The role of silicon hydride species in the photoluminescence intensity behavior of porous Si has been studied. The surfaces of luminescent porous Si samples were converted to a predominate SiH termination using a remote H plasma. The as-passivated samples were then immersed in various concentrations of hydrofluouric solutions to regulate the recovery of SiH2 termination on the surface. Photoluminescence measurements and transmission Fourier-transform infrared spectroscopy have shown that predominant silicon monohydride (SiH) termination results in weak photoluminescence. In contrast, it has been observed that the appearance of silicon dihydride (SiH2) coincides with an increase in the photoluminescence intensity.

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The photoluminescence (PL) of porous Si that has been hydrogenated in boiling water has been investigated. The PL intensity is observed to increase with a concurrent shift of the spectral peak to shorter wavelengths. These effects can be explained in terms of size effects in the microstructures of porous Si. The spectral changes after annealing and after rehydrogenation are similar to the behavior of a-Si:H. We conclude that hydrogen plays an important role in the luminescence of porous Si.

Chinese dark chess is a popular and easy-to-learn game in Asia. The characteristic of possible revealing of unknown pieces makes it different from Chinese chess or Western chess. Players with luck may win a game by chance. Thus, there is a probabilistic behavior that a player has to consider. Computer Chinese dark chess problems can be divided into three phases: 1) the opening game, 2) the middle game, and 3) the endgame. Revealing pieces reasonably and effectively is the main issue in the opening game. In the middle game, the choice of revealing pieces or moving pieces becomes the critical issue. Designing a good evaluation function is also important both in the middle game and endgame. In an advantageous endgame, how to capture all opponent's pieces to win the game is also a hard problem. Search-based methods, such as αβ pruning, are only suitable in the positions where revealing actions do not influence the results. However, according to our experiments, programs that reveal pieces reasonably and effectively have better playing strength. In this paper, we introduce the game Chinese dark chess and give some research topics about this game. We also discuss some strategies for considering the revealing actions in this paper.

Low-temperature silicon epitaxy is critical for future generation ultralarge scale integrated circuits and silicon-based heterostructures. Remote plasma-enhanced chemical vapor deposition has been applied to achieve silicon homoepitaxy at temperatures as low as 150 °C, which is believed to be the lowest temperature reported to date. Critical to the process are an in situ remote plasma hydrogen cleaning of the substrate surface in an ultrahigh vacuum growth chamber prior to epitaxy, and substitution of thermal energy by remote plasma excitation via argon metastables and energetic electrons to dissociate silane and increase adatom mobility on the surface of the silicon substrate. Excellent crystallinity with very few defects such as dislocations and stacking faults is observed.

In this paper, we describe our implementation of several parallel graph algorithms for nding connected components. Our implementation, with virtual processing, is on a 16,384-processor MasPar MP-1 using the language MPL. We present extensive test data on our code. In our previous projects 21, 22, 23 , we reported the implementation of an extensible parallel graph algorithms library. W e developed general implementation and ne-tuning techniques without expending too much e ort on optimizing each individual routine. We also handled the issue of implementing virtual processing. In this paper, we describe several algorithms and ne-tuning techniques that we developed for the problem of nding connected components i n parallel; many of the ne-tuning techniques are of general interest, and should be applicable to code for other problems. We present data on the execution time and memory usage of our various implementations.