유기발광 디스플레이 수명 모델 제안 및 모델 검증 체계 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 윤병동.Despite the advantages of organic light-emitting diode (OLED) displays over liquid crystal displays, OLED displays suffer from reliability concerns related to luminance degradation and color shift. In particular, existing testing schemes are unable to reliably estimate the lifetime of large OLED displays (i.e., displays of 55 inches or larger). The limited number of test samples and the immature technology result in great hurdles for timely product development. This study proposes a statistical approach to develop a lifetime model for OLED panels. The proposed approach incorporates manufacturing and operational uncertainties, and accurately estimates the lifetime of the OLED panels under normal usage conditions. The proposed statistical analysis approach consists of: (1) design of accelerated degradation tests (ADTs) for OLED panels, (2) establishment of a systematic scheme to build bivariate lifetime models for OLED panels, (3) development of two bivariate lifetime models for OLED panels, and (4) statistical model validation for the heat dissipation analysis model for OLED TV design. This four-step statistical approach will help enable accurate lifetime prediction for large OLED panels subjected to various uncertainties. Thereby, this approach will foster efficient and effective OLED TV design to meet desired lifespan requirements. Furthermore, two bivariate acceleration models are proposed in this research to estimate the lifetime of OLED panels under real-world usage conditions, subject to manufacturing and operational uncertainties. These bivariate acceleration models take into account two main factors—temperature and initial luminance intensity. The first bivariate acceleration model estimates the luminance degradation of the OLED panelthe second estimates the panels color shift. The lifespan predicted by the proposed lifetime model shows a good agreement with experimental results. Ensuring the color shift lifetime is a great hurdle for OLED product development. However, at present, there is no effective way to estimate the color shift lifetime at the early stages of product development while the product design is still changing. The research described here proposes a novel scheme for color shift lifetime analysis. The proposed method consists of: (1) a finite element model for OLED thermal analysis that incorporates the uncertainty of the measured surface temperature, (2) statistical model validation, including model calibration, to verify agreement between the predicted results and a set of experimental data (achieved through adjustment of a set of physical input variables and hypothesis tests for validity checking to measure the degree of mismatch between the predicted and observed results), and (3) a regression model that can predict the color shift lifetime using the surface temperature at the early stages of product development. It is expected that the regression model can substantially shorten the product development time by predicting the color shift lifetime through OLED thermal analysis.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Overview and Significance 2 1.3 Thesis Layout 6 Chapter 2. Literature Review 8 2.1 Accelerated Testing 8 2.2 Luminance Degradation Model for OLEDs 12 2.3 Color Shift of OLEDs 14 2.4 Verification and Validation Methodology 16 Chapter 3. OLED Degradation 28 3.1 Chromaticity and the Definition of Color Shift Lifetime 30 3.2 Degradation Mechanism 31 3.2.1 Luminance Degradation Mechanism 33 3.2.2 Color Shift Mechanism 34 3.3 Performance Degradation Models 36 3.3.1 Performance Degradation Model 36 3.3.2 Performance Color Shift Model 38 3.4 Acceleration Model 38 Chapter 4. Acceleration Degradation Testing (ADT) for OLEDs 42 4.1 Experimental Setup 42 4.2 Definition of the Time to Failure 46 4.2.1 The Time to Failure of Luminance 46 4.2.2 The Time to Failure of Color Shift 47 4.3 Lifespan Test Results 50 Chapter 5. Bivariate Lifetime Model for OLEDs 53 5.1 Fitting TTF Data to the Statistical Distribution 53 5.1.1 Estimation of Lifetime Distribution Parameters 53 5.1.2 Estimation of the Common Shape Parameter 58 5.1.3 Likelihood-Ratio Analysis 62 5.2 Bivariate Lifetime Model 64 5.2.1 Luminance Lifetime Model 64 5.2.2 Color Shift Lifetime Model 66 5.3 Validation of the Lifetime Model 67 Chapter 6. Statistical Model Validation of Heat Dissipation Analysis Model 77 6.1 Estimation Method for TTF using Surface Temperature 79 6.2 Thermal Analysis Model for OLED Displays 81 6.3 Statistical Calibration using the EDR Method 82 6.4 Validity Check 87 6.5 Results and Discussion 90 Chapter 7. Case Study 93 7.1 Computational Modeling 93 7.2 Estimation of Color Shift 95 7.3 Estimation of Luminance Degradation 96 Chapter 8. Contributions and Future Work 98 8.1 Contributions and Impacts 98 8.2 Suggestions for Future Research 103 References 104Docto

Modeling of OLED degradation for prediction and compensation of AMOLED aging artifacts

Degradation is still the most challenging issue for OLED, which causes the image-sticking artifact on AMOLED displays and limits their lifetime. To overcome the demerit, OLED degradation is modeled in this thesis, and compensation based on the models is applied for AMOLEDs. A data-counting model is firstly developed to quantitatively evaluate the degradation on OLEDs, with consideration of the accumulation stress during operation. An electro-optical model is further built, based on an equivalent circuit. It can simulate the electro-optical characteristic (I-V, Eff-V) and the degradation behaviors in aging process. Besides, the correlation model is aimed to derive the current efficiency decay with measurable electrical values, delivering more dependable results at strongly aged state. The prediction and compensation are implemented based on developed models. The results show that the models exactly predict the efficiency decay during operation. The image-sticking aging artifact on AMOLED can be suppressed by applying compensation, so that the display lifetime is extended.Durch das Einbrennen von Bildern in AMOLED Displays wird deren Lebensdauer verringert; dieser Qualitätsverlust stellt nach wie vor die größte Herausforderung für die OLED Technologie dar. In dieser Thesis wird die Degradation der OLEDs modelliert und eine Kompensierung anhand der Modelle erreicht. Zunächst wurde ein Data-counting Modell entwickelt, um die Degradation von OLEDs unter Berücksichtigung der akkumulierten Belastung während des Betriebs quantitativ zu bewerten. Des Weiteren wurde ein elektro-optisches Modell entwickelt, das auf einem äquivalenten Schaltungsmodell basiert. Es kann die elektro-optischen Eigenschaft (I-V, Eff-V) und das Degradationsverhalten im Alterungsprozess simulieren. Außer den beiden Modellen wird noch ein Korrelationsmodell entwickelt, das darauf abzielt, die Abnahme der Stromeffizienz aus den messbaren elektrischen Werten abzuleiten. Dieses Modell liefert im stark gealterten Zustand zuverlässigere Ergebnisse. Aufbauend auf die entwickelten Modelle wurden die Vorhersage und die Kompensierung implementiert. Die Ergebnisse zeigen, dass die Modelle den Effizienzverlust während des Betriebes genau vorhersagen. Das Einbrennen des Bildes in das AMOLED-Display kann durch das Anwenden der Kompensierung unterdrückt werden, so dass die Lebensdauer des Displays verlängert wird

A framework for offloading decision making to conserve battery life on mobile devices

Abstract: The increased use of mobile devices has led to the creation of complex mobile applications that require more resources than are readily available on mobile devices. As resources such as processing power and storage are found on the cloud, resources of mobile devices can be increased by using cloud-based mobile augmentation. However, some resources, specifically battery life, and bandwidth cannot be augmented. To augment mobile device resources such as battery life, offloading can be used. This research discusses offloading methods and examines the approaches used in related research. It is found that most of the energy consumed when offloading is due to network communication, as opposed to computation when executing locally. When offloading to the cloud consumes less energy than local execution, the battery life of a mobile device can be conserved. Choosing between offloading and local execution is called an offloading decision. To make offloading decisions that conserve battery life, the decision-making process is explored. A challenge identified when making offloading decisions is accurately estimating the energy consumption of tasks when offloading and when executing locally. As the energy consumption profile of each device differs according to the capabilities of the device, this aspect is explored. The research conducted in this dissertation proposes the Switch framework. The Switch framework conserves the limited battery life on mobile devices by estimating the consumption of energy of a task and choosing the least expensive option. A software-based device-specific energy consumption profile is created for this purpose. Switch is evaluated using the Switch prototype, which has been designed according to the specifications of the framework. The prototype is evaluated by comparing the estimated energy consumption against the measured energy consumption. The evaluation of the framework suggests that Switch can successfully be used to conserve battery life on mobile devices by making intelligent offloading decisions.M.Sc. (Information Technology