54 research outputs found
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Mathematical simulation of memristive for classification in machine learning
Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution
Shortest path computing in directed graphs with weighted edges mapped on random networks of memristors
Electronic version of an article published as [Fernandez, Carlos, Ioannis Vourkas, and Antonio Rubio. "Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors." Parallel Processing Letters 30.01 (2020): 2050002] [https://doi.org/10.1142/S0129626420500024] © [copyright World Scientific Publishing Company] [https://www.worldscientific.com/worldscinet/ppl]To accelerate the execution of advanced computing tasks, in-memory computing with resistive memory provides a promising solution. In this context, networks of memristors could be used as parallel computing medium for the solution of complex optimization problems. Lately, the solution of the shortest-path problem (SPP) in a two-dimensional memristive grid has been given wide consideration. Some still open problems in such computing approach concern the time required for the grid to reach to a steady state, and the time required to read the result, stored in the state of a subset of memristors that represent the solution. This paper presents a circuit simulation-based performance assessment of memristor networks as SPP solvers. A previous methodology was extended to support weighted directed graphs. We tried memristor device models with fundamentally different switching behavior to check their suitability for such applications and the impact on the timely detection of the solution. Furthermore, the requirement of binary vs. analog operation of memristors was evaluated. Finally, the memristor network-based computing approach was compared to known algorithmic solutions to the SPP over a large set of random graphs of different sizes and topologies. Our results contribute to the proper development of bio-inspired memristor network-based SPP solvers.This work was supported by the Chilean research grants CONICYT REDES ETAPA INICIAL Convocatoria 2017 No. REDI170604, CONICYT BASAL FB0008, and by the Spanish MINECO and ERDF (TEC2016-75151-C3-2-R).Peer ReviewedPostprint (author's final draft
Low-power emerging memristive designs towards secure hardware systems for applications in internet of things
Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs
Resistive switching in ALD metal-oxides with engineered interfaces
L'abstract è presente nell'allegato / the abstract is in the attachmen
A thorough investigation of the switching dynamics of TiN/Ti/10 nm-HfO2/W resistive memories
The switching dynamics of TiN/Ti/HfO2/W-based resistive memories is investigated. The analysis consisted in
the systematic application of voltage sweeps with different ramp rates and temperatures. The obtained results
give clear insight into the role played by transient and thermal effects on the device operation. Both kinetic
Monte Carlo simulations and a compact modeling approach based on the Dynamic Memdiode Model are
considered in this work with the aim of assessing, in terms of their respective scopes, the nature of the physical
processes that characterize the formation and rupture of the filamentary conducting channel spanning the oxide
film. As a result of this study, a better understanding of the different facets of the resistive switching dynamics is
achieved. It is shown that the temperature and, mainly, the applied electric field, control the switching mechanism
of our devices. The Dynamic Memdiode Model, being a behavioral analytic approach, is shown to be
particularly suitable for reproducing the conduction characteristics of our devices using a single set of parameters
for the different operation regimesFEDER program [PID2022-139586NB-C41, PID2022-
139586NB-C42PID2022-139586NB-C43PID2022-139586NB-C44]The Consejería de Conocimiento, Investigaci´on y UniversidadJunta de
Andalucía (Spain) [B-TIC-624-UGR20]Spanish Consejo Superior
de Investigaciones Científicas (CSIC) [20225AT012]FEDER fundsRamón y Cajal grant number
RYC2020-030150-IEuropean project MEMQuD, code 20FUN06EMPIR programme co-financed by the Participating StatesEuropean Union’s Horizon 2020 research and innovation
programm
A thorough investigation of the switching dynamics of TiN/Ti/10 nm-HfO2/W resistive memories
Producción CientíficaThe switching dynamics of TiN/Ti/HfO2/W-based resistive memories is investigated. The analysis consisted in the systematic application of voltage sweeps with different ramp rates and temperatures. The obtained results give clear insight into the role played by transient and thermal effects on the device operation. Both kinetic Monte Carlo simulations and a compact modeling approach based on the Dynamic Memdiode Model are considered in this work with the aim of assessing, in terms of their respective scopes, the nature of the physical processes that characterize the formation and rupture of the filamentary conducting channel spanning the oxide film. As a result of this study, a better understanding of the different facets of the resistive switching dynamics is achieved. It is shown that the temperature and, mainly, the applied electric field, control the switching mechanism of our devices. The Dynamic Memdiode Model, being a behavioral analytic approach, is shown to be particularly suitable for reproducing the conduction characteristics of our devices using a single set of parameters for the different operation regimes.Ministerio de Ciencia e Innovación de España - FEDER [PID2022-139586NB-C41, PID2022-139586NB-C42, PID2022-139586NB-C43, PID2022-139586NB-C44]Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía [B-TIC-624-UGR20]Consejo Superior de Investigaciones Científicas (CSIC)- FEDER [20225AT012]Ramón y Cajal grant number RYC2020-030150-IEuropean project MEMQuD (code 20FUN06) which has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme
Fabrication and Pseudo-Analog Characteristics of Ta2O5 -Based ReRAM Cell
Memristori on yksi elektroniikan peruskomponenteista vastuksen, kondensaattorin ja kelan lisäksi. Se on passiivinen komponentti, jonka teorian kehitti Leon Chua vuonna 1971. Kesti kuitenkin yli kolmekymmentä vuotta ennen kuin teoria pystyttiin yhdistämään kokeellisiin tuloksiin. Vuonna 2008 Hewlett Packard julkaisi artikkelin, jossa he väittivät valmistaneensa ensimmäisen toimivan memristorin.
Memristori eli muistivastus on resistiivinen komponentti, jonka vastusarvoa pystytään muuttamaan. Nimens mukaisesti memristori kykenee myös säilyttämään vastusarvonsa ilman jatkuvaa virtaa ja jännitettä. Tyypillisesti memristorilla on vähintään kaksi vastusarvoa, joista kumpikin pystytään valitsemaan syöttämällä komponentille jännitettä tai virtaa. Tämän vuoksi memristoreita kutsutaankin usein resistiivisiksi kytkimiksi.
Resistiivisiä kytkimiä tutkitaan nykyään paljon erityisesti niiden mahdollistaman muistiteknologian takia. Resistiivisistä kytkimistä rakennettua muistia kutsutaan ReRAM-muistiksi (lyhenne sanoista resistive random access memory). ReRAM-muisti on Flash-muistin tapaan haihtumaton muisti, jota voidaan sähköisesti ohjelmoida tai tyhjentää. Flash-muistia käytetään tällä hetkellä esimerkiksi muistitikuissa. ReRAM-muisti mahdollistaa kuitenkin nopeamman ja vähävirtaiseman toiminnan Flashiin verrattuna, joten se on tulevaisuudessa varteenotettava kilpailija markkinoilla.
ReRAM-muisti mahdollistaa myös useammin bitin tallentamisen yhteen muistisoluun binäärisen (”0” tai ”1”) toiminnan sijaan. Tyypillisesti ReRAM-muistisolulla on kaksi rajoittavaa vastusarvoa, mutta näiden kahden tilan välille pystytään mahdollisesti ohjelmoimaan useampia tiloja. Muistisoluja voidaan kutsua analogisiksi, jos tilojen määrää ei ole rajoitettu. Analogisilla muistisoluilla olisi mahdollista rakentaa tehokkaasti esimerkiksi neuroverkkoja. Neuroverkoilla pyritään mallintamaan aivojen toimintaa ja suorittamaan tehtäviä, jotka ovat tyypillisesti vaikeita perinteisille tietokoneohjelmille. Neuroverkkoja käytetään esimerkiksi puheentunnistuksessa tai tekoälytoteutuksissa.
Tässä diplomityössä tarkastellaan Ta2O5 -perustuvan ReRAM-muistisolun analogista toimintaa pitäen mielessä soveltuvuus neuroverkkoihin. ReRAM-muistisolun valmistus ja mittaustulokset käydään läpi. Muistisolun toiminta on harvoin täysin analogista, koska kahden rajoittavan vastusarvon välillä on usein rajattu määrä tiloja. Tämän vuoksi toimintaa kutsutaan pseudoanalogiseksi. Mittaustulokset osoittavat, että yksittäinen ReRAM-muistisolu kykenee binääriseen toimintaan hyvin. Joiltain osin yksittäinen solu kykenee tallentamaan useampia tiloja, mutta vastusarvoissa on peräkkäisten ohjelmointisyklien välillä suurta vaihtelevuutta, joka hankaloittaa tulkintaa. Valmistettu ReRAM-muistisolu ei sellaisenaan kykene toimimaan pseudoanalogisena muistina, vaan se vaati rinnalleen virtaa rajoittavan komponentin. Myös valmistusprosessin kehittäminen vähentäisi yksittäisen solun toiminnassa esiintyvää varianssia, jolloin sen toiminta muistuttaisi enemmän pseudoanalogista muistia.The memristor is one of the fundamental circuit elements in addition to a resistor, capacitor and an inductor. It is a passive component whose theory was postulated by Leon Chua in 1971. It took over 30 years before any known physical examples were discovered. In 2008 Hewlett Packard published an article where they manufactured a device which they claimed to be the first memristor found.
The memristor, which is a concatenation of memory resistor, is a resistive component that has an ability to change its resistance. It can also remember its resistance value without continuous current or voltage. Typically, a memristor has at least two resistance states that can be altered. This is the reason why memristors are also called resistive switches.
Resistive switches can be used in memory technologies. A memory array that has been built using resistive switches is called ReRAM (resistive random access memory). ReRAM, like Flash memory, is a non-volatile memory that can be programmed or erased electrically. Flash memories are currently used e.g. in memory sticks. However, compared to Flash, ReRAM has faster operating speed and lower power consumption, for instance. It could potentially replace current memory standards in future.
A ReRAM memory cell can also store multiple bits instead of binary operation (”0” or ”1”). Typically there exists multiple intermediate resistance states between ReRAM’s limiting resistances that could be utilized. Such memory could be called analog, if the amount of intermediate states is not limited to discrete levels. Analog memories make it possible to build artificial neural networks (ANN) efficiently, for instance. ANNs try to model the behaviour of brain and to perform tasks that are difficult for traditional computer programs such as speech recognition or artificial intelligence.
This thesis studies the analog behaviour of Ta 2 O 5 -based ReRAM cell. Manufacturing process and measurement results are presented. The operation of ReRAM cell is rarely fully analog as there exists limited amount of intermediate resistance states. This is the reason why operation is called pseudo-analog. Measurement results show that a single ReRAM cell is suitable for binary operation. In some cases, a single cell can store multiple resistance values but there exists significant variance in resistance states between subsequent programming cycles. The proposed ReRAM cell cannot operate as pseudo-analog ReRAM cell in itself as it needs an external current limiting component. Improving the manufacturing process should reduce the variability such that the operation would be more like a pseudo-analog memory.Siirretty Doriast
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