Ortsaufgelöste Charakterisierung organischer Solarzellen mittels Lumineszenzstrahlung

Durch detaillierte Untersuchung der physikalischen Prozesse, die der Wirkungsweise von Polymer-Solarzellen zu Grunde liegen, und der daran gekoppelten Entwicklung neuer Materialien konnte der Energiekonversionswirkungsgrad von organischen Solarzellen bereits deutlich gesteigert werden. Die für diese Technologie hohen Wirkungsgrade von > 10% lassen sich bisher jedoch nur im Labormaßstab mit Zellflächen weniger mm² realisieren. Durch die Skalierung auf größere Zellflächen treten material- und prozessbedingt lokale Defekte auf, die den Wirkungsgrad beträchtlich mindern können. Darüber hinaus treten aufgrund der notwendigen Verwendung mindestens einer transparenten Elektrode inhärente Leistungsverluste auf, die mit der Solarzelllänge skalieren. Schließlich stellt die beschränkte Lebensdauer aufgrund einer Vielzahl von vorwiegend lokal auftretenden Alterungseffekten einen für die kommerzielle Verbreitung limitierenden Faktor dar. Bezogen auf die Fläche der Solarzelle treten alle diese Effekte räumlich ungleichmäßig auf. Für deren Untersuchung werden daher experimentelle und theoretische Methoden benötigt, die die Solarzelle nicht einfach zusammengefasst, sondern in ihrer Gesamtheit als flächig verteiltes System charakterisieren und beschreiben können. In dieser Arbeit wurde die „Bildgebende Lumineszenzdetektion“ für die ortsaufgelöste elektrooptische Charakterisierung von Polymer-Solarzellen genutzt. Die Methode basiert auf der Detektion der vom organischen Halbleiter unter stationärer Anregung emittierten Elektrolumineszenzstrahlung mit einer hochempfindlichen CCD-Kamera. Der bisherige Einsatz dieser Messmethode an organischen Solarzellen war auf begleitende Untersuchungen im Rahmen von Alterungsexperimenten beschränkt. Unter Kenntnis des exakten Schichtaufbaus und durch den Vergleich mit anderen Charakterisierungsmethoden konnten dabei bereits durch rein qualitative Analysen wertvolle Informationen über Degradationsmechanismen und degradationsfördernde Schwachstellen gewonnen werden. Die vorliegende Arbeit geht über derartige qualitative Analysen hinaus. Die Zielstellung dieser Arbeit liegt in der quantitativen Interpretation von Elektrolumineszenzaufnahmen von Dünnschichtsolarzellen. Dazu wurden Modelle und Algorithmen entwickelt, die nicht nur das Verständnis der der Bildgebung zu Grunde liegenden Kontrastmechanismen fördern, sondern auch die Extraktion relevanter Materialparameter ermöglichen. Die entwickelten Methoden wurden exemplarisch an Polymer-Solarzellen auf Basis verschiedener Materialsysteme getestet, lassen sich jedoch prinzipiell auf andere Dünnschichttechnologien übertragen. Für die flächige Beschreibung von Dünnschichtsolarzellen wurde ein Modell entwickelt, welches eine umfassende elektrooptische Charakterisierung ermöglicht. Innerhalb dieses Modells wird die Solarzelle in miteinander gekoppelte Einheitszellen zerlegt, welche durch ein beliebiges Ersatzschaltbild modelliert werden können. Da die Modellierung im Rahmen dieser Arbeit anhand eines serienwiderstandslimitierten Eindioden-Ersatzschaltbildes erfolgte, wurde das Modell als „Mikrodioden-Modell“ bezeichnet. Die Anwendung des Mikrodioden-Modells auf Elektrolumineszenzaufnahmen verschiedener aktueller Materialsysteme zeigte, dass der zusammengefasste Serienwiderstand der Solarzellen effektiv in den Flächenwiderstand der transparenten leitfähigen Elektrode sowie in einen zusammengefassten Widerstand, der den Volumenwiderstand der Aktivschicht und die Kontaktwiderstände der im Schichtaufbau vorhandenen Grenzflächen repräsentiert, zerlegt werden kann. Die gleichzeitige Bestimmung des lokalen Potentials sowie der lokal fließenden Stromdichte ermöglichte die Extraktion der widerstandsbefreiten Kennlinien. Durch den Vergleich des optisch detektierten Rekombinationsstroms mit dem gesamtheitlich fließenden elektrischen Strom wurde zudem der Diodenidealitätsfaktor der verwendeten Materialsysteme ermittelt. Aufgrund der für die Nutzung des Mikrodioden-Modells nicht zwangsweise erfüllten Voraussetzung der flächigen Homogenität der Probe wurde eine ergänzende Beschreibung durch ein Modell lokal unabhängiger Dioden eingeführt. Dieses Modell wurde genutzt, um die Elektrolumineszenzemission flächig stark inhomogener Solarzellen zu untersuchen. Dabei wurde gezeigt, dass das Elektrolumineszenzsignal lokal in eine serienwiderstands- und eine materialspezifische Komponente zerlegt werden kann, die im Einklang mit dem Entmischungsverhalten der untersuchten Materialkombination steht. Anhand von Netzwerksimulationen auf Basis der extrahierten Materialparameter wurde schließlich gezeigt, dass das Verhältnis von Photostromdichte zu Flächenwiderstand eine Ortsabhängigkeit der Solarzellenfunktionalität induziert. Neben einer Geometrieoptimierung wurde daraus eine allgemeine Skalierungsrelation abgeleitet und die Ortsabhängigkeit durch rasternde Photostrommessungen experimentell bestätigt. Für die Untersuchung der Stabilität der elektrischen Kontakte von organischen Solarzellen innerhalb von Langzeitstudien wurde zudem das Verfahren zur Quantifizierung der nicht degradierten Fläche verbessert. Durch Implementierung eines lokal adaptiven Binarisierungsverfahrens konnte so auch die Bestimmung der effektiven Fläche von stark inhomogen bzw. schwach emittierenden Solarzellen erreicht werden.The detailed investigation of physical processes relevant for the functioning of polymer solar cells along with the development of novel materials manifested the path to increasingly high energy conversion efficiencies. Nowadays, efficiencies > 10% can be realized, though only on the lab scale and with active areas of a few mm². Upscaling the active area immediately causes local defects that are induced by the materials and processes involved and have direct impact on the efficiency. Furthermore, meeting the requirement of at least one transparent electrode inherently causes power losses scaling with the solar cell length. In addition to such power losses a multitude of ageing effects still limits the lifetime of organic solar cells, which has a major impact on their commercialization. All of the aforementioned effects occur non-uniformly with respect to the solar cell area. Thus there is a need for experimental and theoretical methods allowing for enhanced characterization by distributed description of the whole solar cell at once. In this work the “Luminescence Imaging” was used for locally resolved electro-optical characterization of polymer solar cells. This method is based on the detection of electroluminescence radiation emitted by the organic semiconductor during stationary excitation. Previously, the application of Luminescence Imaging to organic solar cells was limited to complementary characterization in long term ageing experiments. Such qualitative analysis already allowed for meaningful results on degradation mechanisms and design flaws accelerating degradation pathways. However, it was mandatory to exactly know the layer composition and to compare to other characterization methods. The present work exceeds such qualitative analysis by quantitative interpretation of electroluminescence images of thin-film polymer solar cells. For promoting the understanding of the contrast mechanisms relevant for the luminescence pattern as well as for allowing to extract relevant material parameters, appropriate models along with suitable algorithms were developed. The developed methods were exemplarily applied to polymer solar cells based on different state-of-the-art material systems but can be in principle applied to other thin-film solar cells as well. Allowing for comprehensive electro-optical characterization a model based on distributed description of thin-film solar cells was developed. Within this model a solar cell is partitioned into unit cells with next-neighbor coupling. The unit cell can be modeled by an arbitrary equivalent circuit. In this work, a series resistance limited one-diode model was used, motivating to term the distributed model as “Microdiode-Model”. Application of the Microdiode-Model to electroluminescence images of different state-of-the-art material systems showed that the lumped series resistance of the whole solar cell can be effectively divided into the sheet resistance of the transparent electrode and a combined resistance representing the bulk resistivity of the active layer and the contact resistance to its adjacent layers. The simultaneous determination of the local electric potential along with the local net current-densities allowed for extraction of the current-voltage-characteristics free from series resistance limitations. Comparing the optically detected recombination current to the net electrical current furthermore allowed for determination of the diode ideality of the different material systems. For solar cells not meeting the requirement of planar homogeneity a complementary description by a model of locally independent diodes was introduced and applied to electroluminescence patterns of such solar cells. It is shown that the electroluminescence intensity can be locally decoupled into components specific for the series resistance as well as the materials involved, and that these parameters behave according to the large scale phase separation of the material combination investigated. By network simulations based on the parameters extracted it is finally shown that the relation of photocurrent-density and sheet resistance causes a local dependence in the solar cell functioning. In addition to simple geometry optimization a general scaling relation was derived and the local dependence was experimentally confirmed by light-beam induced photocurrent measurements. Furthermore, the method for quantifying the effective active area of organic solar cells during long term ageing studies was improved by implementing a locally adaptive binarization algorithm. Thus investigating the stability of the electric contacts of inhomogeneous or weak emitting solar cells was accomplished

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks

Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed to prevent gradient leakage without loss of model utility by incorporating a PRivacy EnhanCing mODulE (PRECODE) based on variational modeling. Without further analysis, it was shown that PRECODE successfully protects against gradient inversion attacks. In this paper, we make multiple contributions. First, we investigate the effect of PRECODE on gradient inversion attacks to reveal its underlying working principle. We show that variational modeling introduces stochasticity into the gradients of PRECODE and the subsequent layers in a neural network. The stochastic gradients of these layers prevent iterative gradient inversion attacks from converging. Second, we formulate an attack that disables the privacy preserving effect of PRECODE by purposefully omitting stochastic gradients during attack optimization. To preserve the privacy preserving effect of PRECODE, our analysis reveals that variational modeling must be placed early in the network. However, early placement of PRECODE is typically not feasible due to reduced model utility and the exploding number of additional model parameters. Therefore, as a third contribution, we propose a novel privacy module -- the Convolutional Variational Bottleneck (CVB) -- that can be placed early in a neural network without suffering from these drawbacks. We conduct an extensive empirical study on three seminal model architectures and six image classification datasets. We find that all architectures are susceptible to gradient leakage attacks, which can be prevented by our proposed CVB. Compared to PRECODE, we show that our novel privacy module requires fewer trainable parameters, and thus computational and communication costs, to effectively preserve privacy.Comment: 14 pages (12 figures 6 tables) + 6 pages supplementary materials (6 tables). Under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv admin note: substantial text overlap with arXiv:2208.0476

Model-based data generation for the evaluation of functional reliability and resilience of distributed machine learning systems against abnormal cases

Future production technologies will comprise a multitude of systems whose core functionality is closely related to machine-learned models. Such systems require reliable components to ensure the safety of workers and their trust in the systems. The evaluation of the functional reliability and resilience of systems based on machine-learned models is generally challenging. For this purpose, appropriate test data must be available, which also includes abnormal cases. These abnormal cases can be unexpected usage scenarios, erroneous inputs, accidents during operation or even the failure of certain subcomponents. In this work, approaches to the model-based generation of an arbitrary abundance of data representing such abnormal cases are explored. Such computer-based generation requires domain-specific approaches, especially with respect to the nature and distribution of the data, protocols used, or domain-specific communication structures. In previous work, we found that different use cases impose different requirements on synthetic data, and the requirements in turn imply different generation methods [1]. Based on this, various use cases are identified and different methods for computer-based generation of realistic data, as well as for the quality assessment of such data, are explored. Ultimately we explore the use of Federated Learning (FL) to address data privacy and security challenges in Industrial Control Systems. FL enables local model training while keeping sensitive information decentralized and private to their owners. In detail, we investigate whether FL can benefit clients with limited knowledge by leveraging collaboratively trained models that aggregate client-specific knowledge distributions. We found that in such scenarios federated training results in a significant increase in classification accuracy by 31.3% compared to isolated local training. Furthermore, as we introduce Differential Privacy, the resulting model achieves on par accuracy of 99.62% to an idealized case where data is independent and identically distributed across clients

Optimal geometric design of monolithic thin-film solar modules: Architecture of polymer solar cells,” Solar Energ

a b s t r a c t In this study the geometrical optimization of monolithically integrated solar cells into serially connected solar modules is reported. Based on the experimental determination of electrodes 0 sheet and intermittent contact resistances, the overall series resistance of individual solar cells and interconnected solar modules is calculated. Taking a constant photocurrent generation density into account, the total Joule respectively resistive power losses are determined by a self-consistent simulation according to the 1-diode model. This method allows optimization of the solar module geometry depending on the material system applied. As an example, polymer solar modules based on ITO-electrodes and ITO-free electrodes were optimized with respect to structuring dimensions

Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures

The Flora Incognita app - interactive plant species identification

Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education

Plant species classification using flower images - a comparative study of local feature representations

Steady improvements of image description methods induced a growing interest in imagebased plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flowerbased plant species classification

Code for "Image-Based Classification of Plant Genus and Family for Trained and Untrained Plant Species"

This archive contains code for running image classification experiments on the PlantCLEF2016 dataset as used for the publication "Image-Based Classification of Plant Genus and Family for Trained and Untrained Plant Species" M. Seeland, M. Rzanny, D. Boho, J. Wäldchen and P. Mäder, 2018. BMC Bioinformatics, 2018

Multi-view classification with convolutional neural networks.

Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin

Dropout Is NOT All You Need to Prevent Gradient Leakage

Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms