Environmental, Thermal, and Electrical Susceptibility of Black Phosphorus Field Effect Transistors

Atomic layers of black phosphorus (P) isolated from its layered bulk make a new two-dimensional (2D) semiconducting crystal with sizable direct bandgap, high carrier mobility, and promises for 2D electronics and optoelectronics. However, the integrity of black P crystal could be susceptible to a number of environmental variables and processes, resulting in degradation in device performance even before the device optical image suggests so. Here, we perform a systematic study of the environmental effects on black P electronic devices through continued measurements over a month under a number of controlled conditions, including ambient light, air, and humidity, and identify evolution of device performance under each condition. We further examine effects of thermal and electrical treatments on inducing morphology and, performance changes and failure modes in black P devices. The results suggest that procedures well established for nanodevices in other 2D materials may not directly apply to black P devices, and improved procedures need to be devised to attain stable device operation.Comment: in Journal of Vacuum Science & Technology B (2015

Hybrid Stochastic Synapses Enabled by Scaled Ferroelectric Field-effect Transistors

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work we leverage the stochastic switching of a three-state ferroelectric field effect transistor (FeFET) to implement a long-term and short-term 2-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28nm high-$k$ metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware-algorithm co-design analysis reveals the efficacy of the 2-tier memory in comparison to binary stochastic synapses in on-chip learning tasks -- paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones

Municipal wastewater can result in a dramatic decline in freshwater fishes: a lesson from a developing country

Impacts of ineffective wastewater management on the biodiversity of receiving waters in developing countries are poorly documented. Using a before-after-control-impact methodology, we measured the effects of untreated wastewater release on the fish community in the Barnoi River, Bangladesh. In 2006, prior to untreated wastewater discharge, fish abundance, species richness and water quality were similar across sampling sites. In 2016, after 8 years of wastewater release to the downstream reach, fish abundance and species richness were reduced by >47% and >35% respectively at downstream sites compared to unaffected upstream sites and >51% and >41% lower respectively compared to the pre-wastewater discharge period. The wastewater impact was particularly severe during months of low discharge (October–December). Water transparency, dissolved oxygen and pH were lower (P < 0.001) at impacted downstream sites compared to upstream sites. Nineteen species (41.3% of all species we recorded) are threatened in Bangladesh and the abundance of these species, except one, decreased significantly (P < 0.05) at the impacted sites. We recommend improved wastewater management by applying primary treatment facilities and incorporating reedbed filtration as a mean of biological treatment, into the canals carrying wastewaters. The success of such measures should be tested with fish species that were most responsive to wastewater, using the indicator species concept

ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain

Transformer design is the de facto standard for natural language processing tasks. The success of the transformer design in natural language processing has lately piqued the interest of researchers in the domain of computer vision. When compared to Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) are becoming more popular and dominant solutions for many vision problems. Transformer-based models outperform other types of networks, such as convolutional and recurrent neural networks, in a range of visual benchmarks. We evaluate various vision transformer models in this work by dividing them into distinct jobs and examining their benefits and drawbacks. ViTs can overcome several possible difficulties with convolutional neural networks (CNNs). The goal of this survey is to show the first use of ViTs in CV. In the first phase, we categorize various CV applications where ViTs are appropriate. Image classification, object identification, image segmentation, video transformer, image denoising, and NAS are all CV applications. Our next step will be to analyze the state-of-the-art in each area and identify the models that are currently available. In addition, we outline numerous open research difficulties as well as prospective research possibilities.Comment: ICCD-2023. arXiv admin note: substantial text overlap with arXiv:2208.04309 by other author

Representation Learning in Deep RL via Discrete Information Bottleneck

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information

DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network

Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficiently handle multi-scale features intrinsic to manufacturing environments. Additionally, Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features. Then, we incorporated an attention mechanism called Convolutional Block Attention Module in each block of our base ResNet50 network to selectively emphasize informative features and suppress less useful ones. After that we incorporated RoI Align, replacing RoI Pooling for finer region-of-interest alignment and finally the integration of Focal Loss effectively handles class imbalance, crucial for rare defect occurrences. The rigorous evaluation of our model on both the NEU-DET and Pascal VOC datasets underscores its robust performance and generalization capabilities. On the NEU-DET dataset, our model exhibited a profound understanding of steel defects, achieving state-of-the-art accuracy in identifying various defects. Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.Comment: ICCD-2