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

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Eurodisplay 2019

The collection includes abstracts of reports selected by the program by the conference committee

Recent Advances in Thin Film Electronic Devices

This reprint is a collection of the papers from the Special Issue “Recent Advances in Thin Film Electronic Devices” in Micromachines. In this reprrint, 1 editorial and 11 original papers about recent advances in the research and development of thin film electronic devices are included. Specifically, three research fields are covered: device fundamentals (5 papers), fabrication processes (5 papers), and testing methods (1 paper). The experimental data, simulation results, and theoretical analysis presented in this reprint should benefit those researchers in flat panel displays, flat panel sensors, energy devices, memories, and so on

High Resolution Organic Vapor Jet Printing of Phosphorescent Organic Light Emitting Diode Arrays.

Organic light emitting diodes (OLEDs) are widely used in mobile devices due to their thin form factor, wide color gamut, and high efficiency. The introduction of OLEDs into televisions and monitors has been slowed, in part, by the difficulty of patterning organic thin films over large areas at micron-scale resolutions. A practical patterning technology must also be compatible with efficient device architectures, such as phosphorescent OLEDs (PHOLEDs). Organic vapor jet printing (OVJP) is an approach for depositing and patterning the emissive layers of OLED displays in a scalable manner. An inert carrier gas is used to mix organic vapor from multiple material sources. The vapor mixture is then distributed to a Si micronozzle array that collimates it into multiple jets to deposit well-defined thin film features onto a chilled substrate. This technique is capable of printing features smaller than 20 μm and arrays of 100 μm wide multicolor PHOLED segments. An experimentally validated deposition model predicts that full color (red-green-blue) pixel pitches of 150 μm are obtainable without cross-contamination of dopants between adjacent sub pixels. Green PHOLEDs with an external quantum efficiency of 8.0±0.7%, comparable to that achieved with standard techniques, were fabricated with OVJP. Since the micronozzle array is fabricated using standard Si processing techniques, this approach is readily scalable. Based on the performance of the laboratory system, a production OVJP tool has the potential to print a full color OLED emissive layer onto Gen 8 (4 m2) substrate in as little as 250s.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97921/1/gmcgraw_1.pd

Why did video screens get slimmer? A study of the role of Intellectual Property in the commercial development of organic light-emitting diodes

PhDThis research project consists of a critical analysis of the role of intellectual property amongst other factors in the successful commercial development at the Cavendish Laboratory of optoelectronic light emitting diode display devices based on novel organic semiconductor materials. It begins by giving the background to the quantum mechanical properties upon which the technology is based, followed by a discussion of the path of innovation, describing the interaction between the different socioeconomic factors that influence this path. It then draws an analogy with the development of an analogous technology - inorganic semiconductors - to signpost the factors that may affect the developmental history of the technology. This is followed by an analysis of a chronology derived initially from patents downloaded from the World Patents Database of the European Patent Office to showcase the technology’s development steps, and to study the patenting strategy of Cambridge Display Technology (CDT) - the company that was set up to commercialise the novel technology - through a patent trends analysis. From that, the major socioeconomic factors critical to the technology’s development are analysed, followed by a test and extension of an existing Black Box mathematical model for studying the dynamics of innovation that is based on the interaction of those factors. Finally, through a patent citation analysis, CDT’s commercial strategy for the technology is shown as being based on its patents portfolio to build an extensive licensing programme that pooled major academic, industry and commercial partners for the furtherance of the technology. This later evolved into a new ecosystem for the innovation, of which CDT occupied a central and indispensable position

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

A Contribution Towards Intelligent Autonomous Sensors Based on Perovskite Solar Cells and Ta2O5/ZnO Thin Film Transistors

Many broad applications in the field of robotics, brain-machine interfaces, cognitive computing, image and speech processing and wearables require edge devices with very constrained power and hardware requirements that are challenging to realize. This is because these applications require sub-conscious awareness and require to be always “on”, especially when integrated with a sensor node that detects an event in the environment. Present day edge intelligent devices are typically based on hybrid CMOS-memristor arrays that have been so far designed for fast switching, typically in the range of nanoseconds, low energy consumption (typically in nano-Joules), high density and endurance (exceeding 1015 cycles). On the other hand, sensory-processing systems that have the same time constants and dynamics as their input signals, are best placed to learn or extract information from them. To meet this requirement, many applications are implemented using external “delay” in the memristor, in a process which enables each synapse to be modeled as a combination of a temporal delay and a spatial weight parameter. This thesis demonstrates a synaptic thin film transistor capable of inherent logic functions as well as compute-in-memory on similar time scales as biological events. Even beyond a conventional crossbar array architecture, we have relied on new concepts in reservoir computing to demonstrate a delay system reservoir with the highest learning efficiency of 95% reported to date, in comparison to equivalent two terminal memristors, using a single device for the task of image processing. The crux of our findings relied on enhancing our capability to model the unique physics of the device, in the scope of the current thesis, that is not amenable to conventional TCAD simulations. The model provides new insight into the redox characteristics of the gate current and paves way for assessment of device performance in compute-in-memory applications. The diffusion-based mechanism of the device, effectively enables time constants that have potential in applications such as gesture recognition and detection of cardiac arrythmia. The thesis also reports a new orientation of a solution processed perovskite solar cell with an efficiency of 14.9% that is easily integrable into an intelligent sensor node. We examine the influence of the growth orientation on film morphology and solar cell efficiency. Collectively, our work aids the development of more energy-efficient, powerful edge-computing sensor systems for upcoming applications of the IOT

Factories of the Future

Engineering; Industrial engineering; Production engineerin