Physical Sciences and Mathematics Commons^™

Suppressing Bias Stress Degradation In High Performance Solution Processed Organic Transistors Operating In Air, Hamna F. Iqbal, Qianxiang Ai, Karl J. Thorley, Hu Chen, Iain Mcculloch, Chad Risko, John E. Anthony, Oana D. Jurchescu

Chemistry Faculty Publications

Solution processed organic field effect transistors can become ubiquitous in flexible optoelectronics. While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications. Here, we introduce a strategy to identify the most probable and severe degradation pathways in organic transistors and then implement a method to eliminate the main sources of instabilities. Real time monitoring of the energetic distribution and transformation of electronic trap states during device operation, in conjunction with simulations, revealed the nature of traps responsible for performance degradation. With this information, we …

Optical Metasurfaces, Fatih Balli

Theses and Dissertations--Physics and Astronomy

Traditional optical elements, such as refractive lenses, mirrors, phase plates and polarizers have been used for various purposes such as imaging systems, lithographic printing, astronomical observations and display technology. Despite their long-term achievements, they can be bulky and not suitable for miniaturization. On the other hand, recent nanotechnology advances allowed us to manufacture micro and nanoscale devices with ultra-compact sizes. Metasurfaces, 2D engineered artificial interfaces, have emerged as candidates to replace traditional refractive lenses with ultra-thin miniaturized optical elements. They possess sub-wavelength unit cell structures with a specific geometry and material selection. Each unit cell can uniquely tailor the phase, …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …