Impact of PECVD μc-Si:H deposition on tunnel oxide for passivating contacts

Passivating contacts are becoming a mainstream option in current photovoltaic industry due to their ability to provide an outstanding surface passivation along with a good conductivity for carrier collection. However, their integration usually requires long annealing steps which are not desirable in industry. In this work we study PECVD as a way to carry out all deposition steps: silicon oxide (SiOx), doped polycrystalline silicon (poly-Si) and silicon nitride (SiNx:H), followed by a single firing step. Blistering of the poly-Si layer has been avoided by depositing (p+) microcrystalline silicon (μc-Si:H). We report on the impact of this deposition step on the SiOx layer deposited by PECVD, and on the passivation properties by comparing PECVD and wet-chemical oxide in this hole-selective passivating contact stack. We have reached iVoc > 690 mV on p-type FZ wafers for wet-chemical SiOx\(p+) μc-Si\SiNx:H with no annealing step.

Home, head direction stability and grid cell distortion

The home is a unique location in the life of humans and animals. Numerous behavioral studies investigating homing indicate that many animals maintain an online representation of the direction of the home, a home vector. Here we placed the rat’s home cage in the arena, while recording neurons in the animal’s parasubiculum and medial entorhinal cortex. From a pellet hoarding paradigm it became evident that the home cage induced locomotion patterns characteristic of homing behaviors. We did not observe home-vector cells. We found that head-direction signals were unaffected by home location. However, grid cells were distorted in the presence of the home cage. While they did not globally remap, single firing fields were translocated towards the home. These effects appeared to be geometrical in nature rather than a home-specific distortion. Our work suggests that medial entorhinal cortex and parasubiculum do not contain an explicit neural representation of the home direction.

Effect of TiO2 on the sintering behavior and microstructure of stoichiometric spinel (MgAl2O4)

In this work, magnesium aluminate spinel (MA) (MgO 28 wt%, Al2O3 72 wt%) stoichiometric compound , were synthesized via solid state reaction (SSR) Single firing stage, and the impact of sintering on the physical properties and thermal properties as well as the fine structure and morphology of the ceramic product were examined. The Spinel samples were pressed at of (14 MPa) and sintering soaking time (2h). The effect of adding oxide titania (TiO2) was studied. The obtained powders were calcined at a temperature range of 1200 and 1400 °C. The calcined samples spinel were characterized by XRD, it showed the presence of developed spinel phase end also showed that the best catalyst is titania. The SEM image showed the high sintering temperature increased the regularity of the grain growth, as well as minutes from the acquisition of the spherical shape of the minutes spinel.

Data resampling: An approach for improving characterization of complex dynamics from noisy interspike intervals

Extracting dynamics from point processes produced by different models describing spiking phenomena depends on several factors affecting the quality of reconstruction of nonuniformly sampled dynamical systems. Although its ability is verified by embedding theorems analogous to the Takens theorem for uniformly sampled time series, a limited amount of samples, a low firing rate and the presence of noise can provide significant computational errors and incorrect characterization of the analyzed oscillatory regimes. Here, we discuss how to improve the accuracy of the quantitative evaluation of complex oscillations from point processes using data resampling. This approach provides a more stable estimation of Lyapunov exponents for noisy datasets. The advantages of resampling-based reconstruction are confirmed by the analysis of various spiking mechanisms, including the generation of single firing events and chaotic bursts.

Sparse Bursts Optimize Information Transmission in a Multiplexed Neural Code

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing rate output, which collapses all input streams into one. We propose that neurons can simultaneously represent multiple input streams by using a novel code that distinguishes single spikes and bursts at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. It also suggests specific connectivity patterns that allows to demultiplex this information. These connectivity patterns can be used by the nervous system to maintain optimal multiplexing. Contrary to firing rate coding, our findings indicate that a single neural ensemble can communicate multiple independent signals to different targets.