international workshop

INTERNATIONAL WORKSHOP From ReRAM and Memristors to new Computing Paradigms

Book of Abstracts

28-31 October 2018 Sentido Aegean Pearl Hotel, Rethymno, Crete, Greece 1

MEM-Q

mem-q.ee.duth.gr

MEM-Q-Abstract_Andreeva

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MEM-Q-Abstract_Biolek

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MEM-Q-Abstract_Bojun

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MEM-Q-Abstract_Brivio-INV

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MEM-Q-Abstract_Demin-INV

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MEM-Q-Abstract_Dubkov

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MEM-Q-Abstract_Filatov

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MEM-Q-Abstract_Gkoupidenis-INV

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MEM-Q-Abstract_Halter_Mattia

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MEM-Q-Abstract_Ismail

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MEM-Q-Abstract_Karafyllidis

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MEM-Q-Abstract_Kharcheva

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MEM-Q-Abstract_Korolev

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MEM-Q-Abstract_Koveshnikov

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MEM-Q-Abstract_LamasRR

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MEM-Q-Abstract_Mikhaylov

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MEM-Q-Abstract_Minnekhanov

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MEM-Q-Abstract_Panin

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MEM-Q-Abstract_Perez

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MEM-Q-Abstract_Rubtsov

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MEM-Q-Abstract_Ryazanov-INV

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MEM-Q-Abstract_Tominov

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MEM-Q-Abstract_Udovichenko

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MEM-Q-abstract_YvdBurgt-INV

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MEM-Q-Abstract_Zhuk

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MEM-Q ABSTRACT

PLEASE CHOOSE: ORAL

Local electrical properties of thin-layered oxide systems with I-V curve hysteresis or resistive switching effects observed by means of tunneling atomic force microscopy Natalia Andreeva1*, Dmitry Chigirev1, Andrey Kunitsyn1, & Anatoly Petrov1 1

Faculty of Electronics, Department of Micro- and Nanoelectronics, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia * Natalia Andreeva: [email protected]

We report the results of experimental study of local conductive properties for several types of metal oxide thin layered systems (epitaxial, nano- and polycrystalline ferroelectric thin films, bilayer oxide structures) by means of tunneling atomic force microscopy (AFM). According to the results of measurements of current distribution over the metal oxide film surface together with I-V characteristics, all structures could be divided into three groups. The first group of samples exhibits the reversible formation of conductive nanoscale areas under the voltage application between AFM tip and the bottom electrode of the structure [Figure (b)]. We assume that the formation of these areas caused the deviation of the resistive switching parameters (for example, the value reset voltage). The second group of samples demonstrates the existence of several low resistance states (for one high resistance state) appeared with increasing the value of voltage applied to the structures [Figure (c)]. Time dynamics of I-V curves justifies that there is a relaxation of low resistance states to the first one in this type of systems. We relate an appearance of several low resistance states with capacitive effects in metal oxide thin layers. For the third group of samples I-V curve hysteresis was not connected with the resistive switching effects but introduced by polarization reversal in thin ferroelectric films [Figure (d)].

Figure. a) voltage applied to the thin film capacitor during I-V curve measurements; b) – instability in the value of reset voltage for MIM structures (with resistive switching effects) with nano- and polycrystalline oxide films; c) – probable influence of the capacitance changes on the low resistance state in MIM structures with resistive switching effects; d) – I-V curve hysteresis in MIM structures with ferroelectric thin films without resistive switching effects.

MEM-Q-Abstract_Andreeva

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MEM-Q ABSTRACT

POSTER

Emulation of physical-based models of memristive switching devices via resistive two-port approach Viera Biolkova1, Jiri Vavra2, Zdenek Kolka1, & Dalibor Biolek1,2,* 1

Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 10, Brno, Czech Republic 2 Faculty of Military Technologies, University of Defence, Kounicova 65, Brno, Czech Republic * Corresponding author: [email protected]

Most of today’s hardware emulators of memristive devices are developing with the aim to mimic the behavior of ideal memristors that comply with original Chua’s definition [1]. There are only few of works attempting the emulation of more realistic models of existing resistive switching devices. We show an effective method of emulating known models with complex nonlinear dynamics [2] such as Pickett, Bayat, or Strachan. All these devices can be modeled as first-order extended memristors with their standard port and state equations, which can be transferred into the equivalent circuit in Fig. 1. The digital potentiometer, as well as microcontroller providing the digital signal processing (DSP) of the port and state equations, are carefully selected in terms of the dynamic range, quantization noise, speed, complexity of modeled equations, and power consumption. The emulator was successfully tested for mimicking the complex behavior of the above devices in fully floating configurations. emulator of extended memristor modeled as: i  G ( x, v)v x  F ( x, v ) nonlinear resistive two-port i

C F(x,v)

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Fig. 1. The concept of the programmable emulator of first-order extended memristors.

References [1] L. O. Chua, Memristor–The missing circuit element, IEEE T. Circ. Theory, vol. CT-18, no. 5, pp. 507–519, 1971. [2] D. Biolek et al, Modeling and simulation of large memristive networks, Int. J. Circ. Theor. Appl., vol. 46, no. 1, pp. 50–65, 2018. This work was supported by the Czech Science Foundation under Grant No. 18-21608S.

MEM-Q-Abstract_Biolek

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MEM-Q ABSTRACT

ORAL

Ultra-compact field enhanced electrochemical metallization cells achieving high speed atomic scale memristive switching Bojun Cheng1, Alexandros Emboras1,2*, Yannick Salamin1, Fabian Ducry2, Ping Ma1, Yuriy Fedoryshyn1, Samuel Andermatt2, Mathieu Luisier2 and Juerg Leuthold1** * [email protected] ** [email protected]

Electrochemical metallization cells have attracted attention for various applications such as nextgeneration memories[1], photonic systems,[2, 3] artificial synapses and steep threshold slope transistors.[4] We applied a fabrication technology to transfer the sharp tip of scanning probe microscope into SiO2 with radius below 10 nm and inter-electrode distances down to 1 nm (Figure 1a, 1b). The combination of sharp tip and thin oxide confines the location of a conductive filament to an ultra-small volume at the apex of the tip. As a consequence of this drastic miniaturization process, very reliable switching with set voltages below 200 mV (Figure 1c) and ultra-fast switching times below 10 ns have been achieved. Furthermore, we observe that devices with shorter gap thickness support higher currents. Combined ab-initio quantum transport simulations and experiments suggest that the manufactured structures exhibit reduced self-heating effects due to their lower dimensions, making them very promising candidates as next-generation (non-)volatile memory components. a

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Figure. a) SEM image showing the top view of the ECM cell. The red circle indicates the location of the sharp tip. b) AFM measured surface topology. The dashed line indicate the tip. c). Reproducibility of resistive switching (10 consecutive cycles). The current (upper) and resistance (lower) are measured as a function of the applied voltage.

[1] I. Valov, R. Waser, J. R. Jameson, M. N. Kozicki, Nanotechnology 2011, 22, 254003. [2] A. Emboras, J. Niegemann, P. Ma, C. Haffner, A. Pedersen, M. Luisier, C. Hafner, T. Schimmel, J. Leuthold, Nano Letters 2015, 16, 709. [3] A. Emboras, A. Alabastri, F. Ducry, B. Cheng, Y. Salamin, P. Ma, S. Andermatt, B. Baeuerle, A. Josten, C. Hafner, Acs Nano 2018, 12, 6706. [4] Z. R. Wang, M. Y. Rao, R. Midya, S. Joshi, H. Jiang, P. Lin, W. H. Song, S. Asapu, Y. Zhuo, C. Li, H. Q. Wu, Q. F. Xia, J. J. Yang, Advanced Functional Materials 2018, 28.

MEM-Q-Abstract_Bojun

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MEM-Q ABSTRACT

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Switching Dynamics of Filamentary RRAMs for Neural Computing Architectures Stefano Brivio, Jacopo Frascaroli, Erika Covi and Sabina Spiga CNR – IMM, Unit of Agrate Brianza, via C. Olivetti 2, 20864 Agrate Brianza (Italy) * Corresponding author: [email protected]

The huge increase of the production of digital unstructured data and the need of its quick elaboration has already initiated a revision of the standard computing architectures. For instance, graphic processing unit or application specific integrated circuits are extensively used to run neuro-inspired softwares. On the other side, hardware realizations of neural network are limited to few pioneering examples produced in standard VLSI technology.1–3 Such systems have already revised, in part, the standard spatio-temporal organization of the processing steps by co-locating memory and processing, parallelizing the processing steps and running in an event-driven (or asynchronous) fashion to some extent. The inefficiency of such systems is mainly caused by the employment of conventional memories, as synaptic weighting elements, which do not provide the required technical functionalities, as non-volatility, nanometer scaling, parallel programming and low power operation. These issues can be improved by resistive random access memory (RRAM) technologies,4 whose dynamical properties can be exploited to address the fundamental aspect of the memory capacity, or equivalently the memory lifetime, of a network of hardware synaptic elements. Indeed, the finite resolution of hardware devices responsible for the storing of incoming events produces the continuous replacement (or forgetting) of old memories by new ones, impacting the overall system performance. 5 In a theoretical study, it was demonstrated that finite resolution synapses with suitable dynamics can mitigate this fundamental problem.6 In our work, considering prototypical HfO 2-based devices, we analyzed the dynamic response of filamentary devices to train of identical pulses and evidence a generalized non-linear switching dynamics with gradual (soft) approach to the boundary conductance values,7 which is considered to improve the memory lifetime of a network according to theoretical neuroscience studies.6 The methodological characterization allows us also to identify the technical issues and the trade-offs among performance parameters that need further engineering. Furthermore, through spiking neural network simulation, we demonstrate that soft-bound synapses actually improve the memory lifetime and the overall performance of a network with respect to linear synapses with similar resolution. The equations used for the simulation are derived from experimental data from HfO2-based devices and from VLSI mixed signal digital analog subthreshold neuronal chips that has already been fabricated in 180 nm and 28 nm technology.3,8 The network operation is tested against the standard task of hand-written digit classification. The asynchronous learning operation by virtue of a generalized version of Spike Timing Dependent Plasticity has also been demonstrated at hardware level at the small scale of a 1 synapse - 2 neuron system, as a proof of concept for the simulated network. 9 1

P.A. Merolla, et al., Science 345, 668 (2014). S.B. Furber, et al., Proc. IEEE 102, 652 (2014). 3 N. Qiao, et al., Front. Neurosci. 9, (2015). 4 G. Indiveri, et al., Nanotechnology 24, 384010 (2013). 5 G. Parisi, J. Phys. Math. Gen. 19, L617 (1986). 6 S. Fusi and L.F. Abbott, Nat. Neurosci. 10, 485 (2007). 7 J. Frascaroli, S. Brivio, E. Covi, and S. Spiga, Sci. Rep. 8, 7178 (2018). 8 N. Qiao and G. Indiveri, in 2016 IEEE Biomed. Circuits Syst. Conf. BioCAS 552–555. 9 E. Covi, et al., J. Phys. Appl. Phys. 51, 344003 (2018). 2

MEM-Q-Abstract_Brivio-INV

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Spike-timing dependent plasticity in robust and noise-assisted learning of spiking neuron with memristive weights V.A. Demin1,2*, A.V. Emelyanov1,2, K.E. Nikiruy1,2, V.V. Rylkov1,2, P.K. Kashkarov1,2,3, M.V. Kovalchuk1,2,3 1 National Research Center “Kurchatov Institute”, 123182 Moscow, Russia 2 Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Moscow, Russia 3 Lomonosov Moscow State University, 119234, Moscow, Russia * Corresponding author: [email protected]

Variation of memristor characteristics as for device to device in a large memristive array and for cycle to cycle for the single device is one of the key limiting factors for a hardware realization of memristor-based artificial neural networks (ANNs). It is occasioned to be important mostly for the gradient-descent error back propagation training methods, and also for relay of an ANN weight parameters into an array of memristive devices. Despite of the great efforts done so far to fix the problems concerning that parameter variation, there is still a lack of highly reproducible memristor fabrication technologies. There is another promising approach to work with highly variable synaptic weight devices in the hardware-based ANN built on spiking neurons, spiking neural networks (SNNs). An idea is to apply a learning algorithm based on the robust local training rules that are the weight modifications calculated with locally accessible information, such as a spike-timing dependent plasticity (STDP). It was shown by many authors the possibility to realize naturally a multiplicative (dependent on the weight value) STDP in different memristive devices. The memristors fabricated by ion-beam sputtering on the base of (Co40Fe40B20)x(LiNbO3-y)100-x nanocomposite (LNO NC) (Fig. 1a) and assembled in a 3x3 passive crossbar architecture show a high resistance ratio (Roff/Ron≈100), quite good endurance (more than 103 cycles), perfect retention time (more than 106 s), and, most important, the appropriate STDP behavior (Fig. 1b).

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Fig. 1. TEM image of the LNO NC sample structure (a), and STDP window (b): dependence of a relative difference between final and initial (Gi) conductivities on the time delay between pre- and post-synaptic spikes.

Further, a simple one integrate-and-fire (I&F) neuron system with four LNO NC memristive weights was assembled to realize a simplest STDP-based learning task (Fig. 2a). The latter was an establishing certain resistance distribution under application to the inputs of Poisson-distributed spike trains. When the output I&F neuron generates an action potential, it is overlapped with input spikes and causes STDP-based modifications of memristors. The result is that final distributions of resistive states depend only on specific spike trains applied to the inputs but not on the memristors’ own characteristics or even their initial conductivity values (Fig. 2b). Thus, it is shown that STDP-based learning realized in the hardware SNN with LNO NC memristive weights is highly robust to the individual memristor parameter variation.


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Fig. 2. (a) Schematic diagram of studied neuromorphic network, and (b) resistance map illustration of STDP learning result. Every square in (b) describes a final weight state. Each row corresponds to the certain initial resistive state (102 Ω, 103 Ω, 104Ω), and each column to the certain applied combination of spike trains (numbers on squares stands for 4 specific spike trains).

The next step was defining a role of STDP in a noise-assisted learning. For that we realized the local rule training of our I&F unit system in two modes: the first one is high-contrast learning, i.e. with high ratio (10:1) of light and dark input pixel intensities that are coded by Poisson distribution spike rate, and the second one is low-contrast learning, with low ratio of those intensities (2:1). The accuracy of learning the target resistive patterns felt down from 100% to 50% when going from high- to low-contrast representation. But addition of the following noise (random voltage pulse trains at one Poisson rate) to the inputs led to a drastic increase of the accuracy up to 75%. This does means that memristive STDP can provide the training up of hardware neuromorphic networks with application of the bio-plausible Poisson noise to the inputs of the pre-trained system. This situation is something similar to a sleep-phase learning of biological neural networks that are pre-trained with definite pattern input signal during awake phase of an organism. Obtained results pave the way to memristor-based hardware SNN on-line learning independent on variation of memristive device characteristics. Authors gratefully acknowledge the financial support of the Ministry of Education and Science of the Russian Federation (project No. RFMEFI58717X0042) in part of LNO memristor cross-bar fabrication and characterization, and also the development of principles for modeling neuromorphic systems based on it. The noise study was supported by the Government of the Russian Federation (14.Y26.31.0021).


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MEM-Q ABSTRACT

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Stochastic models of memristors Alexander Dubkov1, Bernardo Spagnolo2,3, & Anna Kharcheva1 1

Faculty of Radiophysics, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 2 Research and Educational Center for Physics of Solid State Nanostructures, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 3 Department of Physics and Chemistry, University of Palermo, Viale delle Scienze, Ed. 18, I-90128, Palermo, Italy * Corresponding author: [email protected]

At present, memory elements called memristors have found application in diverse areas of science and technology ranging from information processing to biologically inspired systems. So far, however, all these studies have neglected the important effect of noise on the memory properties of these elements. As a result, an adequate stochastic model of memristor, taking into account many different factors as well as internal and external noises, is still far from being constructed. We give a brief overview of the works, whose authors study the effect of noise on the memristor functioning. Noise comes in various forms and it can be generally classified as internal or external to the system. While internal noise can provide much information on the system dynamics, external noise is generally considered a nuisance for practical applications. We then naively expect it to be a detrimental effect on the hysteresis of memory elements. At the same time, in statistical physics there are a lot of phenomena with constructive role of noise such as stochastic resonance, resonant activation, noise-enhanced stability and so on. The optimization phenomenon of hysteretic structures due to the stochastic resonance has already been reported earlier with regard to bistable and multistable potentials and neural networks. In the paper [1] authors showed that internal noise helps to increase the contrast ratio between low and high resistive states. The results were based on simulations by means of a model of resistive switching put forth by Strukov et al [2]. Patterson with co-authors in Ref. [3] (see also [4]) showed an experiment in which the addition of external noise had a positive effect in a manganite sample. In the paper [5], authors have proposed stochastic models for electric circuits that may contain memristors, and both the classical and quantum versions of noisy dynamics have been obtained. Preservation of the canonical structure was used as a guiding principle and the resulting theory allows for approximation schemes using Hamiltonian systems. A novel true random number generator based on a stochastic diffusive memristor was proposed in Ref. [6]. Experimentally observed innate stochasticity was modeled in a circuit compatible format in [7]. The model proposed is generic and could be incorporated into variants of threshold-based memristor models. Future research promises the application of stochastic models to more general memristive electric circuits and making deeper connections with the underlying statistical mechanical derivations. The work is supported by the Grant of the Government of the Russian Federation (contract No. 14.Y26.31.0021). 1. A. Stotland and M. Di Ventra, Phys. Rev. E 85, 011116 (2012). 2. D.B. Strukov, G.S. Snider, D.R. Stewart, and R.S. Williams, Nature 453, 80 (2008). 3. G.A. Patterson, P.I. Fierens, A.A. Garcia, and D.F. Grosz, Phys. Rev. E 87, 012128 (2013). 4. G.A. Patterson, P.I. Fierens, and D.F. Grosz, Appl. Phys. Lett. 103, 074102 (2013). 5. J.E. Gough and G. Zhang, J. Math. Phys. 58, 073505 (2017). 6. H. Jyang et al, Nature Commun. 8, 882 (2017). 7. R. Naous, M. Al-Shedivat, and K.N. Salama, IEEE Trans. Nanotech. 15, 15 (2016).

MEM-Q-Abstract_Dubkov

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MEM-Q ABSTRACT

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Noise and resistive switching in a contact of an atomic force microscope probe to yttria stabilized zirconia film on a conductive substrate Dmitrii Filatov1,*, Alexei Novikov1, Dmitrii Liskin1, Sergei Zubkov1, Ivan Antonov1, Dmitrii Antonov1, Oleg Gorshkov1, Arkadii Yakimov1,2, & Bernardo Spagnolo1, 3 1

Research and Educational Center for Physics of Solid State Nanostructures, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 2 Faculty of Radiophysics, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 3 Department of Physics and Chemistry, University of Palermo, Viale delle Scienze, Ed. 18, I-90128, Palermo, Italy * Corresponding author: [email protected]

We report on the results of investigations of the noise generated in the contact of a conductive atomic force microscope (CAFM) probe to a 4-nm thick yttria stabilized zirconia (YSZ, 12% mol. of Y2O3) film on a conductive substrate and of the local resistive switching (RS) in the probe-tosample contact to the YSZ film under a random (noise) voltage between the probe and the substrate. CAFM has been proven to be a powerful tool for studying the RS at the nanometer scale [1]. In such experiments, the CAFM probe contact to a dielectric film on a conductive substrate is treated as a nanometer-sized virtual memristive device. The work has a dual goal: i) statistical and spectral analysis of the noise generated by the virtual memristor in order to find the parameters of the elementary processes of the RS (such as the oxygen O2– ion migration, etc.); ii) investigation of the response of the virtual memristor to the noise signal in search for the fundamental phenomena featuring the memristor as a complex multistable nonlinear system. The analysis of the noise generated by the virtual memristor revealed a flicker 1/f noise in the low-frequency band (f < 10 kHz). The noise has been attributed to the diffusion of the O2– ions via the oxygen vacancies in YSZ. The activation energy of the O2– ion migration Ea has been determined from the noise spectra, and it is found to be in the range 0.52–0.68 eV at 300 K. This result is consistent with the values determined from the ion migration polarization measurements in the YSZ films deposited in the same conditions: Ea = 0.53–0.56 eV at 300–500 K [2]. Fig. 1. A waveform of the electric current via the Under a Gaussian white noise signal with high CAFM probe contact to the YSZ film recorded enough root mean square (RMS) voltage, the when Gaussian white noise voltage with RMS = virtual memristor switches between the low 2.94 V is applied between the CAFM probe and resistance state and the high resistance one the conductive substrate. randomly as a random telegraph signal (RTS) (see Fig. 1). We believe the observed effect to manifest a fundamental intrinsic property of the memristor as a complex bistable system. The work has been supported by Russian Mega-Grant Program (Project No. 14.Y26.31.0021). 1. M. Lanza. Materials 7, 2155 (2014). 2. S. Tikhov et al. Adv. Condens.Matter Phys. 2018, 2028491.

MEM-Q-Abstract_Filatov

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MEM-Q ABSTRACT

ORAL

Organic materials for neuromorphic devices and architectures Paschalis Gkoupidenis Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany [email protected]

Hardware-based implementation of neuromorphic architectures offers efficient ways of data manipulation and processing, especially in data intensive applications such as big data analysis and real time processing. In contrast to traditional von Neumann architectures, neuro-inspired devices may offer promising solutions in interacting with human sensory data and process information in real time. Therefore such kind of devices may offer in the future novel ways of data manipulation in bioelectronics. Over the past years, organic materials and devices have attracted lots of attention in bioelectronics due to their attractive characteristics for bioelectronics applications such as biocompatibility, the ability to operate in liquid electrolytes, tunability via chemical synthesis and low cost fabrication processes. Here, various concepts of organic neuromorphic devices will be presented based on organic electrochemical transistors (OECTs), devices that are traditionally used in bioelectronics. Regarding the implementation of neuromorphic devices, the key properties of the OECT that resemble the neural environment will be presented here. These include the operation in liquid electrolyte environment, low power consumption and the ability of formation of massive interconnections through the electrolyte continuum. Showcase examples of neuromorphic functions with OECTs will also be presented, including short-,1 long-term plasticity,2 spatiotemporal or distributed information processing,3-6 and synchronization concepts. References 1. 2. 3. 4. 5. 6.

P. Gkoupidenis, N. Schaefer, B. Garlan and G. G. Malliaras, Adv. Mater., 2015, 27 (44), 7176-7180. P. Gkoupidenis, N. Schaefer, X. Strakosas, J. A. Fairfield and G. G. Malliaras, Appl. Phys. Lett., 2015, 107 (26), 263302. P. Gkoupidenis, D. A. Koutsouras, T. Lonjaret, J. A. Fairfield and G. G. Malliaras, Sci. Rep., 2016, 6, 27007. P. Gkoupidenis, S. Rezaei-Mazinani, C. M. Proctor, E. Ismailova and G. G. Malliaras, AIP Adv., 2016, 6 (11), 111307. P. Gkoupidenis, D. A. Koutsouras and G. G. Malliaras, Nat. Commun., 2017, 8, 15448. D. A. Koutsouras, G. G. Malliaras and P. Gkoupidenis, MRS Commun., 2018, 5 (1-5).

MEM-Q-Abstract_Gkoupidenis-INV

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MEM-Q ABSTRACT


Milisecond Flash Lamp Annealing for the stabilization of ferroelectric HfxZr1-xO2 Mattia Halter1,2,*, Éamon O’Connor1,†, Felix Eltes1, Marilyne Sousa1, Stefan Abel1, Jean Fompeyrine1 1

IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland 2 Integrated Systems Laboratory, Swiss Federal Institute of Technology Zurich, CH-8092 Zurich, Switzerland * Corresponding author: [email protected] † Currently at EPFL, Lausanne, Switzerland

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The emergence of ferroelectricity in doped HfO2 has attracted a great deal of attention since its discovery in 2011.1 Their full compatibility with the complementary metal-oxide semiconductor (CMOS) process make them viable candidates for application in non-volatile memory devices. HfxZr1-xO2 (HZO) films have emerged as one of the more promising material.2 The ferroelectricity in thin doped HfO2 films is generally accepted to originate from the crystallization of a non centrosymmetrical orthorhombic phase with the space group Pca21.3 During the crystallization, the temperature profile needs to be well controlled in order to favor the formation of the metastable ferroelectric orthorhombic phase.4 Here we report the utilization of millisecond Flash Lamp Annealing (ms-FLA) for the stabilization of ferroelectric HfxZr1-xO2 (HZO) films. The devices consist of a bottom TiN electrode, a 10 nm thick HZO layer and a top TiN electrode. The combination of a preheat step at 375°C with a flash lamp pulse results in ferroelectric characteristics comparable to those obtained when using a rapid thermal anneal (RTA) at 650°C. This statement is supported by X-ray diffraction, capacitance voltage and polarization hysteresis measurements, including upon cycling. In 10 nm thick HZO layers a remanent polarization (Pr) of ~21 µC/cm2 and a coercive field (Ec) of ~1.1 MV/cm are achieved. Cycling analysis shows an increase of endurance for the ms-FLA compared to RTA by one decade, up to 107 bipolar cycles. These results pave the way for a low thermal budget alternative for the crystallization of ferroelectric HZO films.

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Voltage (V) Diffraction angle [2θ] Figure 1: Comparison of a RTA at 650°C and a ms-FLA with a preheat step at 375°C: (a) Polarization versus electric field (PE) characteristics measured after cycling at room temperature and at 1 kHz. (b) GIXRD intensity profile showing a characteristic peak for the orthorhombic phase of HfO2 at 2𝜃 = 30.6°. Bibliography 1 T.S. Böscke, J. Müller, D. Bräuhaus, U. Schröder, and U. Böttger, Appl. Phys. Lett. 99, 0 (2011). 2 J. Müller, T.S. Böscke, U. Schröder, S. Mueller, D. Bräuhaus, U. Böttger, L. Frey, and T. Mikolajick, Nano Lett. 12, 4318 (2012). 3 S. Clima, D.J. Wouters, C. Adelmann, T. Schenk, U. Schroeder, M. Jurczak, and G. Pourtois, Appl. Phys. Lett. 104, 092906 (2014). 4 M. Hyuk Park, H. Joon Kim, Y. Jin Kim, W. Lee, T. Moon, and C. Seong Hwang, Appl. Phys. Lett. 102, 0 (2013).

MEM-Q-Abstract_Halter_Mattia

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A comprehensive study on the resistive switching behavior of Mg0.6Zn0.4Obased thin film devices Muhammad Ismail, Cheonji Li and Sungjun Kim*

School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea

Abstract Mg doped ZnO thin films are potential materials for resistive switching (RS) memories. Highly c-oriented Mg0.4Zn0.6O thin films of different thickness and crystalline features were deposited on Pt/Ti/SiO2/Si substrates by the conventional sol-gel process. Structural and microstructural features were investigated by X-ray diffraction, X-ray photoelectron spectroscopy, and atomic force microscopy. The influence of experimental parameters on RS properties, including the microstructural features of the thin films and the species of electrode materials, have been thoroughly investigated. Keywords: resistive random access memory, thin films, sol-gel, microstructures * Corresponding authors. Email: [email protected], [email protected]

MEM-Q-Abstract_Ismail

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MEM-Q ABSTRACT


Modeling and simulation of quantum transport in nanoscale memristors. Iossif-Agelos Fyrigos1, Vasilios Ntinas, Georgios Ch. Sirakoulis1, Panagiotis Dimitrakis2, Ioannis G. Karafyllidis1,* 1 Departmant of Electrical and Computer Engineerin, Democritus University of Thrace, 67100 Xanthi, Greece 2 Institute of Nanoscience and Nanotechnology, NCSR “Demokritos”, 15 310 Aghia Paraskevi, Athens, Greece * Corresponding author: [email protected]

Nanoscale memristors are modeled using non-equilibrium Green’s functions combined with tightbinding Hamiltonians [1]. This approach captures the combined kinetics of the active electrode atoms and electron transport [2]. The process of the metallic filament formation and dissolution in the solid dielectric is modeled by dividing the process into time steps. At each time step the new filament form is computed and the Hamiltonian describing the system is constructed. This Hamiltonian along with the applied potential are used to compute the device conductance and current for this time step. Our results reproduced the memristor I-V characteristic and showed that the conductance quantization and the corresponding multiple memristor states become more prominent as the solid dielectric thickness decreases. The model is fully parametrized and ready for calibration using experimental data. [1]. S. Datta, “Nanoscale device modeling: the Green’s function method”, Superlattices and Microstructures, vol. 28, pp. 253-278, 2000. [2]. Ilia Valov, Rainer Waser, John R Jameson Michael N Kozicki, “Electrochemical metallization memories—fundamentals, applications, prospects”, Nanotechnology, vol. 22, 254003, 2011.

Conductance of a nanoscale memristor for the same applied voltage. The difference in normalized conductance (transmission) is due to the different filament forms corresponding to formation and dissolution. ACKNOWLEDMENT: The authors gratefully acknowledge the Greece-Russia bilateral joint research project MEM-Q (proj.no./MIS T4DPW-00030/5021467) supported by GSRT and funded by National and European funds.

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Dynamical and statistical properties of one simple model for resistive switching Anna Kharcheva1, Alexander Dubkov1, & Bernardo Spagnolo2,3 1

Faculty of Radiophysics, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 2 Research and Educational Center for Physics of Solid State Nanostructures, Lobachevsky University of Nizhny Novgorod, 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 3 Department of Physics and Chemistry, University of Palermo, Viale delle Scienze, Ed. 18, I-90128, Palermo, Italy * Corresponding author: [email protected]

We consider the simple model for resistive switching describing the certain properties of the memristor behavior which has a current-voltage characteristic of a hysteresis type. In this model the current I(t) and the voltage U(t) are connected by the following relation (1)

where sgn(x) is the signum function, a and b are some constants. Rн = a + b and Rн = a − b correspond to the states with a high and low resistance of the memristor, respectively. Based on trigonometric Fourier series expansion, the spectral composition of the current is studied when a sinusoidal voltage U(t) = Asinωt is applied to the memristor. Hereafter, we analyze the system of stochastic equations with the input of the random voltage in the form of Ornstein-Uhlenbeck process: (2) ,

where ξ(t) is white Gaussian noise with zero mean and the noise intensity D. The spectral power density of current ﬂuctuation is found for this model. The work is supported by the Grant of the Government of the Russian Federation (contract No. 14.Y26.31.0021).

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Dynamic response of metal-oxide memristive devices to complex electric signals D.S. Korolev*, A.I. Belov, I.N. Antonov, S.A. Gerasimova, Ya.I. Pigareva, A.S. Pimashkin, O.N. Gorshkov, D.I. Tetelbaum, A.N. Mikhaylov Lobachevsky University, 23/3 Gagarin prospect, 603950 Nizhny Novgorod, Russia * Corresponding author: [email protected]

Significant progress in studying the principles of the brain operation allows us to proceed with the development of a new generation of brain-inspired systems based on electronic devices that reproduce certain functions of the elements of nervous system, as well as with the integration of such electronic systems and living neural networks in “brain-on-chip” systems. Memristive devices due to internal stochastic nature are able to mimic the dynamics of synapses and neurons in the hardware implementation of artificial neural networks. In this report, we analyze the dynamic response of memristive nanostructures based on silicon or zirconium oxides to the effect of signals of various shapes, in particular, the signals of bioelectric activity of living neuronal cultures. Adaptive behavior of memristive nanostructures assumes a certain change in the resistive state under the action of various types of electrical signals. As such signals, separate voltage pulses of a given shape with variable amplitude and duration were used, as well as periodic pulse sequences, including the spike-like activity of FitzHugh-Nagumo (FHN) neural generators. Signals of electric activity of living cultures of dissociated brain hippocampal cells were recorded using multielectrode array. In addition, external noise of a different nature was used as an additional effect on memristive devices. The main regularity corresponding to the adaptive behavior of memristive nanostructures, is the influence of the parameters of various signal types on the resistive state. Adaptive change in the resistance under the influence of neuron-like signal of the FHN generators has allowed us to study the interaction of two FHN generators coupled via memristive device and to determine the parameters of resistive state that provide synchronization of the generators. One of the most striking examples of imitation of biological functions is the response of memristive devices to signals of neuronal activity recorded from the cultured neural network of the hippocampal cells. The study of the reaction of resistance of memristive nanostructures to the spiking activity of living brain cells has showed that the resistance of memristive device depends on the initial state and is subject to both short-term (volatile) and long-term changes, depending on the parameters of neuronal activity. This property of memristive devices can be used to develop an active registration interface, and in the future – to stimulate the culture of living cells of the brain with active feedback. A further development of the presented approach is the study of the influence of internal and external noise on the resistive switching phenomenon, which provides fundamentally new opportunities for increasing stability, predicting behavior and controlling the response of memristive devices. Here we present the results, which verify the constructive effect of noise on the parameters of resistive switching. We gratefully acknowledge the support of the Government of the Russian Federation (14.Y26.31.0021) and the Russian Science Foundation (16-19-00144).

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Fundamental properties of HfOx based RRAM: needs, issues and challenges Sergei Koveshnikov Institute of Microelectronics Technology, Russian Academy of Sciences * Corresponding author: [email protected]

HfOx is a mainstream oxide in the fab and is a strong candidate for RRAM. To provide RRAM feasibility, three key issues need to be resolved: (1) quantification of the intrinsic tradeoffs between operative voltage, current and Set/Reset switching time, (2) improvement of intrinsic and extrinsic variability of switching characteristics, and (3) development of HVM compatible forming methods. In this work these issues are addressed by developing fully integrated, low parasitics (Cparasitics < 50fF) 1T/1R enabling intrinsic, real-time Set/Reset switching and development of a HVM friendly pulsed forming. Key RRAM performance parameters including low (LRS) and high (HRS) resistance, Set/Reset voltage (Vt) and switching time (ts) were measured as a function of operative current (Icompl), voltage range, pulse speed (dV/dtrise) and temperature using real-time AC methodology. Excellent performance of HfOx based RRAM (Vt 105), multilevel (analog) storage of resistive states (plasticity), operation ability on flexible biocompatible (“wearable”) substrates [1]. The most promising structures for such applications to date are metal-organic memristive frameworks based on polymeric layers of parylene (poly-para-xylylene, or PPX) [2]. Currently, such structures show a fairly wide window of resistive switching (Roff/Ron ~ 104). However, their plasticity has not been studied yet, and the mechanism of formation of multilevel resistive states in in such systems is unclear. Therefore, the central task of this work is to study memristive properties, as well as the mechanism of plasticity of metal-organic memrisors based on PPX layers. We have studied memristive elements of ITO/PPX/metal structure. The PPX layers (100 nm) were deposited on ITO coated glass by the gas phase surface polymerization method using SCS Labcoter PDS 2010 vacuum deposition system. The upper electrodes were Ag, Al, Cu or Ti layers (500–1000 nm) obtained by thermal spraying through a special mask. Memristive characteristics of the samples were studied using the Cascade Microtech PM5 analytical probe station, the voltage pulses were supplied by means of a NI PXIe-4139 instrument, programmed in LABView. Fig. 1 shows the IV curves of the samples with Ag and Cu electrodes along with their multilevel resistive states. The number of the detected resistive states stable over time is 8, 4, 6 and 4 for memristors with Ag, Al, Cu and Ti electrodes, respectively, which means a good plasticity of the samples. The measured Roff/Ron ratios for the above structures were ≈ 30, 7, 1000 and 15, respectively. We also report a good endurance of PPX/Cu structures (~ 104 cycles). Thus, the prepared PPX-based structures are promising objects of the further memristive and neuromorphic studies. This work was supported by the Russian Science Foundation (project № 18-79-10253).

Figure 1. IV curves of PPX-based memristors with Ag (A) and Cu (B) electrodes, and their resistive states (C, D respectively)

[1] Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications / Wiley-VCH Germany, 2016. [2] Y. Cai, J. Tan, L. YeFan, M. Lin, R. Huang. Nanotech. 27, 275206, 2016.

MEM-Q-Abstract_Minnekhanov

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Photomemristive systems based on two-dimensional crystals for electronic neural networks Gennady N. Panin* Nano-Information Technology Academy, Dongguk University, Seoul, South Korea Institute of Microelectronics Technology RAS, Chernogolovka, Russia * [email protected], [email protected]

The unique electronic and optical properties of two-dimensional (2D) crystals, such as graphene, graphene oxide, molybdenum disulfide, thin disulfide etc,1-8 demonstrate their enormous potential in creating ultrahigh density nano- and bio-electronics for innovative image recognition systems and processing information. Synapse-like memristive elements are considered as a new type of electronic memory and logic switches with extremely low power consumption and footprint that can be used to overcome the limit of the current CMOS technology. Memristive elements with a floating photogate, called photomemristors2, 3 based on biocompatible graphene and 2D crystals, are investigated for use in neural networks. The photocatalytic oxidation of graphene is considered as an effective method of creating memristive heterostructures with photoresistive switching of ultrahigh density. Particular attention is paid to the new concept of the formation of self-assembled nanoscale memristive elements interfacing electronic neural networks and natural neurons. Biocompatible memristive systems with a floating photogate exhibit multiple states that can be monitored over a wide range of electromagnetic radiation and can be used for neurohybrid systems, neuromorphological computations, image processing and pattern recognition as well as for selective manipulation of neurons by light. Acknowledgements This work was supported by Basic Science Research Program through the NRF of Korea funded by the Ministry of Education (No. 2017R1D1A1B03035102)

References 1. Xiao Fu, P. Ilanchezhiyan,a G. Mohan Kumar, Hak Dong Cho, Lei Zhang, A. Sattar Chan, Dong J. Lee, Gennady N. Panin and Tae Won Kang, Nanoscale (2017) 9, 1820. 2. Wei Wang, Gennady N. Panin, Xiao Fu, Lei Zhang, P. Ilanchezhiyan, Vasiliy O. Pelenovich, Dejun Fu & Tae Won Kang, Scientific Reports (2016) 6, 31224, www.nature.com/articles/srep31224 3. Olesya O Kapitanova, Gennady N Panin, Hak Dong Cho, Andrey N Baranov and Tae Won Kang, Nanotechnology (2017) 28, 204005. 4. Benyamin Davaji, Hak Dong Cho, Mohamadali Malakoutian, Jong-Kwon Lee, Gennady Panin, Tae Won Kang & Chung Hoon Lee, Scientific Reports (2017) 7, 8811, www.nature.com/articles/s41598-017-08967-y 5. Evgeny Emelin, H. D. Cho, Zeke Insepov, J. C. Lee, Tae Won Kang, Gennady Panin, Dmitry Roshchupkin, and Kurbangali Tynyshtykbayev, Appl. Phys. Lett. (2017) 110, 264103. 6. Olesya O. Kapitanova, Elmar Yu. Kataev.., Hak Dong Cho, Tae Won Kang, Gennady N. Panin, et al, J. Phys. Chem. C (2017) 121, 27915−27922. 7. Wei Wang, Olesya O. Kapitanova, Pugazhendi Ilanchezhiyan, Sixing Xi, Gennady N. Panin, Dejun Fu, Tae Won Kang, RSC Advances (2018) 8, 2410.

8. Egor A. Kolesov, Mikhail S. Tivanov, Olga V. Korolik, Olesya O. Kapitanova, Hak Dong Cho, Tae Won Kang, Gennady N. Panin, Carbon (2018), doi: 10.1016/j.carbon.2018.09.020

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Tailored switching algorithms for emerging applications of HfO2 based RRAM memory arrays E. Perez*,1, M K. Mahadevaiah1 and Ch. Wenger1, C. Zambelli2, P. Olivo2, M. Ziegler3, F. Zahari4, H. Kohlstedt4 1

IHP, Im Technologiepark 25, 15236 Frankfurt (Oder), Germany 2 Universitá degli Studi di Ferrara, 44122 Ferrara, Italy 3 TU Ilmenau, 98684 Ilmenau, Germany 4 Christian-Albrechts-Universität zu Kiel, 24143 Kiel, Germany * Corresponding author: [email protected]

Recent advances in the performance of resistive random access memory (RRAM) have led to a significant interest in CMOS technologies. Although RRAM based memory arrays demonstrated excellent performance parameters, the intercell and intracell variability is still a critical issue. While the intracell variability can be optimized for particular memory cells (see Fig. 1), the intercell variability must be minimized by using specific programming routines like incremental step pulses with an additional read and verify algorithm (see. Fig. 2). In addition, major concerns are cycling variability, and resistance distributions degradation. Controlling these phenomena requires employing program-verify schemes. In this talk, specific algorithmic schemes to minimize resistance dispersion and achieve reliable multi-bit operation are evaluated. However, statistical variations can be tolerated and be useful in computing applications like neuromorphic networks. Recently, Hebbian learning, as an important biological concept for memory and learning, has been realized with single RRAM devices. The synaptic behaviour of RRAM devices can be evaluated by applying successive algorithms consisting of set or reset pulses with different lengths and amplitudes. These algorithms can be used to implemented synaptic functionality in memristive arrays and able the hardware realize of neuromorphic computation schemes.

Fig 1: Cross-sectional TEM image of the 1T-1R integrated cell (a) and photo of the 4 kbit memory array (b)

Fig. 2: Incremental Step Pulse with Verify Algorithm.

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UNN Laboratory of Stochastic Multistable Systems (StoLab) Bernardo Spagnolo1,2, Nikolay Agudov1, Alexey Rubtsov1,*, Oleg Gorshkov1, & Alexey Mikhaylov1 1

Research and Educational Center for Physics of Solid State Nanostructures, Lobachevsky University of Nizhny Novgorod (UNN), 23 Gagarin Ave., 603950, Nizhny Novgorod, Russia 2 Department of Physics and Chemistry, University of Palermo, Viale delle Scienze, Ed. 18, I-90128, Palermo, Italy * Corresponding author: [email protected]

Despite the impressive range of promising applications of memristive nanomaterials and devices there is a serious fundamental problem, which must be overcome before successful implementation in industry. This problem consists in the fact that the phenomenon of resistive switching (memristive effect) has a pronounced stochastic nature. The experimental research provides the ground to conclude that, for each switching cycle, the new resistive state corresponds to a completely new atomic configuration in a local switching volume that is formed or destroyed whenever the state is switched. Therefore, the memristor appears as multistable system, the switching dynamics of which occurs under the action of strong noise, i.e. the noise with the intensity comparable to the height of energy barriers separating stable states of the system. The internal structure of memristor is an example of complex multistable system for which the stochastic methods of complex system analysis have not been applied on a regular base until now. An interdisciplinary laboratory of stochastic multistable systems (StoLab) was established at the Lobachevsky University of Nizhny Novgorod (UNN) in 2018. The laboratory combines and integrates activities of research groups working in theoretical and experimental areas. Prof. B. Spagnolo contributes to the effective management of the laboratory and application of modern statistical analysis methods for investigation of memristive systems. The UNN researchers will provide the experimental study and, in particular, the verification of novel theoretical descriptions.

Head of laboratory Prof. B. Spagnolo

Sector of modern methods of stochastic analysis

Sector of technology of memristive materials

Sector of the microscopic probe investigations

Sector for physics of noise

Sector of neuromorphic technologies

The structure of the UNN Laboratory of Stochastic Multistable Systems (StoLab).

In the framework of the project it is planned to apply novel methods of statistical analysis and modeling of noise with various statistics in modern fundamental and applied research being carried out at UNN in memristive electronics. The scope of research will be continuously expanded on the basis of obtained experience. Applying the latest advances in the field of statistical analysis will provide the significant progress in understanding of not only the fundamental problems, but also the application of memristive microelectronic devices. These can be treated as complex systems and distributed media from the viewpoint of processes occurring at the micro- and nano-scale. The work is supported by the Grant of the Government of the Russian Federation (contract No. 14.Y26.31.0021).

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Superconducting qubit research in Russia and superconductor/ferromagnet structure applications for superconducting quantum and digital logics. Valery Ryazanov1 1

Institute of Solid State Physics, Russian Academy of Sciences, Chernogolovka, Moscow district, Russia, 142432 * Corresponding author: [email protected]

A review of results in the field of superconducting digital and quantum logics from the Institute of Solid State Physics, Russian Academy of Sciences is presented. The main emphasis is on use of hybrid superconductor/ferromagnet/superconductor Josephson junctions (SFS JJs). The most impressive feature of the developed SFS JJs is the ability to be in a state with the Josephson phase difference inversion or in -state [1,2]. This feature makes the SFS JJs valuable phaseshifting elements for applications in the digital superconducting circuits like a Toggle Flip-Flop based on cells with conventional Josephson tunnel junctions and the embedded SFS -junctions [3]. A quantum Josephson circuits, a -biased qubits, has been recently demonstrated too [3,4]. Another application for Josephson magnetic memory development is based on the SFS JJ feature that its critical current can be changed significantly by remagnetization of the F-interlayer [5]. Current interest of modern fundamental and applied researches is also related to implementation and study of planar multiterminal S/F/N circuits. The structure, which we studied in [6], included a Cu/Fe bilayer forming a bridge between two superconducting Al electrodes. It was observed a double-peak peculiarity in differential resistance of the S-(N/F)-S structures at a bias voltage corresponding to the superconducting minigap. The splitting of the minigap was explained by the electron spin polarization in the normal metal which was induced by the neighbouring ferromagnet. Our recent observations are also related to quasiparticle- and spin-injection to banks and barriers of planar Josephson junctions. First results were published in [7].

Results of two-year research in the framework of the first Russian project for superconducting qubits is presented too. The main result is the demonstration of singlequbit quantum gate operations using several types of superconducting qubits with the coherence time of more than 5 μs. 1. V.V. Ryazanov, V.A. Oboznov, A.Yu. Rusanov, A.V. Veretennikov, A.A. Golubov, and J. Aarts, Phys. Rev. Lett 86, 2427 (2001). 2. V. A. Oboznov, V.V. Bol'ginov, A. K. Feofanov, V. V. Ryazanov and A.I. Buzdin, Phys. Rev. Lett. 96, 197003 (2006). 3. A.K. Feofanov, V.A. Oboznov, V.V. Bol’ginov, J. Lisenfeld, S. Poletto, V.V. Ryazanov, A.N. Rossolenko, M. Khabipov, D. Balashov, A.B. Zorin, P.N. Dmitriev, V.P. Koshelets and A. V. Ustinov. Nature Physics 6, 593 (2010). 4. A.V. Shcherbakova, K.G. Fedorov, K.V. Shulga, V.V. Ryazanov, V.V. Bolginov, V.A. Oboznov, S.V. Egorov, V.O. Shkolnikov, M.J. Wolf, D. Beckmann, A.V. Ustinov. Superconductor Science and Technology, 28, 025009 (2015). 5. T. I. Larkin, V. V. Bol’ginov, V. S. Stolyarov, V. V. Ryazanov, I. V. Vernik, S. K. Tolpygo, O. A. Mukhanov, Appl. Phys. Lett. 100, 222601 (2012). 6. T. E. Golikova, F. Hübler, D. Beckmann, I. E. Batov, T. Yu. Karminskaya, M. Yu. Kupriyanov, A. A. Golubov, and V. V. Ryazanov. Phys. Rev. B 86, 064416 (2012). 7. T. E. Golikova, M. J. Wolf, D. Beckmann, I. E. Batov, I. V. Bobkova, A. M. Bobkov, and V. V. Ryazanov, Phys. Rev. B 89, 104507 (2014).

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Investigation of memristive properties of ZnxTiyHfzOi nanocomposites Roman Tominov1*, Vadim Avilov1, Vladimir Smirnov1, Evgeny Zamburg2 & Oleg Ageev1 1

Research and Education Center “Nanotechnology”, Southern Federal University, Taganrog, 347928, Russia 2 Department of Electrical & Computer Engineering, National University of Singapore, 117582, Singapore [email protected], [email protected]

A resistive switching effect in metal/oxide/metal structures is attractive for resistive randomaccess memory (RRAM) manufacturing [1]. Resistive switching effect was observed in many oxide film (ZnO, HfO2, TiO2, NiO, Al2O3, SnO2, SiO2, etc.). Mixing of these materials allows to combine their advantages and remedy the disadvantages [2]. Nanocomposite ZnxTiyHfzOi thin film demonstrates memristive effect and can be used for RRAM manufactruring. The aim of this work is the investigation of resistive switching effect in ZnxTiyHfzOi nanocomposite films. Nanocomposite ZnxTiyHfzOi thin film was grown by pulsed laser deposition on the sapphire/TiN (100 nm) substrate. To form nanocomposite film, ZnO, HfO2, TiO2 targets were used for 51 cycles (4 pulses (2 Hz), 40 pulses (2 Hz), 4 pulses (2 Hz), respectively). The deposition was performed under the following conditions: substrate temperature 400°C, target–substrate distance 50 mm, O2 pressure 1 mTorr, laser pulse energy 300 mJ. Electrical measurements were carried out using semiconductor characterization system Keithley 4200-SCS (Keithley, USA) with W probes. During experiment TiN layer was grounded. Currentvoltage curves were obtained from –2.5 to +2.5 V sweep for 10 cycles at the same point (figure).

Figure. Current-voltage characteristic of ZnxTiyHfOi thin film and endurance test

It was shown that resistive switching from high resistance state (HRS) to low resistance state (LRS) was occurred at 1.9±0.3 V, and from LRS to HRS at -2.0±0.4 V. Resistance-cycle dependence was built based on obtained curves. It was shown that HRS was 220.4±80.2 kOhm, LRS was 2.5±1.3 kOhm. HRS/LRS ratio was about 88. The results can be used for nanocomposite-metal-oxide-film RRAM fabrication. This work was supported by Grant of the President of the Russian Federation No. MK2721.2018.8, by RFBR (research projects No. 18-37-00299 and No. 18-29-11019 mk) and Southern federal university (project No. VnGr-07/2017-02). The results were obtained using the equipment of the Research and Education Center and Center for Collective Use "Nanotechnologies" of Southern Federal University. 1. D. B. Strukov, G.S. Snider, D. R. Stewart, R. S. Williams, Nature, 453, pp. 80-83, 2008. 2. R.V. Tominov, E.G. Zamburg, D.A. Khakhulin, V.S. Klimin, V.A. Smirnov, Y.H. Chu, O.A. Ageev, Journal of Physics: Conference Series, 917, pp. 032023, 2017.

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A biomorphic neuroprocessor based on the composite memristor - diode crossbar A.D. Pisarev1, A.N. Busygin1*, S.Yu. Udovichenko1, A.N. Bobylev1, O.V. Maevsky2 1

REC “Nanotechnology”, University of Tyumen, Tyumen, Russia 2 Nanodevices LTD, Voronezh, Russia *

corresponding author, [email protected], +7 (982) 944-85-17

A concept of a biomorphic neuroprocessor [1] which can host both neural networks with simple neurons applied in the information technology and the biomorphic neural network to simulate the operation of the cortical column was proposed. The first approach within this concept is aimed at reducing the number of electronic components when using analog computations for synapses and neurons. The neuroprocessor circuit in this case includes a memory matrix, a neurons unit, and a logic matrix-based router. The second approach is based on unification of electronic components due to the application of an electrical circuit of the logic matrix in all functional units of the neuroprocessor without the memory matrix (fig.).

Fig. The functional circuit of the neuroprocessor: I – using the memory matrix for synapses and the logic matrix as a router; II – based on a universal logic matrix when there is no memory matrix.

The universal logic matrix can digitally execute intrinsic logic functions, route signals, design the neural network links when a separate memory matrix is not available, as well as process signals in the input and output units. The implementation of synapses in this logic matrix will require more electronic components with a lower power consumption. The neuroprocessor input unit serves to perform initial processing of audio and video signals (converting data coming from the interface unit to the required format). The output unit converts the neuron firing data to the digital binary code, compresses and transmits it to the interface unit. Electrical circuits, topology, fabrication technology, SPICE simulation [2] and operating principles for the main neuroprocessor components, namely an ultra-large memory matrix [3] and a logic matrix [4], based on composite memristor-diode crossbar, were proposed. The application of Zener diodes as non-linear selective elements minimizes parasitic currents and improves crossbar energy efficiency [5]. Neural network based on original biomorphic model of neuron was developed and adapted for neuroprocessor hardware. The hardware is designed using the Hodgkin-Huxley’s electrical neuron model thus neuroprocessor is biomorphic from the point of view of execution of biomorphic neural network functions. References 1. S.Yu. Udovichenko, A.D. Pisarev, A.N. Busygin, O.V. Maevsky, Nanoindustry, 2018, 5 doi: 10.22184/19938578.2017.76.5.26.34 2. S.Yu. Udovichenko, A.D. Pisarev, A.N. Busygin, A.N. Bobylev. First International Workshop, Kurchatov Institute, Moscow (MEM-Q) Book of abstracts, 2018, 19 3. A.D. Pisarev, AN. Busygin, S.Yu. Udovichenko, O.V.Maevsky, Microelectronic Engineering, 2018, 198, 1-7 4. S.Yu. Udovichenko, A.D. Pisarev, A.N. Busygin, O.V. Maevsky, Nanoindustry, 2017, 5, 26-34 doi: 10.22184/1993-8578.2018.84.5.344.355 5. O.V. Maevsky, A.D. Pisarev, A.N. Busygin, S.Yu. Udovichenko, International journal of nanotechnology, 2018, 15(4/5), 388-393

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Organic Electronics for Neuromorphic Computing Yoeri van de Burgt X

Microsystems and Institute for Complex Molecular Systems, Eindhoven University of Technology * Corresponding author: [email protected]

The widely anticipated end to Moore’s law and the growing demand for low power computing systems capable of learning, image recognition and real-time analysis of large streams of unstructured (big) data has spurred intense interest in neural algorithms for brain-inspired computing. In recent years, the rapidly expanding field of neuromorphic computing attempts to address a number of inherent limitations of silicon technology for dedicated machine learning and internet of things applications. Sequential data processing by conventional von Neumann computer architectures has proven to be particularly inefficient for executing artificial neural network algorithms resulting in high energy cost and slow computation. This bottleneck can be circumvented by parallel computing. Despite relative success using dedicated parallel processing units such as graphical processing units and large crossbar-arrays of two-terminal memristive devices, these still lack in performance required to successfully implement compact hardwarebased neural networks and achieve the interconnectivity, information density, and energy efficiency of the brain. Organic electronic materials have shown potential to overcome some of these limitations. This talk describes state-of-the-art organic neuromorphic devices and provides an overview of the current challenges in the field and attempts to address them. We demonstrate a novel concept based on an organic electrochemical transistor and show how crucial challenges in the field such as stability, device-to-device variability and linearity can be overcome.

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Matrices of on-chip Pt/TaOx/Ta resistive switching memory devices for future bioelectronic applications Zhuk M.1, Negrov D.1, Matveyev Yu.2, Orlov O.M.3, Zenkevich A.V.1 1 2

Moscow Institute of Physics and Technology, MIPT, Dolgoprudny, Russia

Deutsches Elektronen-Synchrotron, Notkestraße 85 22607 Hamburg, Germany 3

Molecular Electronics Research Institute, Zelenograd, Russia

Future implantable neurointerface devices will require substantial computational and memory budget available in a very strict power envelope. Such capabilities are very difficult to provide using conventional memory architectures due to very high energy cost of memory interfaces. This problem can be circumvented by tightly coupling memory and computation, but this approach poses a difficulty – memories, which can be placed close to computation devices (e.g., static RAMs), are typically of very low density. Fortunately, a set of emerging memory technologies can overcome this difficulty by providing very low power nanoscale storage cells, which can be integrated directly into computing electronics, paving the way to perform processing in memory. One of the most promising technologies is the non-volatile memory based on the reversible resistive switching (RS) phenomena in a thin film of functional material upon application of external electric field (voltage). Among different transition metal oxides serving a functional layer in RS devices, TaOx is one of the favorites [1,2]. In this work, sub-um size cross-bar Pt/TaOx/Ta resistive switching (RS) devices were produced by magnetron sputtering technique at room temperature. The elemental and chemical composition of few-nm-thick functional TaOx (x~3) layer is revealed by the combination of Rutherford backscattering spectrometry and hard x-ray photoemission spectroscopy techniques. The proposed resistivity switching mechanism is based on the electrical drift of over-stoichiometric (wrt. Ta2O5) O ions. Such sub-um size RS devices were integrated with 1000x1000 matrices of 90-nm CMOS transistors to ensure accurate control of the so called compliance current across 1T-1R devices. For the optimal thickness 6 nm for TaOx layer 1T1R devices in the matrix exhibit robust “forming-free” RS, with more than 1010 cycles under pulsed switching (Von/Voff = 2.2/-2.2 V,1 us). The obtained matrices of 1T-1R devices will serve the basis for embedded non-volatile memory useful for future neurointerfaces under development. 1.

2.

M.-J. Lee, et al., A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat. Mater. 10 625 (2011). Anja Wedig et al., Nanoscale cation motion in TaOx, HfOx and TiOx memristive systems. Nat. Nanotech. 11 67 (2016).

MEM-Q-Abstract_Zhuk

27

2ND MEM-Q INTERNATIONAL WORKSHOP ” From ReRAM and Memristors to new Computing Paradigms” 28-31 October, 2018 AEGEAN PERL Hotel, RETHYMNO, Crete, GREECE Sunday, October 28th

2:00 PM

MEM-Q Consortium Meeting

5:00 PM

REGISTRATION

7:00 PM

Panos Dimitrakis Georgios Sirakoulis Ioannis Karafyllidis Vyacheslav Demin Alexey Mikhaylov

WELCOME RECEPTION

Panos Dimitrakis Georgios Sirakoulis Vyacheslav Demin Alexey Mikhaylov

Paper Title

Presenting/First Author (Last Name, First Name)

Monday, October 29th

Time

Workshop Assigned Paper No.

8:30 AM OPENING SESSION 9:00 AM 9:45 AM 10:00 AM ☺ 10:15 AM

P1

I1

Microscopic manipulation of materials’ properties for enhanced memristive functionalities Coffee Break Silicon as an enabler for device engineering in HfO2 based ReRAM : From material to array reliability.

Panos Dimitrakis Georgios Sirakoulis Vyacheslav Demin Alexey Mikhaylov Valov, Ilia

Barlas, Marios

10:45 AM

O1

11:00 AM

O2

11:15 AM

O3

11:30 AM

I2

12:00 AM 12:15 AM 12:30 AM

O4 O5 O6

12:45 AM

O7

1:00AM 2:30 PM ☺

Millisecond Flash Lamp Annealing for the stabilization of ferroelectric HfxZr1-xO2 Tailored switching algorithms for emerging applications of HfO2 based RRAM memory arrays Ultra-compact field enhanced electrochemical metallization cells achieving high speed atomic scale memristive switching Switching Dynamics of Filamentary RRAMs for Neural Computing Architectures Fundamental properties of HfOx based RRAM: Needs, issues and challenges Investigation of memristive properties of ZnxTiyHfzOi nanocomposites Local electrical properties of thin-layered oxide systems with I-V curve hysteresis or resistive switching effects observed by means of tunneling atomic force microscopy Emulation of physical-based models of memristive switching devices via resistive two-port approach Break

Halter, Mattia Perez, E.

Cheng, Bojun Brivio, Stefano Koveshnikov, Sergei Tominov, Roman Andreeva, Natalia

Vavra, Jiri

Time 2:30 PM

3:00 PM 3:15 PM 3:30 PM 3:45 PM 4:00 PM ☺

Workshop Assigned Paper No. I3 O8 O9 O10 O11 I4

4:15 PM O12 4:45 PM

Paper Title

Laser and Plasma Physics Laboratory at Technological Institute of Crete Photomemristive systems based on two-dimensional crystals for electronic neural networks Silicon Nitride RRAM devices with modified interfaces Fabrication and performance of LaMnO3±δ resistive switching devices for standard and advanced characterization A comprehensive study on the resistive switching behavior of Mg0.6Zn0.4O-based thin film devices Coffee break Superconducting qubit research in Russia and superconductor/ferromagnet structure applications for superconducting quantum and digital logics Modeling and simulation of quantum transport in nanoscale memristors

Presenting/First Author (Last Name, First Name) Tatarakis, Michael Panin, Gennady N. Dimitrakis, Panos Rodriguez-Lamas, Raquel Ismail, Muhammad

Ryazanov, Valery

Karafyllidis, Ioannis

Tuesday, October 30th

Time 9:00 AM 9:45 AM 10:00 AM ☺ 10:30 AM 11:00 AM 11:15 AM 11:30 AM

Workshop Assigned Paper No. P2

I5 O13 O14 I6

12:00 AM O15 12:15 AM 12:30 AM 1:00AM 2:30 PM ☺

O16 I7

Paper Title ReRAM challenges from lab to fab – the road from concept to manufacturing Coffee Break Organic materials for neuromorphic devices and architectures Plasticity of Metal-Organic Memristive Elements Based on Parylene Matrices of on-chip Pt/TaOx/Ta resistive switching memory devices for future bioelectronic applications Between Intelligence and Artificial Intelligence: Some much needed shake-up A biomorphic neuroprocessor based on the composite memristor diode crossbar Engineering building blocks for brain inspired computing — Multiscale modeling approach Memristive in memory computing: Past Challenges and Future Directions Break

Presenting/First Author (Last Name, First Name) Regev, Amir

Gkoupidenis, Paschalis Minnekhanov, Anton Zhuk, M. Strydis, Christos Bobylev, A.N. Pecic, Milan Sirakoulis, George

Time 2:30 PM

3:00 PM 3:00 PM 3:45 PM 4:00 PM ☺ 4:30 PM 4:45 PM 8:30 PM


Paper Title

I8

Organic Electronics for Neuromorphic Computing SPECIAL SESSION ON STOCHASTIC MULTISTABLE SYSTEMS

P3

Multistability and Metastability: Understanding Stochastic Dynamics in Condensed Matter

Presenting/First Author (Last Name, First Name) Burgt, Yoeri van de Panos Dimitrakis Bernardo Spagnolo Spagnolo, Bernardo

Coffee break O17 O18

UNN Laboratory of Stochastic Multistable Systems (StoLab) Dynamical and statistical properties of one simple model for resistive switching WORKSHOP MEETING DINNER

Rubtsov, Alexey Kharcheva, Anna

Wednesday, October 31th

Time


9:00 AM 9:00 AM

9:30 AM 9:45 AM 10:00 AM ☺ 10:15 AM 10:30 AM 10:45 AM

I9

O19 O20

O21 O22 O23

Paper Title SPECIAL SESSION ON STOCHASTIC MULTISTABLE SYSTEMS Spike-timing dependent plasticity in robust and noise-assisted learning of spiking neuron with memristive weights Noise and resistive switching in a contact of an atomic force microscope probe to yttria stabilized zirconia film on a conductive substrate Improvement of resistive switching reproducibility by stabilized oxygen exchange in Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti devices Coffee break Dynamic response of metal-oxide memristive devices to complex electric signals Stochastic models of memristors Impact of Line Edge Roughness on ReRAM uniformity and scaling CLOSING CEREMONY

Presenting/First Author (Last Name, First Name) Dubkov, Alexander Demin, Vyacheslav Filatov, Dmitrii

Mikhaylov, Alexey

Korolev, Dmitry Dubkov, Alexander Dimitrakis, Panos Panos Dimitrakis Georgios Sirakoulis Vyacheslav Demin Alexey Mikhaylov