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The role of substrate bias during the sputter deposition of various layers of double-layered CoCrPt-SiO2 perpendicular recording media has been investigated in order to understand the physical mechanisms behind the various effects observed. Perpendicular recording media with dual Ru intermediate layers were investigated using several magnetic and microstructural characterization techniques. It was observed that, in general, the application of a bias voltage during the deposition of the seedlayer (Ta) and the first intermediate layer (Ru) is helpful in reducing the c-axis dispersion of the recording layer. For the other layers, application of bias voltage leads to deterioration in the magnetic properties. It was also observed that the application of a bias voltage during the deposition of the first intermediate layer (especially Ru) may not enhance the preferred growth of Ru hexagonal-close-packed (00.2) planes parallel to the disk surface, as predicted before. However, the bias voltage on the Ru layer still reduces the c-axis dispersion of the magnetic layer. From the omega-offset x-ray diffraction investigations, it is estimated that the lattice parameter “a” of the Ru layer is reduced slightly with bias voltage, which could probably lead to a reduction in the lattice mismatch between the Ru layer and Co-alloy layer. Bias conditions also could lead to improved interface condition. Such an improvement in the lattice matching or interface conditions could probably be the cause of the reduction of c-axis dispersion of the recording layer. [ABSTRACT FROM AUTHOR]

Neuronal microcircuits are formed of a myriad of spatially and functionally specific cell classes. Despite the importance of the spatial component in the characterisation of neural circuits, it has not received the attention it deserves. While multi-electrodes are widely used in the study of microcircuits, the spatial information available from them remains largely unexploited for analysis beyond spike sorting. Here we show how the spatial pattern of the extracellular signal is determined by both the electrophysiology and morphology of neurons. Starting from known current source models for the generation of the extracellular potential, we use the spatial pattern observed across a multi-electrode array to localise and classify neurons into putative morphological classes. We evaluated the localisation and classification models with low fitting errors in simulated data. When applying them to recorded data we found correspondence between localisation statistics and expected recording radius and found evidence to support the separation into putative morphological classes. While existing localisation methods do not hold for the recording distances expected on multi-electrode recordings (under 60 μm), classification methods have been limited to the temporal component by either characterising spike shape or firing patterns. We show here how the information available from extracellular recordings can be used to localise and classify neurons based on the spatial pattern seen by multi-electrode arrays. Together they can improve current characterisation and classification of neurons based on complementary criteria such us firing pattern and functional characterisation. [ABSTRACT FROM AUTHOR]