Investigation of CdS Nanowires and Planar Films for Enhanced Performance as Window Layers in CdS-CdTe Solar Cell Devices

Cadmium sulfide (CdS) and cadmium telluride (CdTe) are two leading semiconductor materials used in the fabrication of thin film solar cells of relatively high power conversion efficiency and low manufacturing cost. In this work, CdS/CdTe solar cells with a varying set of processing parameters and device designs were fabricated and characterized for comparative evaluation. Studies were undertaken to elucidate the effects of (i) each step in fabrication and (ii) parameters like thickness, sheet resistance, light absorptivity solution concentration, inert gas pressure etc. Best results were obtained when the thickness of CdS planar film for the window layer was in the range of 150 nm to 200 nm. Also, CdS nanowires were fabricated for use as the window layer in CdS-CdTe solar cells. Their materials characteristics were studied with scanning electron microscopy (SEM) and X-ray Diffraction (XRD). Spectral absorption measurements on the planar CdS films and nanowire CdS layers were performed and results compared. It was established that the nanowire CdS design was superior because its absorption of sunlight was far less than that of planar CdS film, which would lead to enhanced performance in the CdS-CdTe solar cell through higher short circuit current density and higher open circuit voltage. Diode behavior of CdS-CdTe devices on planar CdS and nanowire CdS was analyzed and compared. KEYWORDS: Thin Film Solar Cell, Nanowire, UV Absorption, Open-circuit Voltage, Close Space Sublimatio

Diffusive Charge Transport in Graphene

The physical mechanisms limiting the mobility of graphene on SiO2 are studied and printed graphene devices on a flexible substrate are realized. Intentional addition of charged scattering impurities is used to study the effects of charged impurities. Atomic-scale defects are created by noble-gas ions irradiation to study the effect of unitary scatterers. The results show that charged impurities and atomic-scale defects both lead to conductivity linear in density in graphene, with a scattering magnitude that agrees quantitatively with theoretical estimates. While charged impurities cause intravalley scattering and induce a small change in the minimum conductivity, defects in graphene scatter electrons between the valleys and suppress the minimum conductivity below the metallic limit. Temperature-dependent measurements show that longitudinal acoustic phonons in graphene produce a small resistivity which is linear in temperature and independent of carrier density; at higher temperatures, polar optical phonons of the SiO2 substrate give rise to an activated, carrier density-dependent resistivity. Graphene is also made into high mobility transparent and flexible field effect device via the transfer-printing method. Together the results paint a complete picture of charge carrier transport in graphene on SiO2 in the diffusive regime, and show the promise of graphene as a novel electronic material that have potential applications not only on conventional inorganic substrates, but also on flexible substrates

PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs

Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches class restriction to candidate constraints according to their measuring scores.We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively. The experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.Comment: Accepted by AAAI2

Automatic Rule Generation for Time Expression Normalization

The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.Comment: Accepted to Findings of EMNLP 202

Robust Sparse Mean Estimation via Incremental Learning

In this paper, we study the problem of robust sparse mean estimation, where the goal is to estimate a $k$-sparse mean from a collection of partially corrupted samples drawn from a heavy-tailed distribution. Existing estimators face two critical challenges in this setting. First, they are limited by a conjectured computational-statistical tradeoff, implying that any computationally efficient algorithm needs $\tilde\Omega(k^2)$ samples, while its statistically-optimal counterpart only requires $\tilde O(k)$ samples. Second, the existing estimators fall short of practical use as they scale poorly with the ambient dimension. This paper presents a simple mean estimator that overcomes both challenges under moderate conditions: it runs in near-linear time and memory (both with respect to the ambient dimension) while requiring only $\tilde O(k)$ samples to recover the true mean. At the core of our method lies an incremental learning phenomenon: we introduce a simple nonconvex framework that can incrementally learn the top-$k$ nonzero elements of the mean while keeping the zero elements arbitrarily small. Unlike existing estimators, our method does not need any prior knowledge of the sparsity level $k$. We prove the optimality of our estimator by providing a matching information-theoretic lower bound. Finally, we conduct a series of simulations to corroborate our theoretical findings. Our code is available at https://github.com/huihui0902/Robust_mean_estimation

Gate-controlled reversible rectifying behaviour in tunnel contacted atomically-thin MoS2_{2} transistor

Atomically-thin 2D semiconducting materials integrated into van der Waals heterostructures have enabled architectures that hold great promise for next generation nanoelectronics. However, challenges still remain to enable their full acceptance as compliant materials for integration in logic devices. Two key-components to master are the barriers at metal/semiconductor interfaces and the mobility of the semiconducting channel, which endow the building-blocks of ${pn}$ diode and field effect transistor. Here, we have devised a reverted stacking technique to intercalate a wrinkle-free h-BN tunnel layer between MoS$_{2}$ channel and contacting electrodes. Vertical tunnelling of electrons therefore makes it possible to suppress the Schottky barriers and Fermi level pinning, leading to homogeneous gate-control of the channel chemical potential across the bandgap edges. The observed unprecedented features of ambipolar ${pn}$ to ${np}$ diode, which can be reversibly gate tuned, paves the way for future logic applications and high performance switches based on atomically thin semiconducting channel.Comment: 23 pages, 5 main figures + 9 SI figure

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances (https://www.science.org/doi/10.1126/sciadv.adi1480