LIGA fabrication of nanocrystalline Ni–W alloy micro specimens from ammonia-citrate bath

Ammonia-citrate bath has been investigated for the deposition of nano crystalline Ni–W alloy micro components using the LIGA process. First the bath stability and deposit characteristics were studied. Fabrication of micro specimens were then carried out on silicon substrates covered with novolac as well as thick PMMA resist for LIGA. Effects of different parameters like current density, nickel ion and tungsten ion concentration in the bath, deposition time etc. on the deposit characteristics and current efficiency were studied. The deposited Ni–W samples were characterized by scanning electron microscopy, energy depressive X-ray spectroscopy, light optical microscopy and X-ray diffraction. Results show that during a few tens of hours of deposition, ammonia loss from the covered bath used is minimal and the bath remains stable. Selection of proper bath and deposition parameters allows a window for the deposition of crack free, thick, nano crystalline nickel–tungsten alloys. Using the optimum parameters, it has been possible to fabricate Ni-12 at% W micro tensile specimens with a nominal thickness of 120 μm by the LIGA process.     

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni–P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni–P coatings. The temperature and molar rate of the bath were constant ( 91^° \textC, 0.4 \textNi^{\text + +} /\textH₂ \textPO₂ ^{- -} 91^\circ {\text{C}},\:0.4\,{\text{Ni}}^{{{\text{ + + }}}} /{\text{H}}_{2} {\text{PO}}_{2}^{{ - - }} ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network’s predictions and own experimental data are given.     

Effect of pulse frequency on the morphology and nanoindentation property of electroplated nickel films

This paper reports the effect of pulse current with different frequencies on the morphology and mechanical properties of nickel (Ni) films deposited by electroplating. The pulse frequency varies from 0 (DC) to 500 Hz while the duty cycle (T _on/T _off) is 1 during electroplating. The average roughness and deposition rate of Ni films decrease with the increasing frequency. The smoothest Ni surface with average roughness of 16.5 nm is achieved at the frequency of 500 Hz while the deposition rate reaches a nearly stable rate of 0.04 μm/min. The surface concentration of ions does not vary with time at a sufficiently high frequency with a long off time for ion to diffuse onto the surface. It will result in much more nuclei formed on the surface of cathode at a limited growth rate to get small grains for smooth morphology. The nanohardness of Ni film initially decreases rapidly from 3.9 to 1.18 GPa at 0 to 10 Hz, respectively, then increases to about 4.87 GPa in the range of 100–200 Hz, and decreases slowly to 4.0 GPa at 500 Hz. The stiffness of Ni films electroplated by pulse current at 100–200 Hz is higher than that by dc electroplating. The compromised Ni film with smooth morphology, good hardness and proper deposition rate is obtained at frequency of 100–200 Hz under the current density of 3 Adm⁻².     

Fabrication of a guide block for measuring a device with fine pitch area-arrayed solder bumps

This paper describes the design and fabrication of a guide block and micro probes, which were used for a vertical probe card to test a chip with area-arrayed solder bumps. The size of the fabricated guide block was 10 mm × 6 mm. The guide block consisted of 172 holes to insert micro probes, 2 guide holes for exact alignment, and 4 holes for bolting between the guide block and the housing of a PCB. Pitch and size of the inserting holes were 80 μm, and 90 μm × 30 μm, respectively. A silicon on insulator wafer was used as the substrate of the guide block to reduce micro probes insertion error. The micro probes were made of nickel–cobalt (Ni–Co) alloy using an electroplating method. The length and thickness of the micro probes were 910 and 20 μm, respectively. A vertical probe card assembled with the fabricated guide block and micro probes showed good x–y alignment and planarity errors within ±4 and ±3 μm, respectively. In addition, average leakage current and contact resistance were approximately 0.35 nA and 0.378 ohm, respectively. The proposed guide block and micro probes can be applied to a vertical probe card to test a chip with area-arrayed solder bumps.     

An ab initio study of the magnetic and electronic properties of Fe, Co, and Ni nanowires on Cu(001) surface

Magnetism at the nanoscale has been a very active research area in the past decades, because of its novel fundamental physics and exciting potential applications. We have recently performed an ab initio study of the structural, electronic and magnetic properties of all 3d transition metal (TM) free-standing atomic chains and found that Fe and Ni nanowires have a giant magnetic anisotropy energy (MAE), indicating that these nanowires would have applications in high density magnetic data storages. In this paper, we perform density functional calculations for the Fe, Co and Ni linear atomic chains on Cu(001) surface within the generalized gradient approximation, in order to investigate how the substrates would affect the magnetic properties of the nanowires. We find that Fe, Co and Ni linear chains on Cu(001) surface still have a stable or metastable ferromagnetic state. When spin–orbit coupling (SOC) is included, the spin magnetic moments remain almost unchanged, due to the weakness of SOC in 3d TM chains, whilst significant orbital magnetic moments appear and also are direction-dependent. Finally, we find that the MAE for Fe, and Co remains large, i.e., being not much affected by the presence of Cu substrate.     

Fabrication of the monolithic polymer–metal microstructure by the backside exposure and electroforming technology

Monolithic polymer–metal microstructures can be fabricated on the silicon or glass substrate using two kinds of photoresists and electroforming technologies for the inkjet and microfluidic application. However, it suffers from the high shrinkage problem of first SU8 resist after exposure and post exposure baking. This paper reports a novel approach to solve the shrinkage problem by introducing backside exposure of first SU8 resist for the fabrication of the monolithic polymer–metal microstructure. In combination with the light absorption layer coating on the unexposed SU8 resist, metal seed layer deposition, frontside exposure for second JSR resist on the seed layer and the nickel (Ni) electroforming together with release process, we have demonstrated a high physical resolution of 1,200 dpi monolithic Ni nozzle plate with negligible shrinkage. It also has the advantages of low cost and high resolution for the improvement of the traditional bonding of polymer and metal nozzle plate, which is generally in need of a complex alignment to stick the metal nozzle plate and dry film polymer on the heating chip together.     

A thermodynamic assessment of Ni–Sb system

The Ni–Sb system was critically assessed by means of the CALculation of PHAse Diagram (CALPHAD) technique. The solution phases, Liq and (αNi), were modelled as the substitutional solutions with the Redlich–Kister equation. The intermediate phases, (γNiSb) and (βNi₃Sb), with homogeneity ranges were described respectively using three-sublattices (Sb)_1/3(Ni%,V a)_1/3(V a%,Ni)_1/3 and (Sb)_1/4(Ni%,V a)_1/2(Ni%,V a)_1/4 based on their structure features. Corresponding to the phase (βNi₃Sb), the two low-temperature phases of (δNi₃Sb) and (θNi₅Sb₂) with narrow homogeneity ranges were modelled as two-sublattice, (Ni)_3/4(Sb,Ni)_1/4 and (Ni)_5/7(Sb,Ni)_2/7. The intermetallic compound ζNiSb₂ with no homogeneity ranges was treated as stoichiometric compound. The phase ε$\epsilon$Sb was considered as pure Sb for the solubility of Ni in ε$\epsilon$Sb is very low. A set of self-consistent thermodynamic parameters of the Ni–Sb system was obtained. The optimized phase diagram and thermodynamic properties were presented and compared with experimental data.     

A framework for exploring numerical solutions of advection–reaction–diffusion equations using a GPU-based approach

In this paper we describe a general purpose, graphics processing unit (GP-GPU)-based approach for solving partial differential equations (PDEs) within advection–reaction–diffusion models. The GP-GPU-based approach provides a platform for solving PDEs in parallel and can thus significantly reduce solution times over traditional CPU implementations. This allows for a more efficient exploration of various advection–reaction–diffusion models, as well as, the parameters that govern them. Although the GPU does impose limitations on the size and accuracy of computations, the PDEs describing the advection–reaction–diffusion models of interest to us fit comfortably within these constraints. Furthermore, the GPU technology continues to rapidly increase in speed, memory, and precision, thus applying these techniques to larger systems should be possible in the future. We chose to solve the PDEs using two numerical approaches: for the diffusion, a first-order explicit forward Euler solution and a semi-implicit second order Crank–Nicholson solution; and, for the advection and reaction, a first-order explicit solution. The goal of this work is to provide motivation and guidance to the application scientist interested in exploring the use of the GP-GPU computational framework in the course of their research. In this paper, we present a rigorous comparison of our GPU-based advection–reaction–diffusion code model with a CPU-based analog, finding that the GPU model out-performs the CPU implementation in one-to-one comparisons.     

Micromechanical characterization of electroplated permalloy films for MEMS

In order to improve the reliability of MEMS designs, evaluating the mechanical properties of soft magnetic materials is needed. In this paper, we present a tensile testing method to characterize the mechanical properties of microscale electroplated permalloy (80 wt% Ni, 20 wt% Fe) films. The gauge section of the specimen is 50 μm wide, 100 μm long and 5 μm thick. The measured Young’s modulus of permalloy films is 96.4 GPa, and the tensile strength is 1.61 GPa. The fracture strain measured by the images of specimens is about 2%.     

Straight-through microchannel devices for generating monodisperse emulsion droplets several microns in size

The authors recently proposed a promising technique for producing monodisperse emulsions using a straight-through microchannel (MC) device composed of an array of microfabricated oblong holes. This research developed new straight-through MC devices with tens of thousands of oblong channels of several microns in size on a silicon-on-insulator plate, and investigated the emulsification characteristics using the microfabricated straight-through MC devices. Monodisperse oil-in-water (O/W) and W/O emulsions with average droplet diameters of 4.4–9.8 μm and coefficients of variation of less than 6% were stably produced using surface-treated straight-through MC devices that included uniformly sized oblong channels with equivalent diameters of 1.7–5.4 μm. The droplet size of the resultant emulsions depended greatly on the size of the preceding oblong channels. The emulsification process using the straight-through MC devices developed in this research had very high apparent energy efficiencies of 47–60%, defined as (actual energy input applied to droplet generation/theoretical minimum energy input necessary for making droplets) × 100. Straight-through MC devices with numerous oblong microfluidic channels also have great potential for increasing the productivity of monodisperse fine emulsions.     

Development and fabrication of magnetic thin films

For designing and fabricating electromagnetic microactuators as pursued within the collaborate research center “Design and Fabrication of Active Microsystems” (Sonderforschungsbereich 516), soft and hard magnetic materials are required to create and guide magnetic flux. The investigations on the development of suitable materials and their deposition technologies are presented in this paper. In the area of soft magnetic materials, the application of Cobalt–Iron (CoFe) as an alternative to Nickel–Iron (NiFe, Permalloy) was investigated. The benefit of CoFe over NiFe is its greater saturation flux density. The technology utilized for the deposition was electroplating. In the area of hard magnetic materials, gas flow sputtering was applied for depositing Samarium–Cobalt (SmCo). This technology enables the deposition of pure SmCo layers at high deposition rates and without high vacuum. Furthermore, the dependence of the magnetic properties of the SmCo on the film composition was examined.     

On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows

In a recent paper Boykov et al. (LNCS, Vol. 3953, pp. 409–422, 2006) propose an approach for computing curve and surface evolution using a variational approach and the geo-cuts method of Boykov and Kolmogorov (International conference on computer vision, pp. 26–33, 2003). We recall in this paper how this is related to well-known approaches for mean curvature motion, introduced by Almgren et al. (SIAM Journal on Control and Optimization 31(2):387–438, 1993) and Luckhaus and Sturzenhecker (Calculus of Variations and Partial Differential Equations 3(2):253–271, 1995), and show how the corresponding problems can be solved with sub-pixel accuracy using Parametric Maximum Flow techniques. This provides interesting algorithms for computing crystalline curvature motion, possibly with a forcing term. A. Chambolle’s research supported by ANR project “MICA”, grant ANR-08-BLAN-0082. J. Darbon’s research supported by ONR grant N000140710810.     

Tracking the activity of participants in a meeting

A vision system suitable for a smart meeting room able to analyse the activities of its occupants is described. Multiple people were tracked using a particle filter in which samples were iteratively re-weighted using an approximate likelihood in each frame. Trackers were automatically initialised and constrained using simple contextual knowledge of the room layout. Person–person occlusion was handled using multiple cameras. The method was evaluated on video sequences of a six person meeting. The tracker was demonstrated to outperform standard sampling importance re-sampling. All meeting participants were successfully tracked and their actions were recognised throughout the meeting scenarios tested.H. Nait Charif was funded by UK EPSRC Grant GR/R27419/01. Hammadi Nait Charif was born in Tinghir, Ouarzazat, Morocco on 25 December 1965. He received his Master of Engineering (Ingenieur d'Etat Diploma) in electrical engineering in 1990 and after a short-term job with the Ministry of Telecommunication, was appointed lecturer at Mohamed I University in 1991. He was a Monbusho visiting research fellow at Chiba University, Japan (1994–1995) where he received his PhD in 1998. He was an Assistant Professor and then an Associate Professor in electrical engineering at Mohamed I University (1998–2001). In 1999, he was a Fulbright Visiting Assistant Professor at Michigan State University. At the University of Dundee he has worked on the EPSRC project “Advanced Sensors for Supportive Environments for Elderly”. His research interests include image processing, computer vision and neural networks. Stephen McKenna is a Senior Lecturer at the University of Dundee. He graduated BSc (Hons) in Computer Science from the University of Edinburgh and PhD from the University of Dundee (1994). He has held post-doctoral research positions at Queen Mary, University of London and Tecnopolis Csata, Italy and has been a visiting researcher at BT Labs and George Mason University. Funders of his research include EPSRC, BBSRC and MRC. He has served on international program committees and is an Associate Editor of the journal Machine Vision and Applications. He co-authored the book “Dynamic Vision” and has published 75 articles on computer vision and pattern recognition. His research interests include the application of computer vision, imaging and machine learning to intelligent human–computer interaction, monitoring, surveillance, medicine and biology.     

A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project,Hai Phong city (Vietnam)

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.     

A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO

This research focuses to propose a new hybrid approach which combined the recurrent fuzzy neural network (RFNN) with particle swarm optimization (PSO) algorithm to simulate the flyrock distance induced by mine blasting. Here, this combination is abbreviated using RFNN–PSO. To evaluate the acceptability of RFNN–PSO model, adaptive neuro-fuzzy inference system (ANFIS) and non-linear regression models were also used. To achieve the objective of this research, 72 sets of data were collected from Shur river dam region, in Iran. Maximum charge per delay, stemming, burden, and spacing were considered as input parameters in the models. Then, the performance of the RFNN–PSO model was evaluated against ANFIS and non-linear regression models. Correlation coefficient (R2), Nash and Sutcliffe (NS), mean absolute bias error (MABE), and root-mean-squared error (RMSE) were used as comparing statistical indicators for the assessment of the developed approach’s performance. Results show a satisfactory achievement between the actual and predicted flyrcok values by RFNN–PSO with R2, NS, MABE, and RMSE being 0.933, 0.921, 13.86, and 15.79, respectively.     

Embodied Communication in Humans and Machines – A Research Agenda

The challenge to develop an integrated perspective of embodiment in communication has been taken up by an international research group hosted by Bielefeld University’s Center for Interdisciplinary Research (ZiF) from October, 2005 through September, 2006. An international conference was held there on 12–15 January, 2005 to define a research agenda that will explicitly address Embodied Communication in Humans and Machines.     

A Bradley–Terry artificial neural network model for individual ratings in group competitions

A common statistical model for paired comparisons is the Bradley–Terry model. This research re-parameterizes the Bradley–Terry model as a single-layer artificial neural network (ANN) and shows how it can be fitted using the delta rule. The ANN model is appealing because it makes using and extending the Bradley–Terry model accessible to a broader community. It also leads to natural incremental and iterative updating methods. Several extensions are presented that allow the ANN model to learn to predict the outcome of complex, uneven two-team group competitions by rating individuals—no other published model currently does this. An incremental-learning Bradley–Terry ANN yields a probability estimate within less than 5% of the actual value training over 3,379 multi-player online matches of a popular team- and objective-based first-person shooter.