Memristive Computing

Memristive computing refers to the utilization of the memristor, the fourth fundamental passive circuit element, in computational tasks. The existence of the memristor was theoretically predicted in 1971 by Leon O. Chua, but experimentally validated only in 2008 by HP Labs. A memristor is essentially a nonvolatile nanoscale programmable resistor — indeed, memory resistor — whose resistance, or memristance to be precise, is changed by applying a voltage across, or current through, the device. Memristive computing is a new area of research, and many of its fundamental questions still remain open. For example, it is yet unclear which applications would benefit the most from the inherent nonlinear dynamics of memristors. In any case, these dynamics should be exploited to allow memristors to perform computation in a natural way instead of attempting to emulate existing technologies such as CMOS logic. Examples of such methods of computation presented in this thesis are memristive stateful logic operations, memristive multiplication based on the translinear principle, and the exploitation of nonlinear dynamics to construct chaotic memristive circuits. This thesis considers memristive computing at various levels of abstraction. The first part of the thesis analyses the physical properties and the current-voltage behaviour of a single device. The middle part presents memristor programming methods, and describes microcircuits for logic and analog operations. The final chapters discuss memristive computing in largescale applications. In particular, cellular neural networks, and associative memory architectures are proposed as applications that significantly benefit from memristive implementation. The work presents several new results on memristor modeling and programming, memristive logic, analog arithmetic operations on memristors, and applications of memristors. The main conclusion of this thesis is that memristive computing will be advantageous in large-scale, highly parallel mixed-mode processing architectures. This can be justified by the following two arguments. First, since processing can be performed directly within memristive memory architectures, the required circuitry, processing time, and possibly also power consumption can be reduced compared to a conventional CMOS implementation. Second, intrachip communication can be naturally implemented by a memristive crossbar structure.

Charge and spin transport in memristive La0.7Sr0.3Mno3/SrTiO3/Co devices

Il lavoro svolto si concentra sul trasporto di carica e spin in dispositivi trilayer La0.7Sr0.3MnO3/SrTiO3/Co multifunzionali. Questi dispositivi mostrano sia magnetoresistenza che resistive switching, con un'interessante interazione fra i due effetti. Le giunzioni SrTiO3 sono state scelte per questo lavoro sia per via dei precedenti studi su SrTiO3 come barriera in dispositivi spintronici (cioè dispositivi con magnetoresistenza), sia perché sono promettenti come materiale base per costruire memristor (cioè dispositivi con resistive switching). Il lavoro di tesi è stato svolto all'Istituto per lo studio dei materiali nanostrutturati (ISMN-CNR) a Bologna. Nella prima parte di questa tesi illustrerò la fisica dietro al resistive switching e alla magnetoresistenza di dispositivi trilayer, mostrando anche risultati di studi su dispositivi simili a quelli da me studiati. Nella seconda parte mostrerò la complessa fisica degli ossidi utilizzati nei nostri dispositivi e i possibili meccanismi di trasporto attraverso essi. Nell'ultima parte descriverò i risultati ottenuti. I dispositivi La0.7Sr0.3MnO3/SrTiO3/Co sono stati studiati tramite caratterizzazione elettrica, di magnetotrasporto e con spettroscopia di impedenza. Le misure ottenute hanno mostrato una fisica molto ricca dietro al trasporto di spin e carica in questi dispositivi, e la mutua interazione fra fenomeni spintronici e di resistive switching rappresenta una chiave per comprendere la fisica di questi fenomeni. Analisi dati della dipendenza della resistenza della temperature e caratteristiche corrente-tensioni saranno usati per quantificare e descrivere il trasporto in questi dispositivi.

High On/Off ratio memristive switching of manganite/cuprate bilayer by interfacial magnetoelectricity

Memristive switching serves as the basis for a new generation of electronic devices. Memristors are two-terminal devices in which the current is turned on and off by redistributing point defects, e.g., vacancies, which is difficult to control. Memristors based on alternative mechanisms have been explored, but achieving both the high On/Off ratio and the low switching energy desirable for use in electronics remains a challenge. Here we report memristive switching in a La_(0.7)Ca_(0.3)MnO_(3)/PrBa_(2)Cu_(3)O_(7) bilayer with an On/Off ratio greater than 103 and demonstrate that the phenomenon originates from a new type of interfacial magnetoelectricity. Using results from firstprinciples calculations, we show that an external electric-field induces subtle displacements of the interfacial Mn ions, which switches on/off an interfacial magnetic “dead” layer, resulting in memristive behavior for spin-polarized electron transport across the bilayer. The interfacial nature of the switching entails low energy cost about of a tenth of atto Joule for write/erase a “bit”. Our results indicate new opportunities for manganite/cuprate systems and other transition-metal-oxide junctions in memristive applications.

New insight on bio-sensing by nano-fabricated memristors

We report here on a new insight for bio- sensing based on the memristive effect of functional- ized Schottky-barrier memristive silicon nanowire in dry environment. The device concept is discussed. Elec- trical measurements confirm the bio-detection by the narrowing of the memristive Ids − Vds hysteresis upon interaction of antigen with antibody-functionalized nanowire.

TinO2n-1 Magneli phases studied using density functional theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Building memristor applications: from device model to circuit design

Since the memristor was first built in 2008 at HP Labs, no end of devices and models have been presented. Also, new applications appear frequently. However, the integration of the device at the circuit level is not straightforward, because available models are still immature and/or suppose high computational loads, making their simulation long and cumbersome. This study assists circuit/systems designers in the integration of memristors in their applications, while aiding model developers in the validation of their proposals. We introduce the use of a memristor application framework to support the work of both the model developer and the circuit designer. First, the framework includes a library with the best-known memristor models, being easily extensible with upcoming models. Systematic modifications have been applied to these models to provide better convergence and significant simulations speedups. Second, a quick device simulator allows the study of the response of the models under different scenarios, helping the designer with the stimuli and operation time selection. Third, fine tuning of the device including parameters variations and threshold determination is also supported. Finally, SPICE/Spectre subcircuit generation is provided to ease the integration of the devices in application circuits. The framework provides the designer with total control overconvergence, computational load, and the evolution of system variables, overcoming usual problems in the integration of memristive devices.

Comment: Is memristor a dynamic element?

The authors present a charge/flux formulation of the equations of memristive circuits, which seemingly show that the memristor should not be considered as a dynamic circuit element. Here, is shown that this approach implicitly reduces the dynamic analysis to a certain subset of the state space in such a way that the dynamic contribution of memristors is hidden. This reduction might entail a substantial loss of information, regarding e.g. the local stability properties of the circuit. Two examples illustrate this. It is concluded that the memristor, even with its unconventional features, must be considered as a dynamic element.