High-density Recording Systems Research Articles

Dipolar interaction and sample shape effects on the hysteresis properties of 2d array of magnetic nanoparticles

We study the ground-state and magnetic hysteresis properties of 2d arrays ( $$L^{}_x\times L^{}_y$$ ) of dipolar interacting magnetic nanoparticles (MNPs) by performing micromagnetic simulations. Our primary interest is to understand the effect of sample shape, $$\Theta $$ , the ratio of the dipolar strength to the anisotropy strength and the direction of the applied field $$\vec {H} = H_{0}{\hat{e}}^{}_H$$ on the ground state and the magnetic hysteresis in an array of MNPs. To study the effect of the shape of the sample, we have varied the aspect ratio $$A^{}_r=L^{}_y/L^{}_x$$ , which in turn, is found to induce shape anisotropy in the system. Our main observations are: (a) When the dipolar interaction is strong $$(\Theta >1)$$ , the ground-state morphology has an in-plane ordering of magnetic moments, (b) the ground-state morphology has randomly oriented magnetic moments that are robust regarding system sizes and $$A^{}_r$$ for weakly interacting MNPs ( $$\Theta <1$$ ), (c) micromagnetic simulations suggest that the dipolar interaction decreases the coercive field $$H^{}_c$$ , (d) the remanence magnetisation $$M^{}_r$$ is found to be strongly dependent not only on the strength of dipolar interaction but also on the shape of the sample and (e) due to the anisotropic nature of dipolar interaction, a strong shape anisotropy effect is observed when the field is applied along the long axis of the sample. In such a case, the dipolar interaction induces an effective ferromagnetic coupling when the aspect ratio is enormous. These results are of vital importance in high-density recording systems, magneto-impedance sensors, etc.

Fabrication of Long-Range Ordered Aluminum Oxide and Fe/Au Multilayered Nanowires for 3-D Magnetic Memory

Large-scale long-range ordered anodic aluminum oxide and multilayered nanowires (NWs) are attractive to 3-D nanostructured material applications, such as high-density 3-D magnetic memory. This article demonstrates long-range ordered aluminum oxide made by simple and inexpensive double imprinting with line-patterned stamp and uniform iron-gold multilayered NWs fabricated by galvanostatic electrochemical deposition with a single electrolyte bath. These two structural features show potential for future high-density recording systems that require long-range ordered devices separated from each other by insulation to eliminate crosstalk.

Dynamic Power Reduction in Scalable Neural Recording Interface Using Spatiotemporal Correlation and Temporal Sparsity of Neural Signals

We report a scalable neural recording interface with embedded lossless compression to reduce dynamic power consumption ( $\text{P}_{D}$ ) for data transmission in high-density neural recording systems. We investigated the characteristics of neural signals and implemented effective lossless compression for local field potential (LFP) and extracellular action potential (EAP or spike) in separate signal paths. For LFP, spatial–temporal (spatiotemporal) correlation of the LFP signals is exploited in a $\Delta $ -modulated $\Delta \Sigma $ analog-to-digital converter ( $\Delta -\Delta \Sigma $ ADC) and a dedicated digital difference circuit. Then, statistical redundancy is further eliminated through entropy encoding without information loss. For spikes, only essential parts of waveforms in the spikes are extracted from the raw data by using spike detectors and reconfigurable analog memories. The prototype chip was fabricated using 180-nm CMOS processes, incorporating 128 channels into a modular architecture that is easily scalable and expandable for high-density neural recordings. The fabricated chip achieved the data rate reduction for the LFPs and spikes by a factor of 5.35 and 10.54, respectively, from the proposed compression scheme. Consequently, $P_{D}$ was reduced by 89%, when compared to the uncompressed case. We also achieved the state-of-the-art recording performance of 3.37 $\mu \text{W}$ per channel, 5.18 $\mu V_{\mathrm {rms}}$ noise, and 3.41 ${\text {NEF}}^{2}V_{\mathrm {DD}}$ .

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks

Event Abstract Back to Event TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks Vito P. Pastore1*, Aleksandar Godjoski1, Sergio Martinoia1 and Paolo Massobrio1 1 University of Genova, Department of Informatics, Bioengineering, Robotics, System Engineering , Italy Motivation One of the goal of contemporary neuroscience is to study the interplay between topology, structure, functional-effective connectivity and neuronal dynamics at different level of complexity and on different experimental models (from simple in-vitro networks to whole brain areas). Functional connectivity is defined as the strength of the influence one network member has on another during ongoing behavior [1]. The analysis of multiple neural spike train data recorded from experimental models has gained tremendous relevance recently with the widespread application of Micro-Electrode Arrays (MEAs) [2]. At present, to the best of our knowledge, there is no available dedicated software that puts together a set of different functional connectivity analysis methods. Thus, we developed a user-friendly toolbox [3] in order to provide the researchers community a powerful tool to perform functional connectivity analysis on in-vitro neuronal networks coupled to standard and high-density MEAs, while guaranteeing computational efficiency and high accuracy. Material and Methods We implemented 'ToolConnect' toolbox as a standalone windows Graphical User Interface (GUI) application, using C# and Microsoft Visual Studio with .NET framework 4.5 development environment. The software is designed to be intuitive and straightforward to use. It is based on several windows forms and a friendly and modular GUI through which provides the user with powerful tools to manipulate and analyze data. ToolConnect offers functional connectivity analysis based on two correlation (cross-correlation and partial correlation) and two information theory based methods (transfer entropy and joint entropy). Cross correlation measures the frequency at which one cell fires as a function of time relative to the firing of a spike in another cell [4]. Partial correlation allows to distinguish between direct and indirect connections by removing the portion of the relationship between two spike trains that can be attributed to linear relationships with recorded spike trains from other neurons [5, 6]. Transfer Entropy is an information theoretic measure able to estimate causal relationships between time series taking into account their past activity [7]. Joint Entropy analyzes the cross Inter-Spike-Intervals (cISI): if two neurons are strongly connected the cISI histogram will show a peak and Joint Entropy will be close to zero, otherwise the cISI histogram will be almost flat, and the Joint Entropy will be high. The graphical section offers dedicated interfaces and allows the user to: i) plot the correlograms between each couple of the set of analyzed electrodes (for cross- and partial correlation); ii) manage, thresh and plot the Connectivity Matrix (CM) and the connectivity graph; iii) compute some powerful metrics that allow to extract the main topological features (degree, cluster coefficient, path length). One of the major features of the toolbox is its independence from the acquisition system (e.g. Multi Channel Systems, Qwane Biosciences, 3Brain) and from the MEA layout (number of microelectrodes and spatial organization). Results We designed and developed ToolConnect taking care to satisfy the user-friendliness requirement. According to this, our software has a GUI, which permits also to inexperienced users to perform functional connectivity analysis, to graphically represent the results, hiding the algorithms implementation specifics and software’s code design. Figure 1 shows a screenshot of ToolConnect’s GUI. Figure 2 shows the connectivity graphs obtained from the analysis of cortical networks coupled to the MEA60 and the MEA2100 acquisition systems of Multi Channel Systems (www.multichannelsystems.com; MCS, Reutlingen, Germany) and the BioCam acquisition system of 3Brain Systems. To assess the performances of tool connect we performed functional-connectivity analysis based on the cross-correlation method on hippocampal neuronal networks coupled to the MEA 60 from Multi-Channel Systems (MCS), during spontaneous and stimulus-evoked activity[3]. Discussion To the best of our knowledge, ToolConnect is the first functional connectivity toolbox dedicated to the analysis of multiple spike trains recorded from in-vitro neural networks coupled to MEAs. It provides the user with a complete set of computational and graphical tools of intuitive and straightforward usage through a dedicated and modular GUI. ToolConnect is implemented taking care of the optimization of the resources usage (requested RAM) and the reduction of the computational time. We were able to obtain acceptable performances with computational time lower than 2 minutes (for 10 minutes of recording sampled at 10 kHz). These performances make ToolConnect compatible with high-density recording systems (e.g., the 4096 electrodes of the 3brain system). In this way, it will be possible to perform functional connectivity analysis on neural networks with dimensions of thousands of neurons preserving an acceptable spatial and temporal resolution, hence allowing to obtain realistic and complete information on the dynamics and the topology of such systems.

Cycle Slip Detection and Correction Through Classification of Modulation Code Failures

In high-density recording systems, synchronization might be lost due to increased noise and/or inaccurate sampling/filtering. In such, long bursts of errors occur due to bits either erased from or inserted into the bit stream. At low signal-to-noise ratios, such bit slips (also known as cycle slips) lead to excessive post-error correction code (post-ECC) failures. In order to reduce the rate of the post-ECC failures, cycle slips should be detected and corrected. In the past, the majority of the research was concentrated on iterative joint timing recovery and error decoding methodologies. In this study, we propose to detect and correct cycle slips using a decoding failure analysis of maximum transition run (MTR) codeword sequences in conjunction with a general-purpose classification algorithm. First, noncyclic properties of constrained coding are identified by realizing that MTR codewords render invalid more often if bit shifts happen. Later, we present a method to use such invalid codewords to help detect and correct cycle slips. Finally, some numerical results are presented to show the effectiveness of the proposed detection/correction scheme using MTR codes and the support vector machines. It is demonstrated that such a combination has the potential for BER and post-ECC failure rate performance improvements by eliminating cycle slips for the generalized partial response channels.

Computationally Efficient Neural Feature Extraction for Spike Sorting in Implantable High-Density Recording Systems

Modern microelectrode arrays acquire neural signals from hundreds of neurons in parallel that are subsequently processed for spike sorting. It is important to identify, extract, and transmit appropriate features that allow accurate spike sorting while using minimum computational resources. This paper describes a new set of spike sorting features, explicitly framed to be computationally efficient and shown to outperform principal component analysis (PCA)-based spike sorting. A hardware friendly architecture, feasible for implantation, is also presented for detecting neural spikes and extracting features to be transmitted for off chip spike classification. The proposed feature set does not require any off-chip training, and requires about 5% of computations as compared to the PCA-based features for the same classification accuracy, tested for spike trains with a broad range of signal-to-noise ratio. Our simulations show a reduction of required bandwidth to about 2% of original data rate, with an average classification accuracy of greater than 94% at a typical signal to noise ratio of 5 dB.

Highly Parallel FPGA Emulation for LDPC Error Floor Characterization in Perpendicular Magnetic Recording Channel

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Low-density parity-check (LDPC) codes offer a promising error correction approach for high-density magnetic recording systems due to their near-Shannon limit error-correcting performance. However, evaluation of LDPC codes at the extremely low bit error rates (BER) required by hard disk drive systems, typically around <formula formulatype="inline"><tex Notation="TeX">$10^{-12}$</tex> </formula> to <formula formulatype="inline"><tex Notation="TeX">$10^{- 15}$</tex> </formula>, cannot be carried out on high-performance workstations using conventional Monte Carlo techniques in a tractable amount of time. Even field-programmable gate array (FPGA) emulation platforms take a few weeks to reach BER between <formula formulatype="inline"><tex Notation="TeX">$10^{-11}$</tex></formula> and <formula formulatype="inline"><tex Notation="TeX">$10^{-12}$</tex></formula>. Thus, we implemented a highly parallel FPGA processing cluster to emulate a perpendicular magnetic recording channel, which enabled us to accelerate the emulation by <formula formulatype="inline"><tex Notation="TeX">$&gt; 100 \times$</tex></formula> over the fastest reported emulation. This increased throughput enabled us to characterize the performance of LDPC code BER down to near <formula formulatype="inline"> <tex Notation="TeX">$10^{- 14}$</tex></formula> and investigate its error floor. </para>

Micromagnetism of advanced hard magnetic materials

Abstract The continuum theory of micromagnetism has been found to be a rather effective tool to describe magnetic states and magnetization processes. In this paper some rigorous micromagnetic results obtained for characteristic magnetic properties of nanocrystalline hard magnetic materials will be outlined and their application to the development of outstanding magnetic materials will be discussed. In particular the role of exchange coupled composite systems will be considered and the effects of the microstructure will be investigated. Furthermore, the deteriorating effects of magnetically softened surface regions, the depinning and pinning of domain walls at phase boundaries, the remanence exchange enhancing effects and the depinning fields of domain walls of composite particles in the case of high-density recording systems will be discussed.

CPP-GMR technology for magnetic read heads of future high-density recording systems

This paper introduces CPP-GMR technology, its features, routes to output enhancement, problems to be solved and possibilities as a recording head. For instance, use of high spin-dependent bulk scattering, high resistivity, or half-metallic magnetic materials for free and reference magnetic layers were shown as ways to improve the output of CPP-GMR. A current state of CPP-GMR head development was also mentioned in view points of sensor downsizing, magnetic head noise and high-density recording demonstration. CPP-GMR still has some points to be improved, however it is believed that the CPP-GMR will actualize a next high-performance magnetic read head in no distant future.

Critical fields of an exchange coupled two-layer composite particle

High-density recording systems require magnetic bits with perpendicular easy axis and large magnetocrystalline anisotropy to guarantee thermal stability. However, the large magnetic fields up to 10 T for the reversal of magnetization cannot be afforded by conventional write heads. Therefore, composite exchange coupled spring systems of soft and hard magnetic layers may be used to reduce the switching field. In this case the reversal of magnetization in general takes place in two steps: a nucleation process in the soft layer and a depinning process for the displacement of the domain wall at the phase boundary of the soft and the hard magnetic layer. The nucleation and depinning fields are determined on the basis of the continuum theory of micromagnetism. It is shown that the nucleation fields decrease according to a 1 / L 2 law with increasing thickness L of the soft layer and the depinning field of the charged Néel wall may be reduced by factors of 3–6 in comparison with the ideal nucleation field of the hard magnetic phase. One-step rectangular hysteresis loops are obtained for thicknesses of the soft layer smaller than the exchange length of the magnetostatic field.

Topology design to improve HDD suspension dynamic characteristics

In this paper, topology optimization in design of suspensions for high-density magnetic recording systems is studied. With the development of higher data density and the faster data streaming, a hard-disk drive (HDD) is required to move faster with greater positioning accuracy. At this situation, even small mechanical vibration induced by seeking motion may result in error in data reading. According to the operational characteristics of HDD suspension, the present work is focused on the topology design of HDD suspension for the dynamic shock response problem. As a result of this research, several new topological structures of HDD suspension are obtained, which are largely different from previous work, and dynamic response under shock force and stability of passive structure are also improved.

Evaluation of gap length fluctuation with harmonic analysis method

Gap length (GL) of reading head is one of the most critical parameters for high-density magnetic recording systems. A novel method is proposed for quantitative evaluation of the GL fluctuation among a batch of magneto-resistive/giant magneto-resistive heads with same structure design. The method works at head-gimbal assembly level and the evaluation can be done with any read/write analysis equipment. The testing process is based on harmonic analysis of the readback signal. The testing system consists of a selected reference head and sample heads for evaluation. A GL variation function is introduced for the evaluation of GL deviation between the reference head and sample head. This method proved to be easy for implementation and results suggest that variation of GL is considerable and has obvious effect on recording performance in high recording density systems.

Adaptive Equalization Using Expanded Maximum-Likelihood Detector Output for Optical Recording Systems

For high-density optical recording systems, the effectiveness of the partial–response maximum-likelihood (PRML) detection scheme is limited by the channel asymmetry. Hence, to solve the problem and improve detection performance, we present an adaptive equalization scheme using the output of an expanded maximum-likelihood (ML) detector. The proposed PRML detection method adapts well to the asymmetric optical recording channels and has better bit error rate performance than the conventional PRML detection method.

A New 9-Level Run-Length-Limited (1, 3) Code for Multilevel Optical Recording Channels

In this paper, we propose the M=9 run-length-limited (d,k)=(1,3) code for multilevel recording channels. The code rate is 5/3 (bits/symbol), and the code has a simple 3-state encoder and a sliding block decoder. During the decoding process, the maximum error propagation of input data is seven bits. The structures of the encoder and decoder of the proposed code are very simple, so it may be easily implemented for high-density multilevel recording systems.

Modeling of the nonlinear perpendicular recording readback signal

Perpendicular recording has been proposed as a better recording technology than the traditional longitudinal recording for high-density recording systems. The change in the recording format from longitudinal to perpendicular recording brings about new challenges in head, media, and channel designs. One of these challenges is the increasing amount of read and write nonlinearities. In addition, the nature of these nonlinearities may not be the same as in longitudinal recording. We developed a model for characterizing and modeling nonlinearities, regardless of their nature or severity, based on finite memory Volterra series. It was determined that a Volterra Model does not adequately model the perpendicular recording signal due to the fact that perpendicular recording suffers from very long inter-symbol interference (ISI). The long ISI is due to the presence of droop effect and high pass filter effects of the preamplifier and this translates into a requirement of large memory length L, in order to avoid truncation effects that lead to lack of fit of the model. The new model introduced here is called the composite Volterra model and it requires a moderate memory length and deals with the extended ISI using a linear model. The composite Volterra model is shown to provide a good fit to measured signals with various amounts of nonlinearities and using the composite Volterra model, one is able to synthesize a readback signal with appropriate nonlinearities for any given input data pattern.

Head position control on quasi-static read/write tester

To develop high-density magnetic recording systems, a simple feedback system without servo writing was applied to a quasi-static read/write tester in which a medium reciprocates against a stand-still head. The head position signal in cross–track direction during the scanning is fed back to the high-precision piezoelectric actuator of the media stage. The stage is controlled so as to make the head/medium alignment error zero. A contact head slider assembled on a parallel–link suspension was used to evaluate the feedback system. The tester shows an accuracy of 1.5nm in cross–track direction which is preferable for the read/write tests at future high recording densities.

Synthesis of core-shell nanowires of FeCoNi alloy core with silicon oxide layers

Using simple chemical vapor deposition method, the core-shell nanowires of FeCoNi alloy core with SiOx layers were made by two steps. As the FeCoNi alloy exhibits excellent soft magnetic properties and good thermal stability, the core-shell nanowires may be used in nanodevices such as the write heads of high-density magnetic recording systems.

Synthesis of core-shell nanowires of FeCoNi alloy core with silicon oxide layers

Using simple chemical vapor deposition method, the core-shell nanowires of FeCoNi alloy core with SiOx layers were made by two steps. As the FeCoNi alloy exhibits excellent soft magnetic properties and good thermal stability, the core-shell nanowires may be used in nanodevices such as the write heads of high-density magnetic recording systems.

Critical thicknesses of domain formations in cubic particles and thin films

Single-domain, high-remanence particles are the prerequisite for their application in magnetic high-density recording systems. Analytical calculations for the transition between single and multidomain configurations remain unprecise because the calculation of the stray field requires strong simplifications. By means of the three-dimensional finite element method, zero-field magnetization structures of magnetically hard and soft thin film elements and cubic particles have been determined. Numerical results are compared with analytical calculations showing that the latter results depend sensitively on the energy determined for vortex structures.