Laser applications in thin-film photovoltaics

We review laser applications in thin-film photovoltaics (thin-film Si, CdTe, and Cu(In,Ga)Se2 solar cells). Lasers are applied in this growing field to manufacture modules, to monitor Si deposition processes, and to characterize opto-electrical properties of thin films. Unlike traditional panels based on crystalline silicon wafers, the individual cells of a thin-film photovoltaic module can be serially interconnected by laser scribing during fabrication. Laser scribing applications are described in detail, while other laser-based fabrication processes, such as laser-induced crystallization and pulsed laser deposition, are briefly reviewed. Lasers are also integrated into various diagnostic tools to analyze the composition of chemical vapors during deposition of Si thin films. Silane (SiH4), silane radicals (SiH3, SiH2, SiH, Si), and Si nanoparticles have all been monitored inside chemical vapor deposition systems. Finally, we review various thin-film characterization methods, in which lasers are implemente

Laser applications in thin-film photovoltaics

We review laser applications in thin-film photovoltaics (thin-film Si, CdTe, and Cu(In,Ga)Se2 solar cells). Lasers are applied in this growing field to manufacture modules, to monitor Si deposition processes, and to characterize opto-electrical properties of thin films. Unlike traditional panels based on crystalline silicon wafers, the individual cells of a thin-film photovoltaic module can be serially interconnected by laser scribing during fabrication. Laser scribing applications are described in detail, while other laserbased fabrication processes, such as laser-induced crystallization and pulsed laser deposition, are briefly reviewed. Lasers are also integrated into various diagnostic tools to analyze the composition of chemical vapors during deposition of Si thin films. Silane (SiH4), silane radicals (SiH3, SiH2, SiH, Si), and Si nanoparticles have all been monitored inside chemical vapor deposition systems. Finally, we review various thin-film characterization methods, in which lasers are implemented

Structured matrices for predictive control of large and multi-dimensional systems

The extremely large telescopes that should see first light in coming years demand so-called adaptive optics systems to overcome the devastating effect of the atmospheric turbulence on the image quality. A sensor measures the incoming distortion of the light and is used for reshaping the latter using a deformable mirror. Processing the large number of sensor channels to operate the actuators at kilohertz frequencies is challenging on the computational point of view. The correction applied by the mirror and based on the sensor measurements should indeed not be already outdated because the turbulence has evolved during the computation time. In order to reduce the memory storage and the computational requirements, prior knowledge on the system is commonly translated into assumptions on the system matrices. When the sensors are regularly spread on a two-dimensional grid as is the case in adaptive optics, and the underlying function that describes the spatial dynamics is separable in its horizontal and vertical coordinates, a particular matrix representation is studied. This parametrization allows to write the matrices with a linear number of parameters (instead of quadratic without) and especially to derive more efficient algorithms for identifying from data the spatio-temporal dynamics of the turbulent atmosphere. This PhD thesis draws pros and cons of such a parametrization of large matrices for Linear Time Invariant systems, especially from an identification perspective. Besides, its close connection with tensors raises new fundamental questions in the analysis of such systems.Team Raf Van de Pla

Kronecker-ARX models in identifying (2D) spatial-temporal systems

In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation leads to high data compression. Estimating in least-squares sense the coefficient-matrices gives rise to a bilinear estimation problem, which is tackled using a three-stage algorithm. A numerical example demonstrates the advantages of the new modeling paradigm. It leads to comparable performances with the unstructured least-squares estimation of VAR models. However, the number of parameters in the new modeling paradigm grows linearly w.r.t. the number of nodes in the 2D sensor network instead of quadratically in the full unstructured matrix case.Team Raf Van de Pla

Subspace identification of 1D spatially-varying systems using Sequentially Semi-Separable matrices

We consider the problem of identifying 1D spatially-varying systems that exhibit no temporal dynamics. The spatial dynamics are modeled via a mixed-causal, anti-causal state space model. The methodology is developed for identifying the input-output map of e.g a 1D flexible beam described by the Euler-Bernoulli beam equation and equipped with a large number of actuators and sensors. It is shown that the static input-output map between the lifted inputs and outputs possess a so-called Sequentially Semi-Separable (SSS) matrix structure. This structure is of key importance to derive algorithms with linear computational complexity for controller synthesis of large-scale systems. A nuclear norm subspace identification method of the N2SID class is developed for estimating these state space models from input-output data. To enable the method to deal with a large number of repeated experiments a dedicated Alternating Direction Method of Multipliers (ADMM) algorithm is derived. It is shown in this paper that a nuclear norm relaxation on the SSS structure can be imposed which improves the estimates of the system matrices.Accepted Author ManuscriptTeam Raf Van de Pla

Recursive Kronecker-based Vector AutoRegressive identification for large-scale adaptive optics

This brief presents an algorithm for the recursive identification of Vector AutoRegressive (VAR) models of large dimensions. We consider a VAR model where the coefficient matrices can be written as a sum of Kronecker products. The algorithm proposed consists of recursively updating the Kronecker factor matrices at each new time step using alternating least squares. When the number of terms in the Kronecker sum is small, a significant reduction in computational complexity is achieved with respect to the recursive least squares algorithm on an unstructured VAR model. Numerical validation of nonstationary atmospheric turbulence data, both synthetic and experimental, is shown for an adaptive optics application. Significant improvements in accuracy over batch identification methods that assume stationarity are observed while both the computational complexity and the required storage are reduced.Team Raf Van de Pla

Kronecker-ARX models in identifying (2D) spatial-temporal systems

In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation leads to high data compression. Estimating in least-squares sense the coefficient-matrices gives rise to a bilinear estimation problem, which is tackled using a three-stage algorithm. A numerical example demonstrates the advantages of the new modeling paradigm. It leads to comparable performances with the unstructured least-squares estimation of VAR models. However, the number of parameters in the new modeling paradigm grows linearly w.r.t. the number of nodes in the 2D sensor network instead of quadratically in the full unstructured matrix case.</p

A system dynamics model for the management of the Manila clam, Ruditapes philippinarum (Adams & Reeve, 1850) in the Bay of Arcachon (France)

The Manila clam Ruditapes philippinarum (Adams and Reeve, 1850) is one of the mollusc species that, driven mainly by the shellfish market industry, has extended throughout the world, far beyond the limits of its original habitat. The Manila clam was introduced into France for aquaculture purposes, between 1972 and 1975. In France, this venerid culture became increasingly widespread and, since 1988, this species has colonised most of the embayments along the French Atlantic coast. In 2004, this development resulted in a fishery of ca. 520 t in Arcachon Bay