RESUMEN
The optical and electrical properties of Si rich SiC (SRSC) solar cell absorber layers will strongly depend on interfacial layers between the Si and the SiC matrix and in this work, we analyze hitherto undiscovered interfacial layers. The SRSC thin films were deposited using a plasma-enhanced chemical vapor deposition (PECVD) technique and annealed in a nitrogen environment at 1100 °C. The thermal treatment leads to metastable SRSC films spinodally decomposed into a Si-SiC nanocomposite. After the thermal treatment, the coexistence of crystalline Si and SiC nanostructures was analysed by high resolution transmission electron microscopy (HRTEM) and electron diffraction. From the quantitative extraction of the different plasmon signals from electron energy-loss spectra, an additional structure, amorphous SiC (a-SiC) was found. Quantitative spectroscopic electron tomography was developed to obtain three dimensional (3D) plasmonic maps. In these 3D spectroscopic maps, the Si regions appear as network structures inside the SiC matrix where the a-SiC appears as an interfacial layer separating the matrix and Si network. The presence of the a-SiC interface can be explained in the framework of the nucleation and growth model.
RESUMEN
We demonstrate an analytical method to optimize the stoichiometry and thickness of multilayer silicon oxide films in order to achieve the highest density of non-touching and closely spaced silicon nanocrystals after annealing. The probability of a nanocrystal nearest-neighbor distance within a limited range is calculated using the stoichiometry of the as-deposited film and the crystallinity of the annealed film as input parameters. Multiplying this probability with the nanocrystal density results in the density of non-touching and closely spaced silicon nanocrystals. This method can be used to estimate the best as-deposited stoichiometry in order to achieve optimal nanocrystal density and spacing after a subsequent annealing step.
RESUMEN
Optical and electrical properties of hydrogenated nanocrystalline silicon (nc-Si:H) solar cells are strongly influenced by the morphology of underlying substrates. By texturing the substrates, the photogenerated current of nc-Si:H solar cells can increase due to enhanced light scattering. These textured substrates are, however, often incompatible with defect-less nc-Si:H growth resulting in lower Voc and FF. In this study we investigate the correlation between the substrate morphology, the nc-Si:H solar-cell performance, and the defect density in the intrinsic layer of the solar cells (i-nc-Si:H). Statistical surface parameters representing the substrate morphology do not show a strong correlation with the solar-cell parameters. Thus, we first quantify the line density of potentially defective valleys of randomly textured ZnO substrates where the opening angle is smaller than 130° (ρ<130). This ρ<130 is subsequently compared with the solar-cell performance and the defect density of i-nc-Si:H (ρdefect), which is obtained by fitting external photovoltaic parameters from experimental results and simulations. We confirm that when ρ<130 increases the Voc and FF significantly drops. It is also observed that ρdefect increases following a power law dependence of ρ<130. This result is attributed to more frequently formed defective regions for substrates having higher ρ<130.
RESUMEN
We propose a method, with minimal bias caused by user input, to quickly detect and measure the nanocrystal size distribution from transmission electron microscopy (TEM) images using a combination of Laplacian of Gaussian filters and non-maximum suppression. We demonstrate the proposed method on bright-field TEM images of an a-SiC:H sample containing embedded silicon nanocrystals with varying magnifications and we compare the accuracy and speed with size distributions obtained by manual measurements, a thresholding method and PEBBLES. Finally, we analytically consider the error induced by slicing nanocrystals during TEM sample preparation on the measured nanocrystal size distribution and formulate an equation to correct this effect.
RESUMEN
Thin-film silicon solar cells are often deposited on textured ZnO substrates. The solar-cell performance is strongly correlated to the substrate morphology, as this morphology determines light scattering, defective-region formation, and crystalline growth of hydrogenated nanocrystalline silicon (nc-Si:H). Our objective is to gain deeper insight in these correlations using the slope distribution, rms roughness (σ(rms)) and correlation length (lc) of textured substrates. A wide range of surface morphologies was obtained by Ar plasma treatment and wet etching of textured and flat-as-deposited ZnO substrates. The σ(rms), lc and slope distribution were deduced from AFM scans. Especially, the slope distribution of substrates was represented in an efficient way that light scattering and film growth direction can be more directly estimated at the same time. We observed that besides a high σ(rms), a high slope angle is beneficial to obtain high haze and scattering of light at larger angles, resulting in higher short-circuit current density of nc-Si:H solar cells. However, a high slope angle can also promote the creation of defective regions in nc-Si:H films grown on the substrate. It is also found that the crystalline fraction of nc-Si:H solar cells has a stronger correlation with the slope distributions than with σ(rms) of substrates. In this study, we successfully correlate all these observations with the solar-cell performance by using the slope distribution of substrates.
RESUMEN
Thin-film silicon solar cells (TFSSC), which can be manufactured from abundant materials solely, contain nano-textured interfaces that scatter the incident light. We present an approximate very fast algorithm that allows optimizing the surface morphology of two-dimensional nano-textured interfaces. Optimized nano-textures scatter the light incident on the solar cell stronger leading to a higher short-circuit current density and thus efficiency. Our algorithm combines a recently developed scattering model based on the scalar scattering theory, the Perlin-noise algorithm to generate the nano textures and the simulated annealing algorithm as optimization tool. The results presented in this letter allow to push the efficiency of TFSSC towards their theoretical limit.
