Reliable Multivalued Conductance States in TaOx, Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis

Transition metal oxide-based memristors have widely been proposed for applications toward artificial synapses. In general, memristors have two or more electrically switchable stable resistance states that device researchers see as an analogue to the ion channels found in biological synapses. The mechanism behind resistive switching in metal oxides has been divided into electrochemical metallization models and valence change models. The stability of the resistance states in the memristor vary widely depending on: oxide material, electrode material, deposition conditions, film thickness, and programming conditions. So far, it has been extremely challenging to obtain reliable memristors with more than two stable multivalued states along with endurances greater than similar to 1000 cycles for each of those states. Using an oxygen plasma-assisted sputter deposition method of noble metal electrodes, we found that the metal-oxide interface could be deposited with substantially lower interface roughness observable at the nanometer scale. This markedly improved device reliability and function, allowing for a demonstration of memristors with four completely distinct levels from similar to 6 x 10(-6) to similar to 4 x 10(-8) S that were tested up to 10(4) cycles per level. Furthermore through a unique in situ transmission electron microscopy study, we were able to verify a redox reaction-type model to be dominant in our samples, leading to the higher degree of electrical state controllability. For solid-state synapse applications, the improvements to electrical properties will lead to simple device structures, with an overall power and area reduction of at least 1000 times when compared to SRAM.11Ysciescopu

An exact study of phase transitions in mean field Potts models

We construct the exact partition function of the Potts model on a complete graph subject to external fields with linear and nematic type couplings. The partition function is obtained as a solution to a linear diffusion equation and the free energy, in the thermodynamic limit, follows from its semiclassical limit. Analysis of singularities of the equations of state reveals the occurrence of phase transitions of nematic type at not zero external fields and allows for an interpretation of the phase transitions in terms of shock dynamics in the space of thermodynamics variables. The approach is shown at work in the case of a q-state model for q=3 but the method generalises to arbitrary q.Comment: 4 page, 4 figure

Unsupervised Features Learning for Sampled Vector Fields

In this paper we introduce a new approach to computing hidden features of sampled vector fields. The basic idea is to convert the vector field data to a graph structure and use tools designed for automatic, unsupervised analysis of graphs. Using a few data sets we show that the collected features of the vector fields are correlated with the dynamics known for analytic models which generates the data. In particular the method may be useful in analysis of data sets where the analytic model is poorly understood or not known

Communication Research

Contains reports on eight research projects

Learning Markov Random Fields for Combinatorial Structures via Sampling through Lov\'asz Local Lemma

Learning to generate complex combinatorial structures satisfying constraints will have transformative impacts in many application domains. However, it is beyond the capabilities of existing approaches due to the highly intractable nature of the embedded probabilistic inference. Prior works spend most of the training time learning to separate valid from invalid structures but do not learn the inductive biases of valid structures. We develop NEural Lov\'asz Sampler (Nelson), which embeds the sampler through Lov\'asz Local Lemma (LLL) as a fully differentiable neural network layer. Our Nelson-CD embeds this sampler into the contrastive divergence learning process of Markov random fields. Nelson allows us to obtain valid samples from the current model distribution. Contrastive divergence is then applied to separate these samples from those in the training set. Nelson is implemented as a fully differentiable neural net, taking advantage of the parallelism of GPUs. Experimental results on several real-world domains reveal that Nelson learns to generate 100\% valid structures, while baselines either time out or cannot ensure validity. Nelson also outperforms other approaches in running time, log-likelihood, and MAP scores.Comment: accepted by AAAI 2023. The first two authors contribute equall

Symmetry in Graph Theory

This book contains the successful invited submissions to a Special Issue of Symmetry on the subject of ""Graph Theory"". Although symmetry has always played an important role in Graph Theory, in recent years, this role has increased significantly in several branches of this field, including but not limited to Gromov hyperbolic graphs, the metric dimension of graphs, domination theory, and topological indices. This Special Issue includes contributions addressing new results on these topics, both from a theoretical and an applied point of view

Monotone and near-monotone biochemical networks

Monotone subsystems have appealing properties as components of larger networks, since they exhibit robust dynamical stability and predictability of responses to perturbations. This suggests that natural biological systems may have evolved to be, if not monotone, at least close to monotone in the sense of being decomposable into a “small” number of monotone components, In addition, recent research has shown that much insight can be attained from decomposing networks into monotone subsystems and the analysis of the resulting interconnections using tools from control theory. This paper provides an expository introduction to monotone systems and their interconnections, describing the basic concepts and some of the main mathematical results in a largely informal fashion