SEU Tolerant Robust Memory Cell Design

The implementation of semiconductor circuits and systems in nano-technology makes it possible to achieve high speed, lower voltage level and smaller area. The unintended and undesirable result of this scaling is that it makes integrated circuits susceptible to soft errors normally caused by alpha particle or neutron hits. These events of radiation strike resulting into bit upsets referred to as single event upsets(SEU), become increasingly of concern for the reliable circuit operation in the field. Storage elements are worst hit by this phenomenon. As we further scale down, there is greater interest in reliability of the circuits and systems, apart from the performance, power and area aspects. In this paper we propose an improved 12T SEU tolerant SRAM cell design. The proposed SRAM cell is economical in terms of area overhead. It is easy to fabricate as compared to earlier designs. Simulation results show that the proposed cell is highly robust, as it does not flip even for a transient pulse with 62 times the Q(crit) of a standard 6T SRAM cell.

Dataflow analysis for data-race free programs

Memory models for shared-memory concurrent programming languages typically guarantee sequential consistency (SC) semantics for datarace-free (DRF) programs, while providing very weak or no guarantees for non-DRF programs. In effect programmers are expected to write only DRF programs, which are then executed with SC semantics. With this in mind, we propose a novel scalable solution for dataflow analysis of concurrent programs, which is proved to be sound for DRF programs with SC semantics. We use the synchronization structure of the program to propagate dataflow information among threads without requiring to consider all interleavings explicitly. Given a dataflow analysis that is sound for sequential programs and meets certain criteria, our technique automatically converts it to an analysis for concurrent programs.

Basic features of current induced/shared by cables mounted on down conductors/towers during stroke interception

The telecommunication, broadcasting and other instrumented towers carry power and/or signal cables from their ground end to their upper regions. During a direct hit to the tower, significant induction can occur to these mounted cables. In order to provide adequate protection to the equipments connected to them, protection schemes have been evolved in the literature. Development of more effective protection schemes requires a quantitative knowledge on various parameters. However, such quantitative knowledge is difficult to find at present. Amongst several of these aspects, the present work aims to investigate on the two important aspects: (i) what would be the nature of the induced currents and (ii) what will be the current sharing if as per the practice, the sheath of the cable is connected to the down conductor/tower. These aspects will be useful in design of protection schemes and also in analyzing the field structure around instrumented towers.

Throughput analysis of primary and secondary networks in a shared IEEE 802.11 system

In this paper, we analyze the coexistence of a primary and a secondary (cognitive) network when both networks use the IEEE 802.11 based distributed coordination function for medium access control. Specifically, we consider the problem of channel capture by a secondary network that uses spectrum sensing to determine the availability of the channel, and its impact on the primary throughput. We integrate the notion of transmission slots in Bianchi's Markov model with the physical time slots, to derive the transmission probability of the secondary network as a function of its scan duration. This is used to obtain analytical expressions for the throughput achievable by the primary and secondary networks. Our analysis considers both saturated and unsaturated networks. By performing a numerical search, the secondary network parameters are selected to maximize its throughput for a given level of protection of the primary network throughput. The theoretical expressions are validated using extensive simulations carried out in the Network Simulator 2. Our results provide critical insights into the performance and robustness of different schemes for medium access by the secondary network. In particular, we find that the channel captures by the secondary network does not significantly impact the primary throughput, and that simply increasing the secondary contention window size is only marginally inferior to silent-period based methods in terms of its throughput performance.

Memory effect in SrRu(1-x)O3 (0.01 < x < 0.07)

SrRuO3 is widely known to be an itinerant ferromagnet with a T-C similar to 160 K. It is well known that glassy materials exhibit time dependent phenomena such as memory effect due to their generic slow dynamics. However, for the first time, we have observed memory effect in SrRu(1-x)O3 (0.01memory etc. arise in disordered glassy systems. Thus the observation of memory effect in case of an itinerant ferromagnetic system like SrRuO3 is quite strange. The emergence of such unusual magnetic response is strongly believed to be connected with a cryptic interactions arises in the low temperature. Our effort on neutron diffraction study has been able to trace the cause of such hidden magnetic interaction responsible for bringing glassiness in a ferromagnet.

Effect of thermal and thermo-mechanical cycling on the microstructure of Ni-rich NiTi shape memory alloys

Microstructural changes of Ni-rich NiTi shape memory alloy during thermal and thermo-mechanical cycling have been investigated using Electron Back Scattered Diffraction. A strong dependence of the orientation of the prior austenite grain on the misorientation development has been observed during thermal cycling and thermo-mechanical cycling. This effect is more pronounced at the grain boundaries compared to grain interior. At a larger applied strain, the volume fraction of stabilized martensite phase increases with increase in the number of cycling. Deformation within the martensite leads to stabilization of martensitic phase even at temperatures slightly above the austenite finish temperature. Modulus variation with respect to temperature has been explained on the basis of martensitic transformation.

Spatial memory deficits in maternal iron deficiency paradigms are associated with altered glucocorticoid levels

``The goal of this study was to examine the effect of maternal iron deficiency on the developing hippocampus in order to define a developmental window for this effect, and to see whether iron deficiency causes changes in glucocorticoid levels. The study was carried out using pre-natal, post-natal, and pre + post-natal iron deficiency paradigm. Iron deficient pregnant dams and their pups displayed elevated corticosterone which, in turn, differentially affected glucocorticoid receptor (GR) expression in the CA1 and the dentate gyrus. Brain Derived Neurotrophic Factor (BDNF) was reduced in the hippocampi of pups following elevated corticosterone levels. Reduced neurogenesis at P7 was seen in pups born to iron deficient mothers, and these pups had reduced numbers of hippocampal pyramidal and granule cells as adults. Hippocampal subdivision volumes also were altered. The structural and molecular defects in the pups were correlated with radial arm maze performance; reference memory function was especially affected. Pups from dams that were iron deficient throughout pregnancy and lactation displayed the complete spectrum of defects, while pups from dams that were iron deficient only during pregnancy or during lactation displayed subsets of defects. These findings show that maternal iron deficiency is associated with altered levels of corticosterone and GR expression, and with spatial memory deficits in their pups.'' (C) 2013 Elsevier Inc. All rights reserved.

Temperature dependence of indentation recovery ratios in austenitic and martensitic shape memory alloys

Instrumented microindentation (IM) on two Ni-Ti shape memory alloys (SMAs), where one is austenitic and the other is martensitic at room temperature, were conducted from 40 to 150 degrees C. Results show that the depth and work recovery ratios, eta(d) and eta(w) respectively, are complementary to each other. While eta(d) decreases gradually with temperature for austenite, it drops markedly for the martensite in the martensite-to-austenite transformation regime. These results affirm the utility of IM for characterizing SMAs.

Simultaneous measurement of mechanical and electrical contact resistances during nanoindentation of NiTi shape memory alloys

The nanoindentation technique can be employed in shape memory alloys (SMAs) to discern the transformation temperatures as well as to characterize their mechanical behavior. In this paper, we use it with simultaneous measurements of the mechanical and the electrical contact resistances (ECR) at room temperature to probe two SMAs: austenite (RTA) and martensite (RTM). Two different types of indenter tips - Berkovich and spherical - are employed to examine the SMAs' indentation responses as a function of the representative strain, epsilon(R). In Berkovich indentation, because of the sharp nature of the tip, and in consequence the high levels of strain imposed, discerning the two SMAs on the basis of the indentation response alone is difficult. In the case of the spherical tip, epsilon(R) is systematically varied and its effect on the depth recovery ratio, eta(d), is examined. Results indicate that RTA has higher eta(d) than RTM, but the difference decreases with increasing epsilon(R) such that eta(d) values for both the alloys would be similar in the fully plastic regime. The experimental trends in eta(d) vs. epsilon(R) for both the alloys could be described well with a eta(d) proportional to (epsilon(R))(-1) type equation, which is developed on the basis of a phenomenological model. This fit, in turn, directs us to the maximum epsilon(R), below which plasticity underneath the indenter would not mask the differences in the two SMAs. It was demonstrated that the ECR measurements complement the mechanical measurements in demarcating the reverse transformation from martensite to austenite during unloading of RTA, wherein a marked increase in the voltage was noted. A correlation between recovery due to reverse transformation during unloading and increase in voltage (and hence the electrical resistance) was found. (C) 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Rotating beams and non-rotating beams with shared eigenpair for pinned-free boundary condition

In this paper we look for a rotating beam, with pinned-free boundary conditions, whose eigenpair (frequency and mode-shape) is same as that of a uniform non-rotating beam for a particular mode. It is seen that for any given mode, there exists a flexural stiffness function (FSF) for which the ith mode eigenpair of a rotating beam with uniform mass distribution, is identical to that of a corresponding non-rotating beam with same length and mass distribution. Inserting these derived FSF's in a finite element code for a rotating pinned-free beam, the frequencies and mode shapes of a non-rotating pinned-free beam are obtained. For the first mode, a physically realistic equivalent rotating beam is possible, but for higher modes, the FSF has internal singularities. Strategies for addressing these singularities in the FSF for finite element analysis are provided. The proposed functions can be used as test functions for rotating beam codes and also for targeted destiffening of rotating beams.

Hybrid aggregated-vector algorithm for efficient parallelization of fast multipole method

An efficient parallelization algorithm for the Fast Multipole Method which aims to alleviate the parallelization bottleneck arising from lower job-count closer to root levels is presented. An electrostatic problem of 12 million non-uniformly distributed mesh elements is solved with 80-85% parallel efficiency in matrix setup and matrix-vector product using 60GB and 16 threads on shared memory architecture.

Evolution of microstructure and texture in Ni49.4Ti38.6Hf12 shape memory alloy during hot rolling

This paper deals with the evolution of microstructure and texture during hot rolling of hafnium containing NiTi based shape memory alloy Ni49.4Ti38.6Hf12. The formation of the R-phase has been associated with the precipitation of (Ti,Hf)(2)Ni phase. The crystallographic texture of the parent phase B2 as well as the product phases R and B19' have been determined. It has been found that the variant selection during the B2 -> R phase transformation is quite strong compared to the case of the B2 -> B19' transformation. During deformation, the texture of the austenite phase evolves with strong Goss and Bs components. After transformation to martensitic structure, it gives rise to a 011]parallel to RD fiber. Microstructure and texture studies reveal the occurrence of partial dynamic recrystallization during hot rolling. Large strain heterogeneities that occur surrounding (Ti,Hf)(2)Ni precipitates are relieved through extended dynamic recovery instead of particle stimulated nucleation.

Combining indentation and diffusion couple techniques for combinatorial discovery of high temperature shape memory alloys

We demonstrate the possibility of accelerated identification of potential compositions for high-temperature shape memory alloys (SMAs) through a combinatorial material synthesis and analysis approach, wherein we employ the combination of diffusion couple and indentation techniques. The former was utilized to generate smooth and compositionally graded inter-diffusion zones (IDZs) in the Ni-Ti-Pd ternary alloy system of varying IDZ thickness, depending on the annealing time at high temperature. The IDZs thus produced were then impressed with an indenter with a spherical tip so as to inscribe a predetermined indentation strain. Subsequent annealing of the indented samples at various elevated temperatures, T-a, ranging between 150 and 550 degrees C allows for partial to full relaxation of the strain imposed due to the shape memory effect. If T-a is above the austenite finish temperature, A(f), the relaxation will be complete. By measuring the depth recovery, which serves as a proxy for the shape recovery characteristic of the SMA, a three-dimensional map in the recovery temperature composition space is constructed. A comparison of the published Af data for different compositions with the Ta data shows good agreement when the depth recovery is between 70% and 80%, indicating that the methodology proposed in this paper can be utilized for the identification of promising compositions. Advantages and further possibilities of this methodology are discussed.

Information content of molecular graph and prediction of gas phase thermal entropy of organic compounds

Entropy is a fundamental thermodynamic property that has attracted a wide attention across domains, including chemistry. Inference of entropy of chemical compounds using various approaches has been a widely studied topic. However, many aspects of entropy in chemical compounds remain unexplained. In the present work, we propose two new information-theoretical molecular descriptors for the prediction of gas phase thermal entropy of organic compounds. The descriptors reflect the bulk and size of the compounds as well as the gross topological symmetry in their structures, all of which are believed to determine entropy. A high correlation () between the entropy values and our information-theoretical indices have been found and the predicted entropy values, obtained from the corresponding statistically significant regression model, have been found to be within acceptable approximation. We provide additional mathematical result in the form of a theorem and proof that might further help in assessing changes in gas phase thermal entropy values with the changes in molecular structures. The proposed information-theoretical molecular descriptors, regression model and the mathematical result are expected to augment predictions of gas phase thermal entropy for a large number of chemical compounds.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.