Applications of Spectrally-Resolved Photoluminescence in Silicon Photovoltaics

In broad terms, this thesis is devoted to measuring and interpreting the photoluminescence spectra emitted from different structures in crystalline silicon wafers and solar cells. Based on the knowledge accumulated, it also establishes a variety of applications of photoluminescence spectroscopy in silicon photovoltaics. The thesis may be divided into 3 main categories: band-to-band luminescence from wafers, deep-level luminescence from defects and impurities, and composite luminescence from different structures and layers in solar cells. First, this thesis utilizes band-to-band photoluminescence spectra emitted from planar silicon wafers to determine the values of the band-to-band absorption coefficient and the radiative recombination coefficient as a function of temperature with high precision. Parameterizations of these two coefficients are established to allow convenient calculations. Based on the newly established temperature data, the impacts of surface geometries and excess carrier profiles on luminescence spectra emitted from various silicon wafers are investigated via both modeling and experiments as a function of temperature. The results suggest that, the accuracy of many photoluminescence-based techniques, established mainly at room temperature in the literature, can be further improved by performing the measurements at higher temperatures due to the increasing impacts of surface reflectivities and excess carrier profiles on luminescence spectra with rising temperatures. These applications highlight the significance of the established data of the two coefficients for spectral fitting techniques. Next, the thesis investigates the deep-level luminescence from defects and impurities distributed around sub-grain boundaries in multicrystalline silicon wafers. The thesis shows that, the dislocations at sub-grain boundaries and the defects and impurities trapped around the dislocations emit separate luminescence peaks at low temperatures. The luminescence intensity of the trapped defects and impurities is found to be altered significantly after phosphorus gettering, whereas the dislocation luminescence is not changed throughout different solar cell processing steps. Also, the trapped defects and impurities are found to be preferentially distributed on one side of the sub-grain boundaries due to the asymmetric distribution of their luminescence intensity across the sub-grain boundaries. In addition, the thesis also demonstrates that the damage induced by laser doping is related to dislocations, since its deep-level luminescence spectrum has similar properties to those emitted from dislocations in multicrystalline silicon wafers. The interface between the laser-doped and un-doped regions is found to contain more damage than the laser-doped regions. Furthermore, the thesis reports a new photoluminescence-based method to separate the luminescence signatures from different layers and structures in a single silicon substrate, courtesy of the well-resolved luminescence peaks at low temperatures from different layers. In particular, the technique is applied to characterize the doping level of both locally-diffused and laser-doped regions on various silicon solar cells and cell precursors, utilizing band-gap narrowing effects in heavily-doped silicon. The results show that, the interface between the laser-doped and un-doped regions is much more heavily-doped that the doped regions. In addition, the technique is also applied to evaluate and the parasitic absorption of different surface passivation films on finished solar cells, due to the correlation between the sub band-gap luminescence intensity from these passivation films and the optical absorption in the films. The technique is contactless and nondestructive, requires minimal sample preparation, and provides micron-scale spatial resolutions. Finally, the thesis combines the advantages of spectrally-resolved photoluminescence (PLS) and photoluminescence excitation spectroscopy (PLE) to develop a PLS-PLE-combined technique for characterizing wafers and solar cells. In particular, the entire photoluminescence spectrum from a silicon wafer or solar cell is captured and monitored while the excitation energy is varied. This technique allows us to quantitatively evaluate both the doping level and the junction depth of various diffused silicon wafers, the defects induced by the post-diffusion thermal treatment at different depths below the wafer surface, and the enhanced diffusion at grain boundaries and sub-grain boundaries in multicrystalline silicon wafers. The results show that, the enhanced diffusion happens at both grain boundaries and sub-grain boundaries

VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification

The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulnerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.Comment: Accepted by IEEE KSE 202

Active disturbance rejection control-based anti-coupling method for conical magnetic bearings

Conical-shape magnetic bearings are currently a potential candidate for various magnetic force-supported applications due to their unique geometric nature reducing the number of required active magnets. However, the bearing structure places control-engineering related problems in view of underactuated and coupling phenomena. The paper proposes an Adaptive Disturbance Rejection Control (ADRC) for solving the above-mentioned problem in the conical magnetic bearing. At first, virtual current controls are identified to decouple the electrical sub-system, then the active disturbance rejection control is employed to eliminate coupling effects owing to rotational motions. Comprehensive simulations are provided to illustrate the control ability

Grundlegende betrachtungen zur wirkung eines "inversen" spanungsverhältnisses als basis für die fräswerk-zeugkonstruktion

The demand for higher productivity and quality, flexibility as well as process safety are marking the development in the field of metal-cutting manufacturing process. Thereby the field of low vibration milling plays a special role. Therefore the development and design of modern milling tools is more and more often affected by novel machining strategies. The article deals with the development and design of a low vibration milling tool including the reversal of conventional chip- cross- section b/h > 1 to the "invers" ratio b/h < 1. For this the difference between the two cross sections will be analysed. The focus of the first experimental research is the determination of the effects of reversing the chip- cross- section on the cutting forces as well as chip formation and - forming. The influence of the tool side rake angle (γf) in milling with "inverse" chip- cross- section will be studied. The results gathered in the field of "inverse" chip- cross- ratio provides the base for formulation of design fundamentals and drafts of a novel milling tool with peeling function

Proposal of MIMO Ultra-Wide Band Antenna with Low Mutual Coupling

In this paper, a new ultra-wide band (UWB) MIMO antenna is proposed. A MIMO antenna set consists of two single ultra-wide band antennas. This simple and compact MIMO antenna, which is designed to work from 3.1 GHz to 10.6 GHz, has a broad bandwidth with the VSWR ≤ 2. In addition, MIMO antenna characteristics such as radiation pattern, maximal gain are thoroughly investigated

Ensembling techniques in solar panel quality classification

Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%

EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head. Both contracting and expanding paths are synergetically learned by a collaboration head. We have benchmarked our proposed EmbryosFormer on two datasets: a public dataset with mouse embryos with 8-cell stage and an in-house dataset with human embryos with 4-cell stage. Source code: https://github.com/UARK-AICV/Embryos.Comment: Accepted at WACV 202

Hemorrhagic Meningioma With Symptom of Convulsion: A Rare Presentation of Parietal Meningioma

Meningioma is the most common, extra-axial, non-glial intracranial tumor with an incidence of 2.3-5.5/100 000, accounting for 20%-30% of all primary brain tumor diagnoses in adults. Meningiomas associated with intratumoral hemorrhage are very rare occurring in 0.5%-2.4%. of individuals. Herein, we report a rare case of hemorrhagic meningioma with the symptom of convulsion. The case was a 68-year-old woman admitted to the hospital with severe headache and convulsions. Computed tomography revealed an increase in heterogeneous lesion measuring 4 × 3 × 2.5 cm at the right parietal lobe. Brain magnetic resonance imaging (MRI) showed a grossly stable homogeneously enhancing extra-axial mass measuring 43 × 33 × 28 mm, small calcified peripheral, intratumoral hemorrhage. Histopathology showed a multi-celled meningioma with bleeding areas (WHO grade I)

M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation

Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network (M$^2$UNet) for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods