Oxide thin films for sustainable, multifunctional and flexible electronics

The growing range of applications of large-area electronics (LAE) in the last years is starting to require levels of performance, functionality and cost not compatible with the current thin-film technologies, such as a-Si or low-temperature polysilicon (LTPS) thin-film transistors (TFTs). This starts to be even more relevant when low-cost flexible substrates and processing technologies are considered. In this context, amorphous oxide semiconductors (AOS) are becoming essential materials in this field. AOS are recognized for their remarkable features such as good uniformity even when produced at room temperature, enabling applications in large area and flexible electronics; wide band gap (thus high transparency), making them suitable for transparent electronics; and good electrical performance despite their amorphous structure, enabling flexible circuitry operating at high-kHz to MHz range. Since the initial publication by Nomura et al. on indium-gallium-zinc oxide (IGZO) TFTs in 2004, these devices had a tremendous development and are now implemented in Gen10 display fabs for the production of low power consumption, high refresh rate and high resolution displays. But oxide electronics has a great potential for going well beyond display backplane applications: in fact, by combining new materials/structures, processing techniques and circuit design architectures having in mind conventional CMOS concepts adapted to the speed and (typically) unipolar limitations of oxide TFTs, these devices can be seen as a powerful platform for sustainable, multifunctional and flexible electronics. This presentation will precisely address these topics, which are currently being studied at CENIMAT: • Turning oxides into an even more sustainable approach for electronics, by replacing IGZO by indium- and gallium-free semiconductors, such as zinc-tin oxide (ZTO). Even if the initial reports on this material have shown largely inferior TFT performance and superior processing temperature when compared to IGZO TFTs, it will be shown that sputtered ZTO TFTs with On/Off ratio above 106, field effect mobility close to 10 cm2/Vs, subthreshold slope of 0.3 V/dec and non-significant performance variation under bending can now be obtained on PEN foil with only 150 C processing temperature. • Taking oxide TFTs towards the limits of microscale patterning, investigating the peculiarities found for oxide TFTs with channel lengths as low as 1 micron. While short channel effects as channel length modulation or drain induced barrier lowering start to be relevant, cut-off frequencies of oxide TFTs can exceed 100 MHz at this scale. The electrical characterization is being supported by TCAD simulations. [1, 2] • Integrating oxide TFTs in digital, analog and mixed-signal circuits with a significant level of complexity (100s of TFTs). To compensate for the intrinsic performance limitations compared to Si-based CMOS, high-gain topologies are being used to create logic gates, amplifiers, multipliers, phase-generators, among many other blocks. As examples, four-quadrant analog multipliers with a gain improvement of 7.2 dB over the Gilbert cell with the diode-connected load or amplifiers with a gain of 34 dB and a power consumption of 0.576 mW (load of 10 MOhm//16 pF) will be presented [3, 4]. These blocks are being used for different fields of applications, such as smart-bottles or flexible x-ray sensors. For this last application we recently found our oxide TFTs to have excellent ionizing-radiation hardness, showing to be insensitive even to exposure doses of 410 krad(SiO2) [5]. [1] Bahubalindruni, P. G. et al. Journal of Display Technology 12, 515-518 (2016) [2] Martins, J., Barquinha, P. & Goes, J. in Technological Innovation for Cyber-Physical Systems: 7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016, Costa de Caparica, Portugal, April 11-13, 2016, Proceedings (eds M. Luis Camarinha-Matos, António J. Falcão, Nazanin Vafaei, & Shirin Najdi) 551-557 (Springer International Publishing, 2016) [3] Bahubalindruni, P. G. et al. Ieee Electron Device Letters 37, 419-421 (2016

Attention Boosted Autoencoder for Building Energy Anomaly Detection

Leveraging data collected from smart meters in buildings can aid in developing policies towards energy conservation. Significant energy savings could be realised if deviations in the building operating conditions are detected early, and appropriate measures are taken. Towards this end, machine learning techniques can be used to automate the discovery of these abnormal patterns in the collected data. Current methods in anomaly detection rely on an underlying model to capture the usual or acceptable operating behaviour. In this paper, we propose a novel attention mechanism to model the consumption behaviour of a building and demonstrate the effectiveness of the model in capturing the relations using sample case studies. A real-world dataset is modelled using the proposed architecture, and the results are presented. A visualisation approach towards understanding the relations captured by the model is also presented

A STUDY ON DENITRIFICATION IN A FLUIDIZED BED BIOREACTOR

The work involves experimental investigation on biological denitrification in a fluidized bed reactor under anaerobic conditions using the microorganism Pseudomonas Stutzeri and plastic beads as fluidizing medium. The influence of various parameters like pH, initial nitrate concentration, Carbon/Nitrogen ratio, hydraulic retention time on nitrate-nitrogen removal rate from synthetic effluent prepared at the laboratory were studied in detail. The optimum operating conditions of pH, initial nitrate concentration, Carbon/Nitrogen ratio and flow rate obtained are 7.0–7.5, 15 mg/l, 1.5–2.0 and 4.41 x 10-5 m3/s respectively

Ultrafine aluminium: Quench collection of agglomerates

Combustion of aluminized solid propellants exhibits phenomena associated with accumulation, agglomeration, ignition, and combustion of ultra-fine aluminium particles. In this study, agglomeration phenomenon of ultra-fine aluminium in solid propellant combustion is investigated using quench collection experimental technique over the pressure ranges from 2MPa to 8MPa. The ultra-fine aluminium powder synthesized by Radio Frequency Induction Plasma technique having harmonic mean size of 438nm is used for agglomeration study. The quenching distance is varied from 5mm to 71mm from the propellant burning surface to estimate the effect on agglomerate size. The morphology and chemical compositions of the collected products were then studied by using scanning electron microscopy coupled with energy dispersive (SEM-EDS) method. Under the explored experimental conditions, the results confirm that ultra-fine aluminium propellant show aggregation/agglomeration with the size ranging from 11 – 21 μm in combustion products. Smaller diameter condensed phase products will likely decrease two-phase flow loss and reduce slag accumulation

Application of artificial neural networks for the prediction of aluminium agglomeration processes

Aluminium is universal and vital constituent in composite propellants and typically used to improve performance. Aluminum agglomeration takes place on the burning surface of aluminized propellants, which leads to reduced combustion efficiency and 2P flow losses. To understand the processes and behaviour of aluminum agglomeration, particles size distribution of composite propellants were studied using a quench particle collection technique, at 2 to 8 MPa and varying quench distances from 5mm to 71mm. To predict the agglomerate diameter of metallized/ultra-fine aluminium of composite propellants, a new artificial neural network (ANN) model was derived. Five Layered Feed Forward Back Propagation Neural Network was developed with sigmoid as a transfer function by varying feed variables in input layer such as Quench distance (QD) and pressure (P). The ANN design was trained victimization stopping criterion as one thousand iterations. Five ANN models dealing with the combustion of AP/Al/HTPB and one ANN model of AP/UFAl/HTPB composite propellants were presented. The validated ANN models will be able to predict unexplored regimes wherein experimental data are not available. From the present work it was ascertained that, for agglomeration produced by quench collection technique, the ANN is one of a substitute method to predict the agglomerate diameter and results can be evaluated rather than experimented, with reduced time and cost. The resulting agglomerates sizes from ANN model, matches with the experimental results. The percentage error is less than 3.0% of the label propellants used in this work

Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows

Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primarily rely on optimizing heuristics, such as the Fr\'echet Inception Distance. While recent developments have introduced principled methods for evaluating precision and recall, they have yet to be successfully integrated into the training of generative models. Our main contribution is a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows, which explicitly optimizes a user-defined trade-off between precision and recall. More precisely, we show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the \mbox{\em PR-divergences}. Conversely, any $f$-divergence can be written as a linear combination of PR-divergences and corresponds to a weighted precision-recall trade-off. Through comprehensive evaluations, we show that our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet

A Routing Algorithm To Reduce The Queueing Complexity In Communication Networks

A new adaptive routing algorithm built ahead the widely studied back-pressure algorithm. We decouple the routing and scheduling components of the algorithm by designing a probabilistic routing table that is used to route packets to per-destination queues. The scheduling decisions in the case of wireless networks are made using counters called shadow queues. The results are also extended to the case of networks that employ simple forms of network coding. The routing algorithm is considered to decrease the average number of hops used by packets in the network. This idea along with the scheduling/routing decoupling leads to setback decrease compared with the traditional back-pressure algorithm. The algorithm can be applied to wire line and wireless networks. Wide simulations show spectacular improvement in delay performance compared to the back-pressure algorithm.  When network coding is employed per-previous-hop queues may also be essential but this is a requirement compulsory by network coding not by our algorithm.