arXiv subjects

Ilia Valov

Publications and source records attributed to Ilia Valov.

8 recordsLinked to original sources

arXiv2025

Intermediate Resistive State in Wafer-Scale MoS${_2}$ Memristors through Lateral Silver Filament Growth for Artificial Synapse Applications

Memristors based on two-dimensional materials (2DMs) have garnered significant attention due to their fast resistive switching (RS) behavior and atomic-level thickness, which enables low power consumption, making them promising candidates for neuromorphic computing. Among these, memristors based on molybdenum disulfide (MoS${_2}$) have been extensively studied. Their RS has been attributed to the formation and rupture of conductive filaments (CFs). However, the underlying mechanism of filament formation remains underexplored, and the inherently stochastic nature of RS leads to high variability and limited reproducibility. Additionally, the lack of scalable fabrication techniques for 2DM-based memristors restricts their integration into standard semiconductor technology. Here, we demonstrate memristors based on metal-organic chemical vapor-deposited MoS${_2}$ on the wafer-scale. Our devices exhibit volatile and nonvolatile RS behavior, tunable by modulating the current compliance. Notably, we observe stable RS characteristics in an intermediate resistive state (IRS), featuring set and reset voltages within $\pm$1 V, an endurance exceeding 2500 cycles in direct current mode, and a state retention over 10${^6}$ s. The experimental data, complemented with simulations, suggest that the IRS originates from the lateral growth of the CF within the MoS${_2}$ layer. Furthermore, the devices successfully emulate synaptic plasticity with current responses on the microsecond timescale, highlighting their potential for large-scale integration in neuromorphic computing architectures.

physics.app-ph↗

arXiv2024

Roadmap to Neuromorphic Computing with Emerging Technologies

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.

eess.SP↗

arXiv2022

2022 Roadmap on Neuromorphic Computing and Engineering

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

cs.ET↗

arXiv2019

Self-organizing memristive nanowire networks with structural plasticity emulate biological neuronal circuits

Acting as artificial synapses, two-terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Organized into large arrays with a top-down approach, memristive devices in conventional crossbar architecture demonstrated the implementation of brain-inspired computing for supervised and unsupervised learning. Alternative way using unconventional systems consisting of many interacting nano-parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits. However, these systems were unable to demonstrate bio-realistic implementation of synaptic functionalities with spatio-temporal processing of input signals similarly to our brain. Here we report on emergent synaptic behavior of biologically inspired nanoarchitecture based on self-assembled and highly interconnected nanowire (NW) networks realized with a bottom up approach. The operation principle of this system is based on the mutual electrochemical interaction among memristive NWs and NW junctions composing the network and regulating its connectivity depending on the input stimuli. The functional connectivity of the system was shown to be responsible for heterosynaptic plasticity that was experimentally demonstrated and modelled in a multiterminal configuration, where the formation of a synaptic pathway between two neuron terminals is responsible for a variation in synaptic strength also at non-stimulated terminals. These results highlight the ability of nanowire memristive architectures for building brain-inspired intelligent systems based on complex networks able to physically compute the information arising from multi-terminal inputs.

physics.app-ph↗

arXiv2019

Design of materials properties and device performance in memristive systems

Future development of the modern nanoelectronics and its flagships internet of things and artificial intelligence as well as many related applications is largely associated with memristive elements. This technology offers a broad spectrum of functionalities, however, it follows predominantly a phenomenological approach and crucial challenge/limit for further development remains variability and lack of fundamental materials' design strategy. Here we demonstrate the vital importance of materials' purity for determining memristors' functionalities, showing that part per million foreign elements significantly change the performance. By appropriate choice of chemistry and amount of doping material we can selectively enhance desired operation mode. We highlight how dopant dependent structure and charge/potential distribution in the space charge layers and the cell capacitance determine the device kinetics and functions. We evidence for first time experimentally the relation between materials properties and switching/neuromorphic performance, thus providing rules and directions for a rational design of memristive devices.

cond-mat.mtrl-sci↗

arXiv2017

Self-Assembling Oxide Catalyst for Electrochemical Water Splitting

Renewable energy conversion and storage, and greenhouse gas emission-free technologies are within the primary tasks and challenges for the society. Hydrogen fuel, produced by alkaline water electrolysis is fulfilling all these demands, however the technology is economically feeble, limited by the slow rate of oxygen evolution reaction. Complex metal oxides were suggested to overcome this problem being low-cost efficient catalysts. However, the insufficient long-term stability, degradation of structure and electrocatalytic activity are restricting their utilization. Here we report on a new perovskite-based self-assembling material BaCo0.98Ti0.02O3-$δ$:Co3O4 with superior performance, showing outstanding properties compared to current state-of-the-art materials without degeneration of its properties even at 353 K. By chemical and structural analysis the degradation mechanism was identified and modified by selective doping. Short-range order and chemical composition rather than long-range order are factors determining the outstanding performance. The derived general design rules can be used for further development of oxide-based electrocatalytic materials.

cond-mat.mtrl-sci↗

arXiv2017

Multibit memory operation of metal-oxide bi-layer memristors

In this work, we evaluate a multitude of metal-oxide bi-layers and demonstrate the benefits from increased memory stability via multibit memory operation. We introduce a programming methodology that allows for operating metal-oxide memristive devices as multibit memory elements with highly packed yet clearly discernible memory states. We finally demonstrate a 5.5-bit memory cell (47 resistive states) with excellent retention and power consumption performance. This paves the way for neuromorphic and non-volatile memory applications.

cond-mat.mes-hall↗

arXiv2013

Nanobatteries in redox-based resistive switches require extension of memristor theory

Redox-based nanoionic resistive memory cells (ReRAMs) are one of the most promising emerging nano-devices for future information technology with applications for memory, logic and neuromorphic computing. Recently, the serendipitous discovery of the link between ReRAMs and memristors and memristive devices has further intensified the research in this field. Here we show on both a theoretical and an experimental level that nanoionic-type memristive elements are inherently controlled by non-equilibrium states resulting in a nanobattery. As a result the memristor theory must be extended to fit the observed non zerocrossing I-V characteristics. The initial electromotive force of the nanobattery depends on the chemistry and the transport properties of the materials system but can also be introduced during ReRAM cell operations. The emf has a strong impact on the dynamic behaviour of nanoscale memories, and thus, its control is one of the key factors for future device development and accurate modelling.

cond-mat.mtrl-sci↗