3D resistive RAM cell design for high-density storage class memory—a review

In this article, we comprehensively review recent progress in the ReRAM cell technology for 3D integration focusing on a material/device level. First we briefly mention pioneering work on high-density crossbar ReRAM arrays which paved the way to 3D integration. We discuss the two main proposed 3D integration schemes—3D horizontally stacked ReRAM vs 3D Vertical ReRAM and their respective advantages and disadvantages. We follow with the detailed memory cell design on important work in both areas, utilizing either filamentary or interface-limited switching mechanisms. We also discuss our own contributions on HfO₂-based filamentary 3D Vertical ReRAM as well as TaO_x/TiO₂ bilayer-based self-rectifying 3D Vertical ReRAM. Finally, we summarize the present status and provide an outlook for the nearterm future.     

Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first time. The algorithms involve independent agents traversing a dynamically constructed directed graph that possibly has loops and multiple edges. A rather elegant and profound category-theoretic model of cognition in biological neural networks developed by a well-known French mathematician, professor Andree Ehresmann jointly with a neurosurgeon, Jan Paul Vanbremeersch over the last thirty years provides a hint at the connection between such algorithms and Hebbian learning.     

Influence of grain boundary weak links on the nonequilibrium response of YBaCuO thin films to short laser pulses

The transient voltage response in both epitaxial and granular YBaCuO thin films to 80 ps pulses of YAGNd laser radiation of wavelength 0.63 and 1.54 m was studied. In the normal and resistive states both types of films demonstrate two components: a nonequilibrium picosecond component and a bolometric nanosecond one. The normalized amplitudes are almost the same for all films. In the superconducting state we observed a kinetic inductive response and two-component shape after integration. The normalized amplitude of the response in granular films is up to five orders of magnitude larger than in epitaxial films. We interpret the nonequilibrium response in terms of a suppression of the order parameter by the excess of quasiparticles followed by the change of resistance in the normal and resistive states or kinetic inductance in the superconducting state. The sharp rise of inductive response in granular films is explained both by a diminishing of the cross section for current percolation through the disordered network of Josephson weak links and by a decrease of condensate density in neighboring regions.     

Highly Exposed ?NH2 Edge on Fragmented g-C3N4 Framework with Integrated Molybdenum Atoms for Catalytic CO2 Cycloaddition: DFT and Techno-Economic Assessment

This study focuses on the applicability of single-atom Mo-doped graphitic carbon nitride (GCN) nanosheets which are specifically engineered with high surface area (exfoliated GCN),  NH₂ rich edges, and maximum utilization of isolated atomic Mo for propylene carbonate (PC) production through CO₂ cycloaddition of propylene oxide (PO). Various operational parameters are optimized, for example, temperature (130 °C), pressure (20 bar), catalyst (Mo₂GCN), and catalyst mass (0.1 g). Under optimal conditions, 2% Mo-doped GCN (Mo₂GCN) has the highest catalytic performance, especially the turnover frequency (TOF) obtained, 36.4 h⁻¹ is higher than most reported studies. DFT simulations prove the catalytic performance of Mo₂GCN significantly decreases the activation energy barrier for PO ring-opening from 50–60 to 4.903 kcal mol⁻¹. Coexistence of Lewis acid/base group improves the CO₂ cycloaddition performance by the formation of coordination bond between electron-deficient Mo atom with O atom of PO, while  NH₂ surface group disrupts the stability of CO₂ bond by donating electrons into its low-level empty orbital. Steady-state process simulation of the industrial-scale consumes 4.4 ton h⁻¹ of CO₂ with PC production of 10.2 ton h⁻¹. Techno-economic assessment profit from Mo₂GCN is estimated to be 60.39 million USD year⁻¹ at a catalyst loss rate of 0.01 wt% h⁻¹.     

Properties of Gauss Digitized Shapes and Digital Surface Integration

This paper presents new topological and geometric properties of Gauss digitizations of Euclidean shapes, most of them holding in arbitrary dimension d. We focus on r-regular shapes sampled by Gauss digitization at gridstep h. The digitized boundary is shown to be close to the Euclidean boundary in the Hausdorff sense, the minimum distance \(\frac{\sqrt{d}}{2}h\) being achieved by the projection map \(\xi \) induced by the Euclidean distance. Although it is known that Gauss digitized boundaries may not be manifold when \(d \ge 3\), we show that non-manifoldness may only occur in places where the normal vector is almost aligned with some digitization axis, and the limit angle decreases with h. We then have a closer look at the projection of the digitized boundary onto the continuous boundary by \(\xi \). We show that the size of its non-injective part tends to zero with h. This leads us to study the classical digital surface integration scheme, which allocates a measure to each surface element that is proportional to the cosine of the angle between an estimated normal vector and the trivial surface element normal vector. We show that digital integration is convergent whenever the normal estimator is multigrid convergent, and we explicit the convergence speed. Since convergent estimators are now available in the literature, digital integration provides a convergent measure for digitized objects.     

Generating heavy particles with energy and momentum conservation

We propose a novel algorithm, called REGGAE, for the generation of momenta of a given sample of particle masses, evenly distributed in Lorentz-invariant phase space and obeying energy and momentum conservation. In comparison to other existing algorithms, REGGAE is designed for the use in multiparticle production in hadronic and nuclear collisions where many hadrons are produced and a large part of the available energy is stored in the form of their masses. The algorithm uses a loop simulating multiple collisions which lead to production of configurations with reasonably large weights.

Program summary

Program title: REGGAE (REscattering-after-Genbod GenerAtor of Events)Catalogue identifier: AEJR_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJR_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 1523No. of bytes in distributed program, including test data, etc.: 9608Distribution format: tar.gzProgramming language: C++Computer: PC Pentium 4, though no particular tuning for this machine was performed.Operating system: Originally designed on Linux PC with g++, but it has been compiled and ran successfully on OS X with g++ and MS Windows with Microsoft Visual C++ 2008 Express Edition, as well.RAM: This depends on the number of particles which are generated. For 10 particles like in the attached example it requires about 120 kB.Classification: 11.2Nature of problem: The task is to generate momenta of a sample of particles with given masses which obey energy and momentum conservation. Generated samples should be evenly distributed in the available Lorentz-invariant phase space.Solution method: In general, the algorithm works in two steps. First, all momenta are generated with the GENBOD algorithm. There, particle production is modeled as a sequence of two-body decays of heavy resonances. After all momenta are generated this way, they are reshuffled. Each particle undergoes a collision with some other partner such that in the pair center of mass system the new directions of momenta are distributed isotropically. After each particle collides only a few times, the momenta are distributed evenly across the whole available phase space. Starting with GENBOD is not essential for the procedure but it improves the performance.Running time: This depends on the number of particles and number of events one wants to generate. On a LINUX PC with 2 GHz processor, generation of 1000 events with 10 particles each takes about 3 s.     

An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range

In this paper, we present an automatic C-code generation strategy for real-time nonlinear model predictive control (NMPC), which is designed for applications with kilohertz sample rates. The corresponding code export module has been implemented within the software package ACADO Toolkit. It is capable of exporting fixed step-size integrators together with their sensitivities as well as a real-time Gauss–Newton method. Here, we employ the symbolic representation of optimal control problems in ACADO in order to auto-generate plain C-code which is optimized for final production. The exported code has been tested for model predictive control scenarios comprising constrained nonlinear dynamic systems with four states and a control horizon of ten samples. The numerical simulations show a promising performance of the exported code being able to provide feedback in much less than a millisecond.     

Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots

This paper proposes an approach that solves the Robot Localization problem by using a conditional state-transition Hidden Markov Model (HMM). Through the use of Self Organized Maps (SOMs) a Tolerant Observation Model (TOM) is built, while odometer-dependent transition probabilities are used for building an Odometer-Dependent Motion Model (ODMM). By using the Viterbi Algorithm and establishing a trigger value when evaluating the state-transition updates, the presented approach can easily take care of Position Tracking (PT), Global Localization (GL) and Robot Kidnapping (RK) with an ease of implementation difficult to achieve in most of the state-of-the-art localization algorithms. Also, an optimization is presented to allow the algorithm to run in standard microprocessors in real time, without the need of huge probability gridmaps.     

2D structured grid generation method producing a mesh with prescribed properties near boundary

A variational method of generating a structured mesh on a two-dimensional domain is considered. To this end, a quasiconformal mapping of the parametric domain with a given Cartesian mesh onto the underlying physical domain is used. The functions implementing the mapping are sought by solving the Dirichlet problem for the system of elliptic second-order partial differential equations. An additional control for the cell shape is executed by introducing a local mapping which induces a control metric. In some particular cases, instead of an additional local mapping, a global mapping of the parametric domain onto the intermediate domain is used, where the curvilinear mesh is produced, and next this domain is mapped onto the underlying physical domain. The control metric allows to obtain a mesh with required properties: grid line orthogonality and prescribed mesh point clustering near the domain boundary. Examples of mesh in the annulus and near airfoil are presented.