Spherical Aluminum Nitride Fillers for Heat-Conducting Plastic Packages

It is necessary for encapsulants to have not only a suitable coefficient of thermal expansion (CTE) compatible to IC devices and a low dielectric constant to reduce the device propagation delay, but also a high thermal conductivity to dissipate large amounts of heat from power-hungry, high-speed IC and high-density packages. Fillers such as silica have been mixed with polymers to improve their properties. Aluminum nitride (AlN) is considered as an alternative one, because it has a higher theoretical thermal conductivity of ∼320 W/mK¹, a compatible CTE with silicon chips and a low dielectric constant. Commercial AlN fillers are angular in shape, because they are prepared via grinding coarse AlN powders synthesized by direct nitridation of aluminum metal and classification. The angular AlN are not expected to have high fluidity when mixed with polymers and hence low packing density. Recently, we successfully obtained single-crystalline spherical AlN fillers. Furthermore, polymer composites filled with the spherical AlN showed excellent thermal conductivity (>8 W/mK) as encapsulants for dissipating the heat generated in electronic devices.     

Fourier-transform speckle profilometry: three-dimensional shape measurements of diffuse objects with large height steps and/or spatially isolated surfaces

An interferometric technique for automated profilometry of diffuse objects has been proposed. It is based on the Fourier-fringe analysis of spatiotemporal specklegrams produced by a wavelength-shift interferometer with a laser diode as a frequency-tunable light source. Unlike conventional moiré techniques the proposed technique permits the objects to have discontinuous height steps and/or surfaces spatially isolated from one another. Experimental results are presented that demonstrate the validity of the principle.     

A low wall-loading DEMO reactor design with high priority for early and reliable realization of a tokamak fusion reactor over the cost performance

Based on scientific databases adopted for designing ITER plasmas and on the advancement of fusion nuclear technology from the recent R&D program, a low wall-loading DEMO fusion reactor has been designed, where high priority has been given to the early and reliable realization of a tokamak fusion plasma over the cost performance. Since the major radius of this DEMO reactor is chosen to be 10 m, plasma ignition is achievable with a low fusion power of 0.8 GW and an operation period of 4–5 hours is available only with inductive current drive. The low ignition power makes it possible to adopt a first wall with an austenitic stainless steel, for which significant databases and operating experience exists, due to its use in the presence of neutron irradiation in fission reactors. In step with development of advanced materials, a step-wise increase of the fusion power seems to be feasible and realistic, because this DEMO reactor has the potential to produce a fusion power of 5 GW.     

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.     

Quantitative analysis of metabolic gases by multichannel Raman spectroscopy: use of a newly designed elliptic-spherical integration type of cell holder

We describe the quantitative analysis of some metabolic gases bymultichannel Raman spectroscopy. Raman spectra were measured forair, acetone, ammonia, carbon dioxide, and mixed gas consisting ofacetone, ammonia, and air. We designed a new elliptic-sphericalintegration type of cell holder to obtain the Raman spectra of gaseswith a high signal-to-noise ratio. Concentrations of acetone, ammonia, and carbon dioxide were determined by the peak intensities ofRaman bands at 2940, 3228, and 1385 cm(-1), respectively. To compensate for the fluctuations of Ramanintensities caused by several factors, such as the fluctuations oflaser power, the peak intensity of a band at 2324 cm(-1) dueto nitrogen gas was used as an internal intensity standard. Thecorrelation coefficient between the corrected Raman intensity at 2940cm(-1) and the concentration of acetone was calculated to be0.984 for a concentration range of 2-12 ppm. The detection limitof acetone gas was found to be 2 ppm.     

Prediction of starting performance of synchronous motor by DC decaytesting method with the rotor in any arbitrary position

This paper presents a novel method to predict the starting performance of synchronous motors. This proposed strategy is based on a simple standstill response testing method using a small-capacity DC power supply unit, tentatively named the DC decay testing method in this paper, and can be applied to standstill synchronous motors with the rotor in any arbitrary position. The proposed testing method is carried out on a 10 kW, 200 V, 31.9 A, 50 Hz four-pole laminated salient-pole synchronous motor with damper windings. The results measured by on-load test and those predicted by the proposed method on starting performance clearly show the validity of the proposed method     

Reorganizing knowledge in neural networks: an explanatory mechanismfor neural networks in data classification problems

We propose an explanatory mechanism for multilayered neural networks (NN). In spite of the effective learning capability as a uniform function approximator, the multilayered NN suffers from unreadability, i.e., it is difficult for the user to interpret or understand the "knowledge" that the NN has by looking at the connection weights and thresholds obtained by backpropagation (BP). This unreadability comes from the distributed nature of the knowledge representation in the NN. In this paper, we propose a method that reorganizes the distributed knowledge in the NN to extract approximate classification rules. Our rule extraction method is based on the analysis of the function that the NN has learned, rather than on the direct interpretation of connection weights as correlation information. More specifically, our method divides the input space into "monotonic regions" where a monotonic region is a set of input patterns that belongs to the same class with the same sensitivity pattern. Approximate classification rules are generated by projecting these monotonic regions.     

Research on machine recognition of handprinted characters

Machine recognition of handprinted Chinese characters has recently become very active in Japan. Both from the practical and the academic point of view, very encouraging results are reported. The work is described systematically and analyzed in terms of so-called feature matching, which is likely to be the mainstream of the research and development of machine recognition of handprinted Chinese characters. A database, ETL8 (881 Kanji, 71 hirakana, and 160 variations for each category), is explained, on which many experiments were performed. Recognition rates reported using this database can be compared, and so somewhat qualitative evaluation of these methods is described. Based on the comparative study, the merits and demerits of both feature and structural matching are discussed and some future directions are mentioned.