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

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

PROGNOSTICS-BASED QUALIFICATION OF WHITE LIGHT-EMITTING DIODES (LEDS)

Light-emitting diode (LED) applications have expanded from display backlighting in computers and smart phones to more demanding applications including automotive headlights and street lightening. With these new applications, LED manufacturers must ensure that their products meet the performance requirements expected by end users, which in many cases require lifetimes of 10 years or more. The qualification tests traditionally conducted to assess such lifetimes are often as long as 6,000 hours, yet even this length of time does not guarantee that the lifetime requirements will be met. This research aims to reduce the qualification time by employing anomaly detection and prognostic methods utilizing optical, electrical, and thermal parameters of LEDs. The outcome of this research will be an in-situ monitoring approach that enables parameter sensing, data acquisition, and signal processing to identify the potential failure modes such as electrical, thermal, and optical degradation during the qualification test. To detect anomalies, a similarity-based-metric test has been developed to identify anomalies without utilizing historical libraries of healthy and unhealthy data. This similarity-based-metric test extracts features from the spectral power distributions using peak analysis, reduces the dimensionality of the features by using principal component analysis, and partitions the data set of principal components into groups using a KNN-kernel density-based clustering technique. A detection algorithm then evaluates the distances from the centroid of each cluster to each test point and detects anomalies when the distance is greater than the threshold. From this analysis, dominant degradation processes associated with the LED die and phosphors in the LED package can be identified. When implemented, the results of this research will enable a short qualification time. Prognostics of LEDs are developed with spectral power distribution (SPD) prediction for color failure. SPD is deconvoluted with die SPD and phosphor SPD with asymmetric double sigmoidal functions. Future SPD is predicted by using the particle filter algorithm to estimate the propagating parameters of the asymmetric double sigmoidal functions. Diagnostics is enabled by SPD prediction to indicate die degradation, phosphor degradation, or package degradation based on the nature of degradation shape of SPD. SPDs are converted to light output and 1976 CIE color coordinates using colorimetric conversion with color matching functions. Remaining useful life (RUL) is predicted using 7-step SDCM (standard deviation of color matching) threshold (i.e., 0.007 color distance in the CIE 1676 chromaticity coordinates). To conduct prognostics utilizing historical libraries of healthy and unhealthy data from other devices, this research employs similarity-based statistical measures for a prognostics-based qualification method using optical, electrical, and thermal covariates as health indices. Prognostics is conducted using the similarity-based statistical measure with relevance vector machine regression to capture degradation trends. Historical training data is used to extract features and define failure thresholds. Based on the relevance vector machine regression results, which construct the background health knowledge from historical training units, the similarity weight is used to measure the similarity between each training unit and test unit under the test. The weighted sum is then used to estimate the remaining useful life of the test unit

Biological Mechanisms Linking Stress and Anhedonia

Evidence from research across species suggests that stress exposure is linked with anhedonia (loss of pleasure and/or decreased motivation). However, the mechanisms through which stress might impact anhedonia remain unclear. Chapters 1 and 2 of this dissertation review putative etiological pathways from stress to anhedonia and discuss stressor characteristics that could inform experimental models of stress-induced anhedonia. Chapter 3 describes an attempt to identify which types of stress are most associated with anhedonia using stress interview data from multiple datasets. Unexpectedly, we found no credible effects on anhedonic symptoms for stressor chronicity, severity, dependence on behavior, or interpersonal focus. Instead, number of stressors endorsed was the best predictor of anhedonic symptoms. Next, Chapters 4 and 5 report on two studies that tested possible biological mediators of the stress-anhedonia link. Chapter 4 describes an analysis of the UK Biobank dataset aimed at evaluating frontostriatal functional connectivity as a mechanism of stress-induced anhedonia. Although stress exposure predicted anhedonia, analyses uncovered no stable relation between frontostriatal connectivity and anhedonia, and no support for the proposed mediation model. Chapter 5 details a study that implemented a laboratory-based stressor to assess its potential impact on motivated behavior (thought to be a key component of anhedonia), and whether any such effects might be mediated by inflammatory responding. Low concentrations of salivary cytokines suggested questionable validity of inflammatory assessment, and no effect of stress on inflammatory responding was observed. Additionally, stress produced no measurable changes in motivated behavior. Thus, analyses revealed no evidence consistent with inflammation as a mechanism of stress-induced anhedonia. Finally, Chapter 6 discusses conclusions and implications of the current findings, and provides ideas for future directions

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Thermal management and humidity based prognostics of high-power LED packages

While Light Emitting Diodes (LEDs) hold much potential as the future of lighting, the high junction temperatures generated during usage result in higher than expected degradation rates and premature failures ahead of the expected lifetime. This problem is especially under-addressed under conditions of high humidity, where there has been limited studies and standards to manage humidity based usage. This research provides an analysis of the factors that contribute to high junction temperatures and suggests prognostic techniques to aid in LED thermal management, specifically under humidity stress. First, this research investigates the effects of current, temperature and humidity on the electrical-optical-thermal (EOT) properties. Temperature rises within an LED because of input stressors which cause heat to build up: the input current, the operating and ambient temperature, and the relative humidity of the environment. Not only is there an accumulation of heat due to these factors that alter the thermal properties, but the electrical and optical characteristics are changed as well. By uncovering specific configurations causing the EOT performance to degrade under stress, better thermal management techniques can be employed. Second, this research proceeds to quantitatively link the EOT performance degradation to the humidity causal factor. The recent proliferation of LED usage in regions with high humidity has not corresponded with sufficient studies and standards governing LED test and usage under the humidity stressor. This has led to indeterminate use and consequentially, a lack of understanding of humidity based failures. A novel humidity based degradation model (HBDM) is successfully developed to gauge the impact of the humidity stressor by means of an index which is shown to be an effective predictor of colour degradation. This prognostication of the colour shift by the HBDM provides both academia and industry not only with an indicator of the physical degradation but also an assessment of the LED yellow-blue colour rendering stability, a critical application criterion. Using the HBDM parameters as indicators of the state of the LED, the degradation study is expanded in the development of a Distance Measure approach to isolate degraded samples exceeding a specified multivariate boundary. The HBDM and Distance Measure approach serve as powerful prognostic techniques in overall LED thermal management