Quantum-statistical transport phenomena in memristive computing architectures
Christopher N. Singh, Brian A. Crafton, Mathew P. West, Alex S. Weidenbach, Keith T. Butler, Allan H. MacDonald, Arjit Raychowdury, Eric M. Vogel, W. Alan Dooli… 展开作者
Christopher N. Singh, Brian A. Crafton, Mathew P. West, Alex S. Weidenbach, Keith T. Butler, Allan H. MacDonald, Arjit Raychowdury, Eric M. Vogel, W. Alan Doolittle, L. F. J. Piper, Wei-Cheng Lee
The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has prevented widespread adoption. Here we show coherent electron wave functions play a pivotal role in the nanoscale transport properties of these emerging, non-volatile memories. By characterizing both filamentary and non-filamentary memristive devices as disordered Anderson systems, the switching characteristics and intrinsic variability arise directly from the universality of electron transport in disordered media. Our framework suggests localization phenomena in nanoscale, solid-state memristive systems are directly linked to circuit level performance. We discuss how quantum conductance fluctuations in the active layer set a lower bound on device variability. This finding implies there is a fundamental quantum limit on the reliability of memristive devices, and electron coherence will play a decisive role in surpassing or maintaining Moore's Law with these systems.
In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al$_2$O$_3$/Nb$_{\text{x}}$O$_{\text{y}}$/Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin Nb$_{\text{x}}$O$_{\text{y}}$ solid state electrolyte between an Al$_2$O$_3$ tunnel barrier and a semiconductor metal interface at an Au electrode. It is shown that the device provides a number of interesting features for potential applications such as an intrinsic current compliance, a relatively long…
We suggest a novel methodology to obtain a digital representation of analog signals and to perform its back-conversion using memristive devices. In the proposed converters, the same memristive systems are used for two purposes: as elements performing conversion and elements storing the code. This approach to conversion is particularly relevant for interfacing analog signals with memristive digital logic/computing circuits.
Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient1-3. In such systems, memristors represent the native electronic analogues of the biological synapses. However, the characteristics of the existing memristors do not fully support the key requirements of synaptic connections: high density, adjustable weight, and low energy operation. Here we show a bilayer memristor that is forming-free, low-voltage (~|0.8V|), energy-efficient (full On/Off switching at ~2pJ), and reliable. Furthermore, pulse measurements reveal …
A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among various propositions, Spiking Neural Network (SNN) seems a valid candidate. (i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. (ii) In terms of technology, SNN may be…
Atomic Switch Networks (ASN) comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly-interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing (RC). This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification …
The possibility of in-memory computing with volatile memristive devices, namely, memristors requiring a power source to sustain their memory, is demonstrated. We have adopted a hysteretic graphene-based field emission structure as a prototype of volatile memristor, which is characterized by a non-pinched hysteresis loop. Memristive model of the structure is developed and used to simulate a polymorphic circuit implementing in-memory computing gates such as the material implication. Specific regions of parameter space realizing useful logic functions are identified. Our results are applicable to…
Flexible and biocompatible memristive devices are particularly attractive for bioelectronic systems due to the interest in improving computing capabilities and the motivation to interface electronics with biological systems including drug delivery, neural interfaces and biosensors. Structures made of more unorthodox, organic material can address different issues due to their characteristics: flexibility, conformability, biocompatibility and simple and low-cost fabrication. It has been observed that gating graphene/ionic liquid (IL) devices leads to the formation of an electrical double layer (…
Continued progress in high speed computing depends on breakthroughs in both materials synthesis and device architectures. The performance of logic and memory can be enhanced significantly by introducing a memristor, a two terminal device with internal resistance that depends on the history of the external bias voltage. State of the art memristors, based on metal insulator metal (MIM) structures with insulating oxides, such as TiO2, are limited by a lack of control over the filament formation and external control of the switching voltage. Here, we report a class of memristors based on grain bou…
Redox-based memristive devices are among the alternatives for the next generation of non volatile memories, but also candidates to emulate the behavior of synapses in neuromorphic computing devices. It is nowadays well established that the motion of oxygen vacancies (OV) at the nanoscale is the key mechanism to reversibly switch metal/insulator/metal structures from insulating to conducting, i.e. to accomplish the resistive switching effect. The control of OV dynamics has a direct effect on the resistance changes, and therefore on different figures of memristive devices, such as switching spee…
Usually, resistive switching (RS) devices show digital RS memory (sharp SET and RESET process), which is most suitable for digital data storage applications. Some RS devices also manifest ideal memristive behavior or analog memory characteristics (gradual change in resistance states). The analog RS properties of memristive devices widen their application domain to a much broader field of neuromorphic computing. The tunability of memristive devices to digital or analog memory applications greatly depends upon the switching medium. In this work, we report a comparative study on RS properties of …
The development of novel devices for neuromorphic computing and non-traditional logic operations largely relies on the fabrication of well controlled memristive systems with functionalities beyond standard bipolar behavior and digital ON-OFF states. In the present work we demonstrate for Ta$_2$O$_5$-based devices that it is possible to selectively activate/deactivate two series memristive interfaces in order to obtain clockwise or counter-clockwise multilevel squared remanent resistance loops, just by controlling the (a)symmetry of the applied stimuli and independently of the nature of the use…
This study presents a comprehensive examination of the development of TiN/SiO$_\mathrm{x}$/Cu/SiO$_\mathrm{x}$/TiN memristive devices, engineered for neuromorphic applications using a wedge-type deposition technique and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial-and-error approaches, which often obscure the effects of varying layer compositions. By employing an \textit{off-center} thermal evaporation method, we created a thickness gradient of SiO$_\mathrm{x}$ and Cu on a 4-inch wafer, facilitating d…
We propose the encoding of memristive quantum dynamics on a digital quantum computer. Using a set of auxiliary qubits, we simulate an effective non-Markovian environment inspired by a collisional model, reproducing memristive features between expectation values of different operators in a single qubit. We numerically test our proposal in an IBM quantum simulator with 32 qubits, obtaining the pinched hysteresis curve that is characteristic of a quantum memristor. Furthermore, we extend our method to the case of two coupled quantum memristors, opening the door to the study of neuromorphic quantu…
We study the entanglement and memristive properties of three coupled quantum memristors. We consider quantum memristors based on superconducting asymmetric SQUID architectures which are coupled via inductors. The three quantum memristors are arranged in two different geometries: linear and triangular coupling configurations. We obtain a variety of correlation measures, including bipartite entanglement and tripartite negativity. We find that, for identical quantum memristors, entanglement and memristivity follow the same behavior for the triangular case and the opposite one in the linear case. …
