Understanding the Reliability of Ferroelectric Tunnel Junction Operations using an Advanced Small-Signal Model

Ferroelectric technology is becoming ever more appealing for a variety of applications, especially analog neuromorphic computing. In this respect, elucidating the physical mechanisms occurring during device operation is of key importance to improve the reliability of ferroelectric devices. In this work, we investigate ferroelectric tunnel junctions (FTJs) consisting of a ferroelectric hafnium zirconium oxide (HZO) layer and an alumina (Al 2 O 3 ) layer by means of C-f and G-f measurements performed at multiple voltages and temperatures. For a dependable interpretation of the results, a new small signal model is introduced that goes beyond the state of the art by i) separating the role of the leakage in the two layers; ii) including the significant impact of the series impedance (that depends on the samples layout); iii) including the frequency dependence of the dielectric permittivity; iv) accounting for the fact that likely not the whole HZO volume crystallizes in the orthorhombic ferroelectric phase. The model correctly reproduces measurements taken on different devices in different conditions. Results highlight that the typical estimation method for interface trap density may be misleading

Nonlinear isocapacitary concepts of mass in nonnegative scalar curvature

We deal with suitable nonlinear versions of Jauregui's Isocapacitary mass in $3$-manifolds with nonnegative scalar curvature and compact outermost minimal boundary. These masses, which depend on a parameter $1<p\leq 2$, interpolate between Jauregui's mass $p=2$ and Huisken's Isoperimetric mass, as $p \to 1^+$. We derive Positive Mass Theorems for these masses under mild conditions at infinity, and we show that these masses do coincide with the $\mathrm{ADM}$ mass when the latter is defined. We finally work out a nonlinear potential theoretic proof of the Penrose Inequality in the optimal asymptotic regime

On the Isoperimetric Riemannian Penrose Inequality

We prove that the Riemannian Penrose Inequality holds for Asymptotically Flat $3$-manifolds with nonnegative scalar curvature and a connected horizon boundary, provided the optimal decay assumptions are met, which result in the $\mathrm{ADM}$ mass being a well-defined geometric invariant. Our proof builds on new asymptotic comparison arguments involving Huisken's Isoperimetric mass and the Hawking mass, as well as a novel interplay between the Hawking mass and a potential-theoretic version of it, recently introduced by Agostiniani, Oronzio and the third named author. As a crucial step in our argument, we establish a Riemannian Penrose Inequality in terms of the Isoperimetric mass, on any $3$-manifold with nonnegative scalar curvature and connected horizon boundary, on which a well posed notion of weak Inverse Mean Curvature Flow is available. In particular, such an Isoperimetric Riemannian Penrose Inequality does not require the asymptotic flatness of the manifold.Comment: 34 page

Biologically Plausible Information Propagation in a CMOS Integrate-and-Fire Artificial Neuron Circuit with Memristive Synapses

Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy efficient bio-inspired mechanisms. While several network architectures have been developed to embed in hardware the bio-inspired learning rules found in the biological brain, such as the Spike Timing Dependent Plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bio-inspired experiments have been reproduced by linking the biological probability of release with the artificial synapses conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysi

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning

Information Transfer in Neuronal Circuits: From Biological Neurons to Neuromorphic Electronics

The advent of neuromorphic electronics is increasingly revolutionizing the concept of computation. In the last decade, several studies have shown how materials, architectures, and neuromorphic devices can be leveraged to achieve brain-like computation with limited power consumption and high energy efficiency. Neuromorphic systems have been mainly conceived to support spiking neural networks that embed bioinspired plasticity rules such as spike time-dependent plasticity to potentially support both unsupervised and supervised learning. Despite substantial progress in the field, the information transfer capabilities of biological circuits have not yet been achieved. More importantly, demonstrations of the actual performance of neuromorphic systems in this context have never been presented. In this paper, we report similarities between biological, simulated, and artificially reconstructed microcircuits in terms of information transfer from a computational perspective. Specifically, we extensively analyzed the mutual information transfer at the synapse between mossy fibers and granule cells by measuring the relationship between pre- and post-synaptic variability. We extended this analysis to memristor synapses that embed rate-based learning rules, thus providing quantitative validation for neuromorphic hardware and demonstrating the reliability of brain-inspired applications

NF-Y activates genes of metabolic pathways altered in cancer cells

The trimeric transcription factor NF-Y binds to the CCAAT box, an element enriched in promoters of genes overexpressed in tumors. Previous studies on the NF-Y regulome identified the general term metabolism as significantly enriched. We dissect here in detail the targeting of metabolic genes by integrating analysis of NF-Y genomic binding and profilings after inactivation of NF-Y subunits in different cell types. NF-Y controls de novo biosynthetic pathways of lipids, teaming up with the master SREBPs regulators. It activates glycolytic genes, but, surprisingly, is neutral or represses mitochondrial respiratory genes. NF-Y targets the SOCG (Serine, One Carbon, Glycine) and Glutamine pathways, as well as genes involved in the biosynthesis of polyamines and purines. Specific cancer-driving nodes are generally under NF-Y control. Altogether, these data delineate a coherent strategy to promote expression of metabolic genes fuelling anaerobic energy production and other anabolic pathways commonly altered in cancer cells

Effect of cycling on ultra-thin HfZrO4, ferroelectric synaptic weights

Two-terminal ferroelectric synaptic weights are fabricated on silicon. The active layers consist of a 2 nm thick WOx film and a 2.7 nm thick HfZrO4 (HZO) film grown by atomic layer deposition. The ultra-thin HZO layer is crystallized in the ferroelectric phase using a millisecond flash at a temperature of only 500 °C, evidenced by x-rays diffraction and electron microscopy. The current density is increased by four orders of magnitude compared to weights based on a 5 nm thick HZO film. Potentiation and depression (analog resistive switching) is demonstrated using either pulses of constant duration (as short as 20 nanoseconds) and increasing amplitude, or pulses of constant amplitude (+/−1 V) and increasing duration. The cycle-to-cycle variation is below 1%. Temperature dependent electrical characterisation is performed on a series of device cycled up to 108 times: they reveal that HZO possess semiconducting properties. The fatigue leads to a decrease, in the high resistive state only, of the conductivity and of the activation energy.ISSN:2634-438