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2.
ACS Nano ; 17(13): 11994-12039, 2023 Jul 11.
Article in English | MEDLINE | ID: mdl-37382380

ABSTRACT

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

3.
Front Big Data ; 5: 787421, 2022.
Article in English | MEDLINE | ID: mdl-35496379

ABSTRACT

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

4.
Nanotechnology ; 32(1): 012002, 2021 Jan 01.
Article in English | MEDLINE | ID: mdl-32679577

ABSTRACT

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

5.
IEEE Trans Neural Netw Learn Syst ; 29(10): 4782-4790, 2018 10.
Article in English | MEDLINE | ID: mdl-29990267

ABSTRACT

Potential advantages of analog- and mixed-signal nanoelectronic circuits, based on floating-gate devices with adjustable conductance, for neuromorphic computing had been realized long time ago. However, practical realizations of this approach suffered from using rudimentary floating-gate cells of relatively large area. Here, we report a prototype $28\times28$ binary-input, ten-output, three-layer neuromorphic network based on arrays of highly optimized embedded nonvolatile floating-gate cells, redesigned from a commercial 180-nm nor flash memory. All active blocks of the circuit, including 101 780 floating-gate cells, have a total area below 1 mm2. The network has shown a 94.7% classification fidelity on the common Modified National Institute of Standards and Technology benchmark, close to the 96.2% obtained in simulation. The classification of one pattern takes a sub-1- $\mu \text{s}$ time and a sub-20-nJ energy-both numbers much better than in the best reported digital implementations of the same task. Estimates show that a straightforward optimization of the hardware and its transfer to the already available 55-nm technology may increase this advantage to more than $10^{2}\times $ in speed and $10^{4}\times $ in energy efficiency.

6.
Front Neurosci ; 12: 195, 2018.
Article in English | MEDLINE | ID: mdl-29643761

ABSTRACT

We have calculated key characteristics of associative (content-addressable) spatial-temporal memories based on neuromorphic networks with restricted connectivity-"CrossNets." Such networks may be naturally implemented in nanoelectronic hardware using hybrid memristive circuits, which may feature extremely high energy efficiency, approaching that of biological cortical circuits, at much higher operation speed. Our numerical simulations, in some cases confirmed by analytical calculations, show that the characteristics depend substantially on the method of information recording into the memory. Of the four methods we have explored, two methods look especially promising-one based on the quadratic programming, and the other one being a specific discrete version of the gradient descent. The latter method provides a slightly lower memory capacity (at the same fidelity) then the former one, but it allows local recording, which may be more readily implemented in nanoelectronic hardware. Most importantly, at the synchronous retrieval, both methods provide a capacity higher than that of the well-known Ternary Content-Addressable Memories with the same number of nonvolatile memory cells (e.g., memristors), though the input noise immunity of the CrossNet memories is lower.

7.
Nat Mater ; 17(4): 293-295, 2018 04.
Article in English | MEDLINE | ID: mdl-29358644
8.
Nat Commun ; 9(1): 413, 2018 01 24.
Article in English | MEDLINE | ID: mdl-29367670

ABSTRACT

The original version of this Article contained an error in Eq. 1. The arrows between the symbols "T" and "B", and "B" and "T", were written "↔" but should have been "→", and incorrectly read: IEBIC=IEBAC+ISEE+I(e↔h)+IEBICT↔B+IESEEB↔T The correct from of the Eq. 1 is as follows:IEBIC=IEBAC+ISEE+I(e↔h)+IEBICT→B+IESEEB→T This has now been corrected in both the PDF and HTML versions of the article.

9.
Nat Commun ; 8(1): 1972, 2017 12 07.
Article in English | MEDLINE | ID: mdl-29215006

ABSTRACT

Metal oxide resistive switches are increasingly important as possible artificial synapses in next-generation neuromorphic networks. Nevertheless, there is still no codified set of tools for studying properties of the devices. To this end, we demonstrate electron beam-induced current measurements as a powerful method to monitor the development of local resistive switching in TiO2-based devices. By comparing beam energy-dependent electron beam-induced currents with Monte Carlo simulations of the energy absorption in different device layers, it is possible to deconstruct the origins of filament image formation and relate this to both morphological changes and the state of the switch. By clarifying the contrast mechanisms in electron beam-induced current microscopy, it is possible to gain new insights into the scaling of the resistive switching phenomenon and observe the formation of a current leakage region around the switching filament. Additionally, analysis of symmetric device structures reveals propagating polarization domains.

10.
Nat Commun ; 8(1): 752, 2017 09 29.
Article in English | MEDLINE | ID: mdl-28963546

ABSTRACT

If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks.High-density information storage calls for the development of modern electronics with multiple stacking architectures that increase the complexity of three-dimensional interconnectivity. Here, Wu et al. build a stacked yet flexible artificial synapse network using layer-by-layer solution processing.


Subject(s)
Learning , Memory , Neural Networks, Computer , Synapses/physiology , Algorithms , Electronics , Humans , Long-Term Potentiation , Models, Neurological , Neuronal Plasticity
12.
Front Neurosci ; 9: 488, 2015.
Article in English | MEDLINE | ID: mdl-26732664

ABSTRACT

The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2- x /Pt memristors and CMOS integrated circuit components.

13.
Nat Commun ; 5: 3990, 2014 Jun 02.
Article in English | MEDLINE | ID: mdl-24886761

ABSTRACT

Oxide-based resistive switching devices are promising candidates for new memory and computing technologies. Poor understanding of the defect-based mechanisms that give rise to resistive switching is a major impediment for engineering reliable and reproducible devices. Here we identify an unintentional interface layer as the origin of resistive switching in Pt/Nb:SrTiO3 junctions. We clarify the microscopic mechanisms by which the interface layer controls the resistive switching. We show that appropriate interface processing can eliminate this contribution. These findings are an important step towards engineering more reliable resistive switching devices.

14.
Nano Lett ; 13(9): 4068-74, 2013 Sep 11.
Article in English | MEDLINE | ID: mdl-23981113

ABSTRACT

Hysteretic metal-insulator transitions (MIT) mediated by ionic dynamics or ferroic phase transitions underpin emergent applications for nonvolatile memories and logic devices. The vast majority of applications and studies have explored the MIT coupled to the electric field or temperarture. Here, we argue that MIT coupled to ionic dynamics should be controlled by mechanical stimuli, the behavior we refer to as the piezochemical effect. We verify this effect experimentally and demonstrate that it allows both studying materials physics and enabling novel data storage technologies with mechanical writing and current-based readout.


Subject(s)
Metals/chemistry , Nanotechnology , Electric Conductivity , Information Storage and Retrieval , Nanostructures/chemistry
15.
Nat Commun ; 4: 2072, 2013.
Article in English | MEDLINE | ID: mdl-23797631

ABSTRACT

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

16.
Nat Nanotechnol ; 8(1): 13-24, 2013 Jan.
Article in English | MEDLINE | ID: mdl-23269430

ABSTRACT

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

17.
ACS Nano ; 6(8): 7026-33, 2012 Aug 28.
Article in English | MEDLINE | ID: mdl-22845698

ABSTRACT

Nanoscale electromechanical activity, remanent polarization states, and hysteresis loops in paraelectric TiO(2) and SrTiO(3) thin films are observed using scanning probe microscopy. The coupling between the ionic dynamics and incipient ferroelectricity in these materials is analyzed using extended Landau-Ginzburg-Devonshire (LGD) theory. The possible origins of electromechanical coupling including ionic dynamics, surface-charge induced electrostriction, and ionically induced ferroelectricity are identified. For the latter, the ionic contribution can change the sign of first order LGD expansion coefficient, rendering material effectively ferroelectric. The lifetime of these ionically induced ferroelectric states is then controlled by the transport time of the mobile ionic species and well above that of polarization switching. These studies provide possible explanation for ferroelectric-like behavior in centrosymmetric transition metal oxides.


Subject(s)
Models, Chemical , Nanostructures/chemistry , Nanostructures/ultrastructure , Strontium/chemistry , Titanium/chemistry , Computer Simulation , Electric Impedance , Equipment Design , Equipment Failure Analysis , Nanomedicine , Stress, Mechanical
18.
Nanotechnology ; 23(7): 075201, 2012 Feb 24.
Article in English | MEDLINE | ID: mdl-22260949

ABSTRACT

Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.

20.
Nanotechnology ; 22(25): 254015, 2011 Jun 24.
Article in English | MEDLINE | ID: mdl-21572186

ABSTRACT

Memristors are memory resistors promising a rapid integration into future memory technologies. However, progress is still critically limited by a lack of understanding of the physical processes occurring at the nanoscale. Here we correlate device electrical characteristics with local atomic structure, chemistry and temperature. We resolved a single conducting channel that is made up of a reduced phase of the as-deposited titanium oxide. Moreover, we observed sufficient Joule heating to induce a crystallization of the oxide surrounding the channel, with a peculiar pattern that finite element simulations correlated with the existence of a hot spot close to the bottom electrode, thus identifying the switching location. This work reports direct observations in all three dimensions of the internal structure of titanium oxide memristors.

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