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Byline: Sivaraman. Kumarasamy, Siddharth. Joshi, Raj. Ghoniya, Kanwaljeet. Garg, Amol. Raheja Sir, Schizencephaly is a rare neuronal migration abnormality characterized by a cleft in the cerebral hemisphere lined by heterotopic [...]

This paper investigates pseudo-polymorphism in 2-pyridylmethoxy derivatives of p-tert-butylcalix[4]arene (PyC4), p-tert-butyldihomooxa-calix[4]arenes (PyHOC4), and p-tert-butylhexahomotrioxacalix[3]arenes (PyHO3C3), presenting 11 crystal structures with 15 crystallographically independent molecules. The macrocycle of PyC4 is smaller and less flexible with respect to those of PyHOC4 and PyHO3C3, and in solution, the cone conformation of these three molecules exhibits different point symmetries: C4, Cs, and C3, respectively. A correlation is observed between the macrocycle's structural rigidity and the number of pseudo-polymorphs formed. The more rigid PyC4 displays a higher number (six) of pseudo-polymorphs compared to PyHOC4 and PyHO3C3, which exhibit a smaller number of crystalline forms (three and two, respectively). The X-ray structures obtained show that the conformation of the macrorings is primarily influenced by the presence of an acetonitrile guest molecule within the cavity, with limited impact from crystal packing and intermolecular co-crystallized solvent molecules. Notably, both calix[4]arene derivatives produce a host–guest complex with acetonitrile, while the most flexible and less aromatic PyHO3C3 does not give crystals with acetonitrile as the guest. Intertwined 1D and 2D solvent channel networks were observed in the PyHOC4-hexane and in the PyHO3C3-H2O-MeOH crystal structures, respectively, while the other pseudopolymorphs of PyHOC4 and PyHO3C3 and all PyC4 crystal forms exhibit closely packed crystal structures without open channels. [ABSTRACT FROM AUTHOR]

Bismuth ferrite is a potentially interesting lead-free piezoelectric material for use in high-temperature applications due to its high Curie temperature. However, the high coercive field and high leakage currents of pure BiFeO3 (BFO) prevent reaching its theoretical performance level. The classic approach to tailoring piezoceramic properties to their desired use conditions is the use of doping. In this work, we produce bulk BFO piezoceramic by the conventional sintering method with single element doping with cobalt (0.125–3 at. %) or titanium (1–5 at. %) and dual doping (Co and Ti added simultaneously). Cobalt doping reduces the required field for poling and also increases the leakage currents. Titanium doping reduces the leakage currents but destroys the piezoelectric properties as the coercive field strength cannot be reached. However, when both elements are used simultaneously at their appropriate levels (0.25 at. % each), a piezoelectric ceramic material is obtained, requiring a low field for full poling (9 kV/mm) and showing excellent room temperature performance such as a d33 = 40 pC/N, a dielectric constant in the region of 100 and dielectric losses less than 1%. [ABSTRACT FROM AUTHOR]

Nominally undoped indium nitride (InN) is known to have an electron accumulation layer on its surface, and prior studies have shown this layer to be sensitive to chemical species. However, the exact roles of these species and the underlying mechanism of e− accumulation layer formation are not clear. In this work, it is shown that ambient adsorbed water on the InN surface strongly enhances the e− accumulation layer formed due to intrinsic surface states. Desorption of ambient physisorbates leads to a decrease in band bending, an increase in work function in undoped InN, and the observation of a p-type Mott-Schottky behavior in Mg:doped InN. The underlying mechanism of this surface-adsorbed water interaction may be through a process called "surface transfer doping," which has previously been reported in hydrogenated diamond and other semiconductors such as GaN and ZnO. [ABSTRACT FROM AUTHOR]

This paper discusses the fabrication of nano ZnO filled epoxy/basalt fiber composites and investigates the effect of such hybridization on the mechanical properties of the composites. The epoxy resin is modified by the addition of ZnO nanofiller in different wt.% (1–5 wt%). The dispersion of the nanofiller within the epoxy resin is brought about by sonication technique. The composites are fabricated through wet layup followed by curing under compression molding. To ascertain the effect of resin modification, flexural and tensile strengths are evaluated. The inter‐laminar shear strength (ILSS) is determined to identify the compatibility of the ZnO nanofiller with the epoxy and the basalt fibers. The improvement in the ILSS indicates thorough wettability being achieved between all the composite components. The addition of ZnO nanofiller improved the flexural strength, while there was no remarkable improvement in the tensile strength of the ZnO modified epoxy/baslat (BEZ) composite. Maximum improvement in the strengths is observed for BEZ2 composite with 2 wt% of ZnO nanofiller. For BEZ2 composite, the flexural and tensile strength increased by about 22% and 9.78% respectively in comparison to unfilled BEZ0 composite. The ILSS for BEZ2 composite improved by 21.74% in comparison to BEZ0 composite. In addition, more than 2 wt% addition of the ZnO nanofiller resulted in the reduction of flexural, tensile and ILSS of the composite due to agglomeration. Such agglomeration resulted in the failure of the composite due to delamination, matrix cracking and fiber fracture as observed through scanning electron microscopy images of the fractured composites. [ABSTRACT FROM AUTHOR]

The research on perovskite-based photovoltaics is rapidly increasing in renewable energy, particularly in solar cell fields, as its efficiency has reached upto 25.7%. Although several efforts have been made to increase the lifetime and stability, several complicated processes are still required to be optimized, to achieve better structural and chemical stability. In this paper, perovskite material is prepared in different molar ratios, in combinations of lead iodide (PbI2) and methyl ammonium iodide (MAI) for better interdiffusion and structural ordering. All the measurements have been performed only using ITO as a bare substrate. The synchrotron source-based X-ray diffraction measurements together with SEM and UV–Vis spectroscopy were performed to understand the structural properties of perovskite films across the entire film thickness for three different molar ratios. The angular-dependent grazing incidence X-ray diffraction (GIXRD) under out-of-plane geometry can provide a better insight of structural properties at the film/air and film/ITO interfaces. Our GIXRD measurements summarizes that within the entire depth of the perovskite film, unreacted PbI2 small particles are not only laying at the bottom, but such unreacted PbI2 crystallites are also distributed along with the entire height of the perovskite film. This is completely new finding, which has been not reported yet. In combined, all these three (SEM, GIXRD, UV–Vis) states of the art techniques have not only provided the in-depth structural ordering within the entire height of the films but also reveals the surface morphology and varying optical properties of the film with varying the molar ratio's too. [ABSTRACT FROM AUTHOR]

We consider the problem of choosing the gate sizes or scale factors in a combinational logic circuit in order to minimize the total area, subject to simple RC timing constraints, and a minimum-allowed gate size. This problem is well known to be a geometric program (GP), and can be solved by using standard interiorpoint methods for small- and medium-size problems with up to several thousand gates. In this paper, we describe a new method for solving this problem that handles far larger circuits, up to a million gates, and is far faster. Numerical experiments show that our method can compute an adequately accurate solution within around 200 iterations; each iteration, in turn, consists of a few passes over the circuit. In particular, the complexity of our method, with a fixed number of iterations, is linear in the number of gates. A simple implementation of our algorithm can size a 10 000 gate circuit in 25 s, a 100 000 gate circuit in 4 min, and a million gate circuit in 40 min, approximately. For the million gate circuit, the associated GP has three million variables and more than six million monomial terms in its constraints; as far as we know, these are the largest GPs ever solved. Index Terms--Gate sizing, geometric programming (GP), large-scale optimization.

Single-mode diode lasers on an InP(001) substrate have been developed using InAs/In0.53Ga0.47As quantum dash (Qdash) active regions and etched lateral Bragg gratings. The lasers have been designed to operate at wavelengths near 2 lm and exhibit a threshold current of 65mA for a 600 lm long cavity, and a room temperature continuous wave output power per facet >5mW. Using our novel growth approach based on the low ternary In0.53Ga0.47As barriers, we also demonstrate ridge-waveguide lasers emitting up to 2.1 lm and underline the possibilities for further pushing the emission wavelength out towards longer wavelengths with this material system. By introducing experimentally the concept of high-duty-cycle lateral Bragg gratings, a side mode suppression ratio of >37 dB has been achieved, owing to an appreciably increased grating coupling coefficient of j~40 cm-1. These laterally coupled distributed feedback (LC-DFB) lasers combine the advantage of high and well-controlled coupling coefficients achieved in conventional DFB lasers, with the regrowth-free fabrication process of lateral gratings, and exhibit substantially lower optical losses compared to the conventional metal-based LC-DFB lasers. [ABSTRACT FROM AUTHOR]

The present study reports the dynamics of phase transitions at the high electron injection limit in two-dimensional layers of VO2, an archetypical strongly correlated oxide that undergoes an insulator to metal transition (IMT) at 67 °C. Prior studies to date have reported electron doping as high as ∼1021 cm−3 in epitaxial thin films and nanoparticles of VO2 through catalytic spillover and electrochemical gating in ionic liquid electrolytes, which has been shown to induce a sequential insulator-to-metal-to-insulator phase transition. With the use of two-dimensional crystalline platelets, which enable fast Li+ diffusion and out diffusion kinetics during electrochemical gating, we show that an electron density as high as 4 × 1023 cm−3 can be reversibly injected into VO2 without significant structural damage. This leads to a giant conduction modulation involving an unprecedented three-step insulator-to-metal-to-insulator-to-metal transition along with a switch in the electrical polarity from n-type to p-type due to electron doping. A unified "lattice redox model" to explain the origin of thermal-, electrochemical-, and compositional-induced IMT that involves vanadium redox-induced band filling, structural distortion, and electron correlative effects is proposed. [ABSTRACT FROM AUTHOR]

Background: Idiopathic generalized epilepsy is defined as seizures with a possible hereditary predisposition without an underlying cause or structural pathology. Assessment of executive dysfunction in idiopathic generalized epilepsies based on standard Indian battery is not available in the literature. Aims and Objectives: To assess specific executive functions affected in patients with idiopathic epilepsy and their association with various variables. Materials and Methods: Type of observational cross-sectional study, where clinical profile of all idiopathic epilepsy patients attending the neurology OPD was studied and their executive higher mental functions were assessed using the NIMHANS battery. Results: A total of 75 idiopathic generalized epilepsy patients were included in the study. Executive functions that were commonly found abnormal in our study were word fluency (P ≤.001), category fluency (P <.001), verbal n-back (P <.001), Tower of London (p < 0.01), and Stroop test (P < 0.01). Executive functions showed a significant correlation with age at symptom onset, duration of epilepsy, and in those with uncontrolled seizures. Conclusion: Patients of idiopathic generalized epilepsy according to the present study were found to have significant executive dysfunction in multiple domains. This necessitates the screening for executive dysfunctions, which if detected should prompt the clinician to initiate cognitive retraining. [ABSTRACT FROM AUTHOR]

Outsourcing of integrated circuit (IC) manufacturing and increasing sophistication of IC reverse engineering techniques have unleashed security threats such as intellectual property (IP) theft and insertion of hardware Trojans. In this article, we propose and implement a run-time reconfigurable camouflage logic technology based on ferroelectric field-effect transistor (FeFET). The technology simultaneously obfuscates design IP from zero-trust foundry and untrusted testing facility and thwarts reverse engineering and counterfeiting threat. We fabricate for the first time an eight FeFET-based circuit block and demonstrate in silico gate-level camouflaging by implementing three Boolean logic functions—NOR, NAND, and XNOR—in the same circuit topology using threshold voltage (VT) programming of FeFET. We perform circuit simulations using empirically calibrated models to estimate power, latency, and area overhead. Compared with recent proposal of programmable camouflage logic using hot-carrier injection (HCI), FeFET offers an overhead reduction of 2×, 1.8×, and 2.8× in area, power, and delay, respectively. [ABSTRACT FROM AUTHOR]

Background: Idiopathic generalized epilepsy is defined as seizures with a possible hereditary predisposition without an underlying cause or structural pathology. Assessment of executive dysfunction in idiopathic generalized epilepsies based on standard Indian battery is not available in the literature. Aims and Objectives: To assess specific executive functions affected in patients with idiopathic epilepsy and their association with various variables. Materials and Methods: Type of observational cross-sectional study, where clinical profile of all idiopathic epilepsy patients attending the neurology OPD was studied and their executive higher mental functions were assessed using the NIMHANS battery. Results: A total of 75 idiopathic generalized epilepsy patients were included in the study. Executive functions that were commonly found abnormal in our study were word fluency (P = .001), category fluency (P < .001), verbal n-back (P < .001), Tower of London (p < 0.01), and Stroop test (P < 0.01). Executive functions showed a significant correlation with age at symptom onset, duration of epilepsy, and in those with uncontrolled seizures. Conclusion: Patients of idiopathic generalized epilepsy according to the present study were found to have significant executive dysfunction in multiple domains. This necessitates the screening for executive dysfunctions, which if detected should prompt the clinician to initiate cognitive retraining. [ABSTRACT FROM AUTHOR]

The two possible pathways toward artificial intelligence (AI)—(i) neuroscience-oriented neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science driven machine learning (like deep learning) differ widely in their fundamental formalism and coding schemes (Pei et al., 2019). Deviating from traditional deep learning approach of relying on neuronal models with static nonlinearities, SNNs attempt to capture brain-like features like computation using spikes. This holds the promise of improving the energy efficiency of the computing platforms. In order to achieve a much higher areal and energy efficiency compared to today's hardware implementation of SNN, we need to go beyond the traditional route of relying on CMOS-based digital or mixed-signal neuronal circuits and segregation of computation and memory under the von Neumann architecture. Recently, ferroelectric field-effect transistors (FeFETs) are being explored as a promising alternative for building neuromorphic hardware by utilizing their non-volatile nature and rich polarization switching dynamics. In this work, we propose an all FeFET-based SNN hardware that allows low-power spike-based information processing and co-localized memory and computing (a.k.a. in-memory computing). We experimentally demonstrate the essential neuronal and synaptic dynamics in a 28 nm high-K metal gate FeFET technology. Furthermore, drawing inspiration from the traditional machine learning approach of optimizing a cost function to adjust the synaptic weights, we implement a surrogate gradient (SG) learning algorithm on our SNN platform that allows us to perform supervised learning on MNIST dataset. As such, we provide a pathway toward building energy-efficient neuromorphic hardware that can support traditional machine learning algorithms. Finally, we undertake synergistic device-algorithm co-design by accounting for the impacts of device-level variation (stochasticity) and limited bit precision of on-chip synaptic weights (available analog states) on the classification accuracy. [ABSTRACT FROM AUTHOR]

Integrated recording of neural electrical potentials from the brain poses great challenges due to stringent dynamic range requirements to resolve small-signal amplitudes buried in noise amidst large artifact and stimulation transients, as well as stringent power and volume constraints to enable minimally invasive untethered operation. Here, we present a 16-channel neural recording system-on-chip with greater than 90-dB input dynamic range and less than 1- $\mu \text{V}_{\mathrm {rms}}$ input-referred noise from dc to 500 Hz, at 0.8- $\mu \text{W}$ power consumption, and 0.024-mm2 area per channel in a 65-nm CMOS process. Each recording channel features a hybrid analog–digital second-order oversampling analog-to-digital converter (ADC), with the biopotential signal coupling directly to the second integrator for high conversion gain and dynamic offset subtraction in the digital domain. This bypasses the need for high-pass filtering pre-amplification in neural recording systems, which often leads to signal distortion. The integrated ADC-direct neural recording offers record figure-of-merit with a noise efficiency factor (NEF) of the combined front end and ADC of 1.81, and a corresponding power efficiency factor (PEF) of 2.6. Predictive digital autoranging of the binary quantizer further supports rapid transient recovery while maintaining fully dc-coupled operation. Hence, the neural ADC is capable of recording ${\le}$ 0.01-Hz slow potentials as well as recovering from $\ge$ 200-mVpp transients within ${\le}$ 1 ms that are important prerequisites to effective electrocortical recording for brain activity mapping. In vivo recordings from marmoset primate frontal cortex demonstrate its unique capabilities in resolving ultra-slow local field potentials indicative of subject arousal state. [ABSTRACT FROM AUTHOR]

Abstract: How does foreign direct investment (FDI) affect the wellbeing of the poor? We address this question by analyzing the impacts of FDI on access to potable water. We predict that higher levels of greenfield FDI in water‐intensive sectors slow the rate of access to potable water in developing countries, with these adverse effects conditional on subnational politics. We hypothesize that this is more likely to occur in polities marked by relatively large poor and marginalized populations, where regulatory capture is more likely to occur. To test our intuition, we analyze subnational data on greenfield FDI in India, confirming that multinational investment in “thirsty” manufacturing sectors are negatively associated with improvements in potable water access. We then present a controlled comparison case study of two Indian states, Kerala and Rajasthan, highlighting the political mechanisms conditioning FDI's effects on potable water. [ABSTRACT FROM AUTHOR]

Current large-scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. New memory technologies, such as nanoscale two-terminal resistive switching memory devices, offer a compact, scalable, and low-power alternative that permits on-chip colocated processing and memory in fine-grain distributed parallel architecture. Here, we report the first use of resistive memory devices for implementing and training a restricted Boltzmann machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive phase change memory (PCM) elements trained with a bioinspired variant of the contrastive divergence algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a twofold to tenfold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared with untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the PCM devices, a factor of ~ 150 times lower than the conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations and number of gradual levels in the PCM analog memory devices. [ABSTRACT FROM PUBLISHER]

This brief presents a CMOS charge buffer with femtofarad-range input capacitance for applications in capacitive electropotential sensing. We analyze and verify a feedback mechanism to negate parasitic capacitances seen at the input of a CMOS amplifier. Measurements are presented from a prototype fabricated in 65-nm CMOS occupying an active area of 193 \mu \textm^2 with an efficiency of 6.5 \mu \textW /MHz. Over-the-air measurements validate its applicability to electropotential sensing. [ABSTRACT FROM PUBLISHER]

We present a telemetry IC with a new data modulation scheme for efficient simultaneous transfer of power and backward data over a single inductive link. Data-driven synchronized single-cycle shorting of the secondary LC tank conserves reactive energy while inducing an instantaneous voltage change on the primary side. Contrary to conventional load shift keying modulation, the recovery time of the secondary LC oscillation after shorting improves asymptotically with increasing quality factor of the secondary LC tank. Since quality factor does not reduce the data rate, the LC tank can be simultaneously optimized for power and data telemetry, obviating the conventional tradeoff between power transfer efficiency and data rate. Cyclic ON-OFF keying time-encoded symbol data mapping of the shorting cycle allows transmission of two data bits per four carrier cycles while supporting simultaneous power delivery during at least six nonshorting out of eight half cycles. All timing control signals for rectification and data transmission are generated from a low-power clock recovery comparator and a phased-locked loop. The 0.92 mm2 65 nm CMOS IC delivers up to 11.5 mW power to the load and simultaneously transmits 6.78 Mb/s data while dissipating 64 \mu \textW power. A bit error rate of ≤9.9 \times 10^-8 was measured over a single 1 cm 13.56 MHz inductive link at a data rate of 6.78 Mb/s with a 10 mW load power. [ABSTRACT FROM PUBLISHER]

The future technology migration in access networks compels the development of key innovative transmitters operating at 10 Gb/s around 1550 nm and capable of transmitting data in extended reach passive optical networks (>60 km). A laser modulated directly appears to be a low cost and a simple solution to address these needs. However, the extinction ratio and the distance of transmission are limited by frequency chirping inherent to high bit rate modulation at 1550 nm. In this letter, we demonstrate the monolithic integration of a directly modulated lasers and a ring resonator (RR) and show the possibility to engineer the RR with specific frequency modulation efficiency. The ring is used as an optical eye reshaper, which enables the increase of the extinction ratio above 9 dB. Transmissions up to 65 km with 5.8 dBm of modulated optical power coupled into the fiber are demonstrated, proving the feasibility of our transmitter for the next generation passive optical network stage 2. [ABSTRACT FROM PUBLISHER]

The ultrafast carrier dynamics in a p-doped dash-in-a-well structure at 1.5 µm is experimentally investigated. An analysis of the timescales related to carrier relaxation and escape processes as well as the "dynamical" linewidth enhancement factor is presented and compared with results obtained from similar un-doped materials. Intentional p-doping of the active region results in an enhancement of the intermediate timescale of the gain dynamics associated with phonon-assisted electron capture and a reduction of the α-factor due to increased differential gain. [ABSTRACT FROM AUTHOR]

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. [ABSTRACT FROM AUTHOR]

The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (<100 Hz) with a capacitor of merely 6 fF, which is hosted in an FG metal-oxide-semiconductor field-effect transistor. The FG-LIF neuron also has the advantage of low operation power (<30 pW/spike). Finally, the proposed circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex. [ABSTRACT FROM AUTHOR]