Method of mitigating titanium impurities effects in p-type silicon material for solar cells

Microstructural evaluation tests performed on Cu-doped, Ti-doped and Cu/Ti doped p-type silicon single crystal wafers, before and after the solar cell fabrication, and evaluation of both dark forward and reverse I-V characteristic records for the solar cells produced from the corresponding silicon wafers, show that Cu mitigates the unfavorable effects of Ti, and thus provides for higher conversion efficiency, thereby providing an economical way to reduce the deleterious effects of titanium, one of the impurities present in metallurgical grade silicon material

Characterization of deliberately nickel-doped silicon wafers and solar cells

Microstructural and electrical evaluation tests were performed on nickel-doped p-type silicon wafers before and after solar cell fabrication. The concentration levels of nickel in silicon were 5 x 10 to the 14th power, 4 x 10 to the 15th power, and 8 x 10 to the 15th power atoms/cu cm. It was found that nickel precipitated out during the growth process in all three ingots. Clumps of precipitates, some of which exhibited star shape, were present at different depths. If the clumps are distributed at depths approximately 20 micron apart and if they are larger than 10 micron in diameter, degradation occurs in solar cell electrical properties and cell conversion efficiency. The larger the size of the precipitate clump, the greater the degradation in solar cell efficiency. A large grain boundary around the cell effective area acted as a gettering center for the precipitates and impurities and caused improvement in solar cell efficiency. Details of the evaluation test results are given

A generalized modal shock spectra method for spacecraft loads analysis

Unlike an earlier shock spectra approach, generalization permits an accurate elastic interaction between the spacecraft and launch vehicle to obtain accurate bounds on the spacecraft response and structural loads. In addition, the modal response from a previous launch vehicle transient analysis with or without a dummy spacecraft - is exploited to define a modal impulse as a simple idealization of the actual forcing function. The idealized modal forcing function is then used to derive explicit expressions for an estimate of the bound on the spacecraft structural response and forces. Greater accuracy is achieved with the present method over the earlier shock spectra, while saving much computational effort over the transient analysis

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test

Learning Multi-Tree Classification Models with Ant Colony Optimization

Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M

On the thermoelastic analysis of solar cell arrays and related material properties

Accurate prediction of failure of solar cell arrays requires accuracy in the computation of thermally induced stresses. This was accomplished by using the finite element technique. Improved procedures for stress calculation were introduced together with failure criteria capable of describing a wide range of ductile and brittle material behavior. The stress distribution and associated failure mechanisms in the N-interconnect junction of two solar cell designs were then studied. In such stress and failure analysis, it is essential to know the thermomechanical properties of the materials involved. Measurements were made of properties of materials suitable for the design of lightweight arrays: microsheet-0211 glass material for the solar cell filter, and Kapton-H, Kapton F, Teflon, Tedlar, and Mica Ply PG-402 for lightweight substrates. The temperature-dependence of the thermal coefficient of expansion for these materials was determined together with other properties such as the elastic moduli, Poisson's ratio, and the stress-strain behavior up to failure

Thermoelastic analysis of solar cell arrays and their material properties

Announced report discusses experimental test program in which five different solar cell array designs were evaluated by subjecting them to 60 thermal cycles from minus 190 deg to 0.0 deg. Results indicate that solder-coated cells combined with Kovar n-interconnectors and p-interconnectors are more durable under thermal loading than other configurations

On the elastic approximation to the vacancy formation energy in metals

Isotropic elastic continuum model application to calculate energy and entropy of vacancy formation in metal crystal