶片於生命週期中使用的可靠度也是一項十分重要的議題。而一個晶片的可靠度會受到製程變異(Process variation)、電壓(Voltage)、溫度(Temperature)、以及老化(Aging)所影響，而這四個因素合稱為『匹夫塔效應』(PVTA Variation)，這些因素除了會受到操作的環境的影響外，還會隨著時間而改變。在這篇論文中，我們以先前實驗室內開發的線上監測晶片內部的製成變異、電壓、和溫度之技術為基礎，進一步將老化效應納入考量，提出一個預估晶片剩餘壽命的方法，並且將軟硬體整合成一套物聯網晶片內之匹夫塔效應之雲端監測系統，其利用了物聯網設備原有的網路通訊能力。透過這樣遠距離的監控技術，一個監控系統內所照顧的物聯網設備晶片，無論在天涯海角的哪一方，雲端的控制中心都能隨時了解它的匹夫塔效應情形，以此來衡量晶片系統的健康狀況。透過這些資訊，可以早期偵測出晶片系統中匹夫塔效應的異常情形，以便在晶片功能壞掉之前就進行更換或是線上修補，進一步確保晶片系統的可靠性。

Reliability of an IC, concerning if an IC can function reliably over its designated lifetime in the field, has become more and more important in today’s safety-critical applications. It is known that reliability can be affected by PVTA effects, (Process, Voltage, Temperature, and Aging). These effects not only depend on the physical locations where an IC is operated, but also vary over time. In this work, we present a cloud-based PVTA monitoring system for the Internet of Things (IoT) devices, by taking advantage of its inherent internet connectivity. By doing so, one can know of the PVTA status of any IoT device remotely and continually at any time and any place. With the obtained information, a potential PVTA-induced failure can be alarmed in advance before it actually strikes, and thereby pre-cautious actions (such as adaptive measures, online repair, or even manual replacement) can be taken in advance to avoid unnecessary system down time.

Abstract i
摘要 ii
誌謝 iii
Content iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Brief Introduction of Proposed System 4
1.3 Thesis Organization 5
Chapter 2 Preliminaries 6
2.1 Ring-Oscillator Based Monitor 6
2.2 Design-for-Monitoring for an IC 8
2.3 ROCP Modeling 9
Chapter 3 Proposed System 11
3.1 Test Chip Design 11
3.2 Cloud-Based Monitoring 12
3.3 System Architecture 14
3.4 Data Transmission 16
3.4.1 Transmission of user commands 17
3.4.2 Transmission of ROCP raw data and PVTA information 18
3.5 Aging Prediction 22
Chapter 4 Experimental Results 29
4.1 Hardware Components 29
4.2 Software Components 30
4.3 Process variation 32
4.2 VDD-drop 33
4.3 Temperature 34
4.3 Aging and Remaining Lifetime Prediction 36
Chapter 5 Conclusion 37
References 38

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