Guest editorial. [Computers and Artificial Intelligence. 1998. Vol. 17, No. 2-3]

Editorial

Effect of adding 0.3wt% Ni into the Sn-0.7wt% Cu solder - Part II: Growth of intermetallic layer with Cu during wetting and aging

The growth behavior of intermetallic layer with or without adding 0.3 wt% Ni into the Sn-0.7Cu solder was studied during the wetting reaction on Cu-substrate and thereafter in solid-state aging condition. The Cu-solder reaction couple was prepared at 255, 275 and 295 °C for 10 s. The samples reacted at 255 °C were then isothermally aged for 2-14 days at 150 °C. The reaction species formed for the Sn-0.7Cu/Cu and Sn-0.7Cu-0.3Ni/Cu soldering systems were Cu6Sn5 and (CuNi)6Sn5, respectively. The thickness of the intermetallic compounds formed at the solder/Cu interfaces and also in the bulk of both solders increased with the increase of reaction temperature. It was found that Ni-containing Sn-0.7Cu solder exhibited lower growth of intermetallic layer during wetting and in the early stage of aging and eventually exceeded the intermetallic layer thickness of Sn-0.7Cu/Cu soldering system after 6 days of aging. As the aging time proceeds, a non-uniform intermetallic layer growth tendency was observed for the case of Sn-0.7Cu-0.3Ni solder. The growth behavior of intermetallic layer during aging for both solders followed the diffusion-controlled mechanism. The intermetallic layer growth rate constants for Sn-0.7Cu and Sn-0.7Cu-0.3Ni solders were calculated as 1.41 × 10-17 and 1.89 × 10-17 m2/s, respectively which indicated that adding 0.3 wt% Ni with Sn-0.7Cu solder contributed to the higher growth of intermetallic layer during aging. © 2006 Elsevier B.V. All rights reserved.

Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Artificial neural networks in modelling osmotic dehydration of foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.

Effect of long-term aging on the rheological characteristics and printing performance of lead-free solder pastes used for flip-chip assembly

Stencil printing of solder pastes is a critical stage in the SMT assembly process as a high proportion of the solder-related defects can be attributed to this stage. As the trend towards product miniaturization continues, there is a greater need for better understanding of the rheological behaviour and printing performance of new paste formulations. This fundamental understanding is crucial for achieving the repeatable solder paste deposits from board-to-board and pad-to-pad required for more reliable solder interconnections. The paper concerns a study on the effect of ageing on the rheological characteristics and printing performance of new lead-free solder pastes formulations used for flip-chip assembly applications. The objective is to correlate the rheological characteristics of aged paste samples to their printing performance. The methodology developed can be used for bench-marking new lead-free paste formulations in terms of shelf life, the potential deterioration in rheological characteristics and their printing performance.

Recovery of a biodiversity action plans species in northwest England: possible role of climate change, artificial habitat and water quality amelioration. Occasional Publication of the Marine Biological Association 16

The honeycomb reef worm Sabellaria alveolata is recognised as being an important component of intertidal communities. It is a priority habitat within the UK Biodiversity Action Plan and as a biogenic reef forming species is covered by Annex 1 of the EC habitats directive. S. alveolata has a lusitanean (southern) distribution, being largely restricted to the south and west coasts of England. A broad-scale survey of S. alveolata distribution along the north-west coasts was undertaken in 2003/2004. These records were then compared with previous distribution records, mainly those collected by Cunningham in 1984. More detailed mapping was carried out at Hilbre Island at the mouth of the River Dee, due to recent reports that S. alveolata had become re-established there after a long absence.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.