Application of the SEM to the measurement of solar cell parameters

Techniques are described which make use of the SEM to measure the minority carrier diffusion length and the metallurgical junction depth in silicon solar cells. The former technique permits the measurement of the true bulk diffusion length through the application of highly doped field layers to the back surfaces of the cells being investigated. It is shown that the secondary emission contrast observed in the SEM on a reverse-biased diode can depict the location of the metallurgical junction if the diode has been prepared with the proper beveled geometry. The SEM provides the required contrast and the option of high magnification, permitting the measurement of extremely shallow junction depths

Spatial Smoothing for Diffusion Tensor Imaging with low Signal to Noise Ratios

Though low signal to noise ratio (SNR) experiments in DTI give key information about tracking and anisotropy, e.g. by measurements with very small voxel sizes, due to the complicated impact of thermal noise such experiments are up to now seldom analysed. In this paper Monte Carlo simulations are presented which investigate the random fields of noise for different DTI variables in low SNR situations. Based on this study a strategy for spatial smoothing, which demands essentially uniform noise, is derived. To construct a convenient filter the weights of the nonlinear Aurich chain are adapted to DTI. This edge preserving three dimensional filter is then validated in different variants via a quasi realistic model and is applied to very new data with isotropic voxels of the size 1x1x1 mm3 which correspond to a spatial mean SNR of approximately 3

Development and pilot line production of lithium doped silicon solar cells

Scaling up the BCl3 without O2 diffusion beyond 30 to 40 cells was investigated by using a 100 cell capacity diffusion boat which held the cells vertically. Sheet resistances and I-V curves were uniform with 10 to 20 cells spaced along the entire boat, so the quantity was increased to 40 and then 60 cells per diffusion. There was no change in cell output and uniformity going from 20 to 40 cells per diffusion; however only half the lithium cells fabricated from slices diffused in the 60 cell diffusion had efficiencies of 11% or better. Although uniform sheet resistances and I-V characteristic curves were obtained with up to 60 cells in the BCl3 with O2 diffusion, the short circuit currents were approximately 15% lower than the anticipated 135 to 140 mA. Consequently, work on this diffusion process has been aimed solely at increasing the short circuit current. The diffusion temperature was lowered from 1055 to 1000 and 950 C, and at each of these temperatures variations in diffusion time were investigated. At 1000 C short circuit currents were approximately 10 mA higher, 130 rather than 120 mA average

Joint Modelling of Gas and Electricity spot prices

The recent liberalization of the electricity and gas markets has resulted in the growth of energy exchanges and modelling problems. In this paper, we modelize jointly gas and electricity spot prices using a mean-reverting model which fits the correlations structures for the two commodities. The dynamics are based on Ornstein processes with parameterized diffusion coefficients. Moreover, using the empirical distributions of the spot prices, we derive a class of such parameterized diffusions which captures the most salient statistical properties: stationarity, spikes and heavy-tailed distributions. The associated calibration procedure is based on standard and efficient statistical tools. We calibrate the model on French market for electricity and on UK market for gas, and then simulate some trajectories which reproduce well the observed prices behavior. Finally, we illustrate the importance of the correlation structure and of the presence of spikes by measuring the risk on a power plant portfolio

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, remain the gold standard for approximate Bayesian inference because they have a robust finite-sample theory and reliable convergence diagnostics. However, alternative methods, which are more scalable or apply to problems where Markov Chain Monte Carlo cannot be used, lack the same finite-data approximation theory and tools for evaluating their accuracy. In this work, we develop a flexible new approach to bounding the error of mean and uncertainty estimates of scalable inference algorithms. Our strategy is to control the estimation errors in terms of Wasserstein distance, then bound the Wasserstein distance via a generalized notion of Fisher distance. Unlike computing the Wasserstein distance, which requires access to the normalized posterior distribution, the Fisher distance is tractable to compute because it requires access only to the gradient of the log posterior density. We demonstrate the usefulness of our Fisher distance approach by deriving bounds on the Wasserstein error of the Laplace approximation and Hilbert coresets. We anticipate that our approach will be applicable to many other approximate inference methods such as the integrated Laplace approximation, variational inference, and approximate Bayesian computationComment: 22 pages, 2 figure

A handheld high-sensitivity micro-NMR CMOS platform with B-field stabilization for multi-type biological/chemical assays

We report a micro-nuclear magnetic resonance (NMR) system compatible with multi-type biological/chemical lab-on-a-chip assays. Unified in a handheld scale (dimension: 14 x 6 x 11 cm³, weight: 1.4 kg), the system is capable to detect<100 pM of Enterococcus faecalis derived DNA from a 2.5 μL sample. The key components are a portable magnet (0.46 T, 1.25 kg) for nucleus magnetization, a system PCB for I/O interface, an FPGA for system control, a current driver for trimming the magnetic (B) field, and a silicon chip fabricated in 0.18 μm CMOS. The latter, integrated with a current-mode vertical Hall sensor and a low-noise readout circuit, facilitates closed-loop B-field stabilization (2 mT → 0.15 mT), which otherwise fluctuates with temperature or sample displacement. Together with a dynamic-B-field transceiver with a planar coil for micro-NMR assay and thermal control, the system demonstrates: 1) selective biological target pinpointing; 2) protein state analysis; and 3) solvent-polymer dynamics, suitable for healthcare, food and colloidal applications, respectively. Compared to a commercial NMR-assay product (Bruker mq-20), this platform greatly reduces the sample consumption (120x), hardware volume (175x), and weight (96x)

Development of lead salt semiconductor lasers for the 9-17 micron spectral region

Improved diode lasers of Pb sub 1-x Sn sub x Se operating in the 9-17 micrometers spectral region were developed. The performance characteristics of the best lasers exceeded the contract goals of 500 microW/mode at T 30K in the 9-12 micrometers region and 200 microW/mode at T 18K in the 16-17 micrometers region. Increased reliability and device yields resulted from processing improvements which evolved from a series of diagnostic studies. By means of Auger electron spectroscopy, laser shelf storage degradation was shown to be characterized by the presence of In metal on the semiconductor crystal surfaces. Studies of various metal barrier layers between the crystals and the In metal led to the development of an improved metallurgical contacting technology which has resulted in devices with performance stability values exceeding the contract goal of a one year shelf life. Lasers cycled over 500 times between 300K and 77K were also shown to be stable. Studies on improved methods of fabricating striped geometry lasers indicated that good spectral mode characteristics resulted from lasers which stripe widths of 12 and 25 micrometers

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1