Impact of Size Effect on Graphene Nanoribbon Transport

Graphene has shown impressive properties for nanoelectronics applications including a high mobility and a width-dependent bandgap. Use of graphene in nanoelectronics would most likey be in the form of graphene nanoribbons (GNRs) where the ribbon width is expected to be less than 20 nm. Many theoretical projections have been made on the impact of edge-scattering on carrier transport in GNRs - most studies point to a degradation of mobility (of GNRs) as well as the on/off ratio (of GNR FETs). This study provides the first clear experimental evidence of the onset of size-effect in patterned GNRs; it is shown that for W<60 nm, carrier mobility in GNRs is limited by edge-scattering.Comment: to be published in IEEE Electron Device Letter

A nonlinear analytical model for tensile failure prediction of pseudo-ductile composite laminates

In this study, the tensile nonlinear responses of composite laminates with [±&theta;n] s and [±&theta;n∕0] s layups are investigated. An analytical model that integrates the progressive failure, shear nonlinearity, fiber rotation, and fragmentation is established to characterize the nonlinear tensile behavior. A nonlinear factor is used to describe the shear nonlinearity of the resin matrix, which is governed by shear stress, while progressive damage indexes are determined by normal stresses. The degree of fiber rotation and the fragmentation between layers are analytically formulated. Tensile results from experiments conducted in this study and from others in the literature are used to verify the model’s prediction accuracy. The proposed model provides acceptably good predictions of nonlinear behavior for pseudo-ductile carbon fiber reinforced composite laminates. A sensitivity analysis shows that the dominant model parameter changes from axial modulus to shear modulus, and eventually to transverse modulus as the off-axial angle increases from 0◦ to 9

SVDiff: Compact Parameter Space for Diffusion Fine-Tuning

Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing methods for customizing these models are limited by handling multiple personalized subjects and the risk of overfitting. Moreover, their large number of parameters is inefficient for model storage. In this paper, we propose a novel approach to address these limitations in existing text-to-image diffusion models for personalization. Our method involves fine-tuning the singular values of the weight matrices, leading to a compact and efficient parameter space that reduces the risk of overfitting and language-drifting. We also propose a Cut-Mix-Unmix data-augmentation technique to enhance the quality of multi-subject image generation and a simple text-based image editing framework. Our proposed SVDiff method has a significantly smaller model size (1.7MB for StableDiffusion) compared to existing methods (vanilla DreamBooth 3.66GB, Custom Diffusion 73MB), making it more practical for real-world applications.Comment: Revised appendix with the addition of cross-attention regularization for single-subject generatio

Resistivity of Graphene Nanoribbon Interconnects

Graphene nanoribbon interconnects are fabricated, and the extracted resistivity is compared to that of Cu. It is found that the average resistivity at a given line-width (18nm<W<52nm) is about 3X that of a Cu wire, whereas the best GNR has a resistivity comparable to that of Cu. The conductivity is found to be limited by impurity scattering as well as LER scattering; as a result, the best reported GNR resistivity is 3X the limit imposed by substrate phonon scattering. This study reveals that even moderate-quality graphene nanowires have the potential to outperform Cu for use as on-chip interconnects.Comment: 10 pages, 3 figures, to be published in IEEE Electron Device Letter

Breakdown Current Density of Graphene Nano Ribbons

Graphene nanoribbons (GNRs) with widths down to 16 nm have been characterized for their current-carrying capacity. It is found that GNRs exhibit an impressive breakdown current density, on the order of 10^8 A/cm2. The breakdown current density is found to have a reciprocal relationship to GNR resistivity and the data fit points to Joule heating as the likely mechanism of breakdown. The superior current-carrying capacity of GNRs will be valuable for their application in on-chip electrical interconnects. The thermal conductivity of sub-20 nm graphene ribbons is found to be more than 1000 W/m-K

MaxViT: Multi-Axis Vision Transformer

Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size has limited their wide adoption in state-of-the-art vision backbones. In this paper we introduce an efficient and scalable attention model we call multi-axis attention, which consists of two aspects: blocked local and dilated global attention. These design choices allow global-local spatial interactions on arbitrary input resolutions with only linear complexity. We also present a new architectural element by effectively blending our proposed attention model with convolutions, and accordingly propose a simple hierarchical vision backbone, dubbed MaxViT, by simply repeating the basic building block over multiple stages. Notably, MaxViT is able to "see" globally throughout the entire network, even in earlier, high-resolution stages. We demonstrate the effectiveness of our model on a broad spectrum of vision tasks. On image classification, MaxViT achieves state-of-the-art performance under various settings: without extra data, MaxViT attains 86.5\% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our model achieves 88.7\% top-1 accuracy. For downstream tasks, MaxViT as a backbone delivers favorable performance on object detection as well as visual aesthetic assessment. We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module. We will make the code and models publicly available

Physical structural and behavioral integration of graphene devices

The strategic importance of microelectronics is reflected in its ubiquity in the global production network and in our daily lives. Above all, the microelectronics revolution has been enabled and driven by the scalability of the silicon transistor and the computational efficiency of its CMOS architecture. While the semiconductor industry has been incredibly adept at pushing the boundaries of scaling in the last few decades, many factors suggest that silicon technology is running into scientific and practical limitations to further scaling. Thus, the push for a beyond-silicon computing platform is imperative. Akin to the transition from bipolar to MOSFET technology or from NMOS to CMOS architecture, the industry is once again looking for the next disruptive technology to continue the exponential growth of computing power. In 2004, two research groups, one from the University of Manchester and the other from Georgia Tech, reported on the electrical properties of ultrathin graphite. Their findings demonstrated the stability of graphene, an atomic layer of graphite, as well as its superb carrier mobility, spurring the semiconductor industry to invest in the material as a candidate for a beyond-silicon computing platform. Within this framework, this thesis explores the promise of graphene as a material and technological platform for electronic devices. The objectives of the thesis are (i) to elucidate opportunities and challenges in the design and fabrication of graphene field-effect devices, and (ii) to advance a new device platform based on graphene p-n junctions.PhDCommittee Chair: Meindl, James; Committee Member: Brand, Oliver; Committee Member: Davis, Jeffrey; Committee Member: Hess, Dennis; Committee Member: Naeemi, Aza

Reference-Voltage-Line-Aided Power Incremental Algorithm for Photovoltaic GMPPT and Partial Shading Detection

Conventional global maximum power point tracking (GMPPT) algorithms show limited tracking speed and accuracy due to the unnecessary peak search considering the complexity of partial shading conditions (PSCs). This paper proposes a novel power incremental (PI) algorithm for photovoltaic GMPPT and partial shading detection with the aid of the reference voltage line (RVL), representing the voltage of possible peaks based on the derived analytical expressions. The proposed analytical solution can detect the occurrence of PSCs easily and never overlook GMPP. Furthermore, both the tracking speed and the efficiency can be improved due to the quick allocation of GMPP and the reduced search area. The proposed RVL-PI-GMPPT algorithm is straightforward without sophisticated searching iterations, which can reduce the computational burden. Combined with the low-cost feature without additional temperature or irradiance sensors, the proposed algorithm is very suitable for the practical engineering design. Both the simulations and experiments under various PSC patterns validated the effectiveness of the proposed algorithm. Conducted fair comparisons with other popular GMPPT methods, the average percentage change of tracking time by implementing the proposed RVL-PI-GMPPT algorithm under three-peaks PSC patterns is 108.9% compared with the PSO method and 209.9% compared with the 0.8Voc model method, respectively

High-Throughput Preparation and Characterization of ZrMoTaW Refractory Multi-Principal Element Alloy Film

In this work, high-throughput screening technology is applied to four-member refractory multi-principal element alloys (RMPEAs) films with high W content. The exploration of refractory metals such as W is strictly limited by the high melting temperature in this work; a multi-gradient deposition method was introduced to overcome this obstacle. By adjusting the power and distance from the target to the sample, component Zr11Mo11Ta25W53 with the best hardening performance was successfully obtained. The uniformity of the material library was analyzed from the perspectives of phase structure and micromorphology. With the help of Hume-Rothery theory and XRD analysis, it is shown that the film has a stable bcc structure. It is believed that film uniformity, nanoscale size, preferential orientation, surface roughness, and solution mechanism are the pivotal factors to improve hardness performance, especially for high W components. The hardness and modulus of elasticity can reach 20 GPa and 300 GPa, respectively, and the H/Er and H3/Er2 values are 0.067 and 0.065, showing the best wear resistance in many samples