Investigation of a-Si:H solar cells with a protocrystalline-like amorphous silicon intrinsic layer
We investigate the effects of the hydrogen dilution in a-Si:H film on the crystallization and the absorption property. Crystallization of the a-Si:H films from amorphous to several tens nanometer-sized crystal state is observed by increase of hydrogen dilution ratio using PECVD. The phase transition of the a-Si:H films from amorphous to micro-crystal occurred very rapidly in a very narrow range of the silane concentration(SC). Protocrystalline-like properties of silicon (pc-Si:H) is obtained fractionally within the a-Si:H matrix. It could be assumed that the volume fraction of the protocrystalline silicon is the highest in the range of SC=3.7∼4.0%, whereas amorphous or micro-crystalline phases are dominant in the other range of SC<4.0% and SC>3.0%, respectively. The μc-Si:H films with SC=1.0%~1.4% have the micro-crystalline volume fraction 75%∼80%. But the μc-Si:H films still contain the amorphous, proto- or nano-crystalline silicon in the rest volume so that they show some different characteristics with other micro-crystalline silicon. The a-Si:H films with SC=3.7∼4.0% doesn't contain the micro-crystalline volume fraction but different characteristics in absorption from the other a-Si:H films with higher SC>4.0%. Such films could be assumed to have the properties of the protocrystalline silicon. They, consequently, showed the low degradation property from the light soaking and the rapid stability.
Protocrystalline Silicon at High Rate from Undiluted Silane
Hot Wire Chemical Vapor Deposition (HWCVD) is shown to be a fast method for the deposition of protocrystalline silicon films from undiluted silane. Intrinsic silicon-hydrogen films (2 µm thick) have been deposited by HWCVD on plain stainless steel as well as on stainless steel precoated with a n-type doped microcrystalline silicon layer. In X-ray diffraction experiments, the linewidths of the first sharp peak (FSP) were 5.59 ± 0.09 degrees and 5.29 ± 0.11 degrees, respectively, indicating improved medium-range order and a template effect due to the µc-Si:H n-layer. For thinner layers (0.7 µm thick), the linewidths of the FSP were 5.29 ± 0.09 degrees and 5.10 ± 0.09 degrees. These FSPs are as narrow as for optimized i-layers made by H2-diluted plasma deposition, however, at a much higher deposition rate (1 nm/s), at moderate temperature (250 °C), and without the use of H 2 dilution. In accompanying transmission electron micrographs, the layers show a significant concentration of elongated small voids in the growth direction that are not interconnected. Small Angle X-ray Scattering (SAXS) results are consistent with these observations. We suspect that the void nature allows the bulk of the film to be more ordered. The utilization of such layers in n-i-p solar cells on plain stainless steel leads to cells with a remarkably good stability, showing a decrease of the fill factor of less than 10 % during 1500 h of light soaking.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.