Automated efficiency loss analysis by luminescence image reconstruction using generative adversarial networks

Identifying solar cell efficiency shortfalls in production lines is crucial to troubleshoot and optimize manufacturing processes. With the adoption of luminescence imaging as a key end-of-line characterization tool, a wealth of information is available to evaluate cell performance and classify defects, suitable for user input-free deep-learning analysis. We propose an automated reconstruction method, based on state-of-the-art generative adversarial networks, to remove defective regions in luminescence images. The reconstructed and original images are compared to estimate the efficiency loss. The method is validated on intentionally damaged cells by reconstructing defect-free images and then predicting the efficiency loss. The method can differentiate between different types of defects and pinpoint the defects that lead to the highest efficiency shortfall, enabling manufacturers to troubleshoot production lines in a fast and cost-effective manner. The proposed approach unlocks the potential of luminescence imaging as an effective end-of-line characterization tool

Magnetohydrodynamic equilibria of a cylindrical plasma with poloidal mass flow and arbitrary cross section shape

The equilibrium of a cylindrical plasma with purely poloidal mass flow and cross section of arbitrary shape is investigated within the framework of the ideal MHD theory. For the system under consideration it is shown that only incompressible flows are possible and, conscequently, the general two dimensional flow equilibrium equations reduce to a single second-order quasilinear partial differential equation for the poloidal magnetic flux function $\psi$, in which four profile functionals of $\psi$ appear. Apart from a singularity occuring when the modulus of Mach number associated with the Alfv\'en velocity for the poloidal magnetic field is unity, this equation is always elliptic and permits the construction of several classes of analytic solutions. Specific exact equlibria for a plasma confined within a perfectly conducting circular cylindrical boundary and having i) a flat current density and ii) a peaked current density are obtained and studied.Comment: Accepted to Plasma Physics & Controlled Fusion, 14 pages, revte

Feature Lines for Illustrating Medical Surface Models: Mathematical Background and Survey

This paper provides a tutorial and survey for a specific kind of illustrative visualization technique: feature lines. We examine different feature line methods. For this, we provide the differential geometry behind these concepts and adapt this mathematical field to the discrete differential geometry. All discrete differential geometry terms are explained for triangulated surface meshes. These utilities serve as basis for the feature line methods. We provide the reader with all knowledge to re-implement every feature line method. Furthermore, we summarize the methods and suggest a guideline for which kind of surface which feature line algorithm is best suited. Our work is motivated by, but not restricted to, medical and biological surface models.Comment: 33 page

Unveiling microscopic carrier loss mechanisms in 12 efficient Cu2ZnSnSe4 solar cells

Understanding carrier loss mechanisms at microscopic regions is imperative for the development of high performance polycrystalline inorganic thin film solar cells. Despite the progress achieved for kesterite, a promising environmentally benign and earth abundant thin film photovoltaic material, the microscopic carrier loss mechanisms and their impact on device performance remain largely unknown. Herein, we unveil these mechanisms in state of the art Cu2ZnSnSe4 CZTSe solar cells using a framework that integrates multiple microscopic and macroscopic characterizations with three dimensional device simulations. The results indicate the CZTSe films have a relatively long intragrain electron lifetime of 10 30 amp; 8201;ns and small recombination losses through bandgap and or electrostatic potential fluctuations. We identify that the effective minority carrier lifetime of CZTSe is dominated by a large grain boundary recombination velocity 104 amp; 8201;cm amp; 8201;s amp; 8722;1 , which is the major limiting factor of present device performance. These findings and the framework can greatly advance the research of kesterite and other emerging photovoltaic material

China’s Artificial Intelligence Innovation:A Top-Down National Command Approach?

China’s open embracing of the age of artificial intelligence (AI) has attracted considerable academic and media attention. Many argue that China has taken advantage of its national approach to contest for AI supremacy and geopolitical dominance. The relevant analyses assume China’s AI plans as being Beijing’s coherent top‐down geopolitically driven national strategy, reflecting Chinese leaders’ global ambitions. This article argues that these views are mistaken. It argues that China’s AI plans are primarily driven by contestation and the struggle for resources among domestic stakeholders who are economically motivated and have little awareness of the bigger geopolitical picture. Instead of a top‐down command approach, China’s national AI plan is an upgrade of existing local AI initiatives to the national level, reflecting a bottom‐up development. This article suggests that the existing analyses vastly exaggerate: (1) Beijing’s capacity to coordinate domestic capital and actors towards a unified, specific strategic objective; and (2) the extent of China’s AI advancement and its geopolitical threat, triggering unnecessary anxiety among China’s near competitors

The Development-Insecurity Nexus in China’s Near-Abroad: Rethinking Cross-border Economic Integration in an Era of State Transformation

Surprisingly, perhaps, China’s flagship Belt and Road Initiative expresses a familiar mix of the security–development nexus and liberal interdependence thesis: Chinese leaders expect economic development and integration will stabilise and secure neighbouring states and improve inter-state relations. However, drawing on the record of China’s intensive economic interaction with Myanmar, we argue that the opposite outcome may occur, for two reasons. First, capitalist development is inherently conflict-prone. Second, moreover, China’s cross-border economic relations today are shaped by state transformation–the fragmentation, decentralisation and internationalisation of party-state apparatuses. Accordingly, economic relations often emerge not from coherent national strategies, but from the uncoordinated, even contradictory, activities of various state and non-state agencies at multiple scales, which may exacerbate capitalist development’s conflictual aspects and undermine official policy goals. In the Sino-Myanmar case, the lead Chinese actors creating and managing cross-border economic engagements are sub-national agencies and enterprises based in, or operating through, Yunnan province. The rapacious form of development they have pursued has exacerbated insecurity, helped to reignite ethnic conflict in Myanmar’s borderlands, and plunged bilateral relations into crisis. Consequently, the Chinese government has had to change its policy and intervene in Myanmar’s domestic affairs to promote peace negotiations

Optimization of solar cell production lines using neural networks and genetic algorithms

© 2020 American Chemical Society. To keep improving the efficiency-to-cost ratio of photovoltaic solar cells, manufacturing lines must be continuously improved. Efficiency optimization is usually performed process-wise and can be slow and time-consuming. In this study, we propose a machine-learning-based method to perform simultaneous multiprocess optimization. Using the natural variation of a production line, we train machine learning models to investigate the relationship between process parameters and cell efficiency. We employ genetic algorithms to identify new process parameters in order to maximize cell efficiency. The proposed method is demonstrated on a simulated production line of monocrystalline aluminum-back surface field solar cells. Using neural networks, an accurate model is built to predict cell efficiencies from input process parameters with errors of <0.03% absolute efficiency. In five iterations, the mean cell efficiency increases from 18.07% to 19.45%. Provided strong process monitoring and accurate wafer tracking, the proposed method is directly applicable to production-type datasets, enabling the photovoltaic industry to build smart factories and join the fourth industrial revolution