Investigation of Thick-Film-Paste Rheology and Film Material for Pattern Transfer Printing (PTP) Technology

Steady cost pressure in silicon solar cell production leads to a continuous reduction of silver consumption per cell. Pattern Transfer Printing (PTP) technology enables to reduce silver consumption by depositing smaller front electrodes on solar cells. Here, we aim at a better understanding of the laser deposition process. The aspect ratio of printed lines improved with increasing paste yield stress but was lower than the theoretical aspect ratio for a given trench geometry, suggesting that line spreading was caused by the pressure that was due to the vaporization of volatile paste components and a yield stress reduction that was due to local paste heating. A low laser power threshold, mandatory to fabricate narrow electrodes with a high aspect ratio and low amount of debris, could be achieved using pastes with low boiling temperature of volatile components and poor wetting between paste and film. The material with the lowest light transmission exhibited the lowest laser power threshold. We attribute this to the weaker adhesion to the paste and a better alignment with the laser focal plane. Our results provide valuable guidelines for paste and film material design aimed at narrower electrodes, with a higher aspect ratio to be obtained at an even lower laser power threshold in PTP-based solar cell metallization

Deterministic quantum mechanics: The role of the Maxwell–Boltzmann distribution

To be accepted by the community, the claim that nuclear motion has to be treated classically must be tested for all kinds of phenomena. For the moment we claim that in a quantum chemical calculation, a classical description of nuclear motion is superior to the use of the Schrödinger equation, and investigate how far we get with this statement. In the present paper we address the question what nuclear quantum statistics means in this context. We will show that the Maxwell–Boltzmann velocity distribution evolves quickly in any molecular dynamics simulation and this guarantees the physically correct behavior of molecular systems. Using first-principles molecular dynamics simulations, or more precisely Car–Parrinello molecular dynamics, we investigate what this means for Bose-Einstein condensates and for Cooper pairs. It turns out that our approach can explain all relevant phenomena. As a consequence, we can introduce a deterministic formulation of quantum mechanics and can get rid of all the paradoxa in traditional quantum mechanics. The basic idea is to treat electrons and nuclei differently

SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection

Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult settings and data modalities. However, most current methods are not suited to address 3D objects captured from differing poses. While solutions using Neural Radiance Fields (NeRFs) have been proposed, they suffer from excessive computation requirements, which hinder real-world usability. For this reason, we propose the novel 3D Gaussian splatting-based framework SplatPose which, given multi-view images of a 3D object, accurately estimates the pose of unseen views in a differentiable manner, and detects anomalies in them. We achieve state-of-the-art results in both training and inference speed, and detection performance, even when using less training data than competing methods. We thoroughly evaluate our framework using the recently proposed Pose-agnostic Anomaly Detection benchmark and its multi-pose anomaly detection (MAD) data set.Comment: Visual Anomaly and Novelty Detection 2.0 Workshop at CVPR 202

Classical nuclear motion: Does it fail to explain reactions and spectra in certain cases?

Is a classical description of nuclear motion sufficient when describing chemical reactions and spectra? This question is interesting because many researchers use a classical description of nuclear motion in molecular dynamics simulations. The present paper investigates some phenomena that were previously attributed to nuclear quantum effects. The question is if these phenomena can be modeled with traditional Car–Parrinello molecular dynamics, that is, with a method which treats nuclear motion classically and which is widely applied to the simulation of chemical reactions and spectra. We find that for the investigated system no additional paradigm is needed for describing chemical reactions. The special reactivity observed for carbenes can be attributed to the special environment represented by a noble gas matrix and to an additional transition state that was not considered before. Also the infrared spectrum of porphycene is perfectly modeled by traditional Car–Parrinello molecular dynamics. More studies are necessary to decide to what extent classical nuclear motion can replace the quantum mechanical description

Learning to Localize in New Environments from Synthetic Training Data

Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene. This can either be very expensive or simply impossible for large, unknown environments, for example in search-and-rescue scenarios. Although there are learning-based approaches that operate scene-agnostically, the generalization capability of these methods is still outperformed by classical approaches. In this paper, we present an approach that can generalize to new scenes by applying specific changes to the model architecture, including an extended regression part, the use of hierarchical correlation layers, and the exploitation of scale and uncertainty information. Our approach outperforms the 5-point algorithm using SIFT features on equally big images and additionally surpasses all previous learning-based approaches that were trained on different data. It is also superior to most of the approaches that were specifically trained on the respective scenes. We also evaluate our approach in a scenario where only very few reference images are available, showing that under such more realistic conditions our learning-based approach considerably exceeds both existing learning-based and classical methods

Learning-Based Real-Time Torque Prediction for Grasping Unknown Objects with a Multi-Fingered Hand

When grasping objects with a multi-finger hand, it is crucial for the grasp stability to apply the correct torques at each joint so that external forces are countered. Most current systems use simple heuristics instead of modeling the required torque correctly. Instead, we propose a learning-based approach that is able to predict torques for grasps on unknown objects in real-time. The neural network, trained end-to-end using supervised learning, is shown to predict torques that are more efficient, and the objects are held with less involuntary movement compared to all tested heuristic baselines. Specifically, for 90 % of the grasps the translational deviation of the object is below 2.9 mm and the rotational below 3.1°. To generate training data, we formulate the analytical computation of torques as an optimization problem and handle the indeterminacy of multi-contacts using an elastic model. We further show that the network generalizes to predict torques for unknown objects on the real robot system with an inference time of 1.5 ms

Highly Transparent, Yet Photoluminescent: 2D CdSe/CdS Nanoplatelet-Zeolitic Imidazolate Framework Composites Sensitive to Gas Adsorption

In this work, thin composite films of zeolitic imidazolate frameworks (ZIFs) and colloidal two-dimensional (2D) core-crown CdSe/CdS nanoplatelet (NPL) emitters with minimal scattering are formed by a cycled growth method and yield highly transparent coatings with strong and narrow photoluminescence of the NPLs at 546 nm (FWHM: 25 nm) in a solid-state composite structure. The porous ZIF matrix acts as functional encapsulation for the emitters and enables the adsorption of the guest molecules water and ethanol. The adsorption and desorption of the guest molecules is then characterized by a reversable photoluminescence change of the embedded NPLs. The transmittance of the composite films exceeds the values of uncoated glass at visible wavelengths where the NPL emitters show no absorption (>540 nm) and renders them anti-reflective coatings. At NPL absorption wavelengths (440–540 nm), the transmittance of the thin composite film-coated glass lies close to the transmittance of uncoated glass. The fast formation of innovative, smooth NPL/ZIF composite films without pre-polymerizing the colloidal 2D nanostructures first provides a powerful tool toward application-oriented photoluminescence-based gas sensing

New approaches to edge passivation of laser cut PERC solar cells

Recently the development trend in the PV industry is towards much larger wafer formats. With increasing wafer area and the resulting increase in short-circuit current at the cell level, there is also a trend towards sub-cells (solar cell cut into smaller pieces) for module integration. Using sub-cells, the resistance losses through the connection can be reduced. Modules based on sub-cells achieve higher levels of fill factors and thus a higher nominal power. However, the energy yield of such sub-cells is reduced compared to full cells due to the non-passivated laser edge. The laser cut edge causes a high recombination of the charge carriers, which negatively affects the pseudo fill factor as well as open-circuit voltage of the cell. The current work introduces two different approaches for passivating the laser separated PERC solar cells. The experiments were performed on p-type PERC monofacial cells and laser scribe and mechanical cleavage (LSMC) technique was used to obtain sub-cells from the host cells. The method ‘laser scribing and simultaneous Al doping’ increases the pFF of the cleaved cells by +0.2–0.4%abs in comparison to the reference cleaved cells whereas the method ‘laser scribing and subsequent Al doping’ shows an improvement in efficiency of the cleaved cells by + 0.2% abs

Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks. The key idea is to exploit scalable and structured posteriors of neural networks as informative priors with generalization guarantees. Our learned priors provide expressive probabilistic representations at large scale, like Bayesian counterparts of pre-trained models on ImageNet, and further produce non-vacuous generalization bounds. We also extend this idea to a continual learning framework, where the favorable properties of our priors are desirable. Major enablers are our technical contributions: (1) the sums-of-Kronecker-product computations, and (2) the derivations and optimizations of tractable objectives that lead to improved generalization bounds. Empirically, we exhaustively show the effectiveness of this method for uncertainty estimation and generalization