Showing papers by "Ivan K. Schuller published in 2022"
TL;DR: This Perspective discusses select examples of quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems and provides an outlook on the current opportunities and challenges.
Abstract: Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
12 citations
TL;DR: In this article , numerical simulations and experiments reveal how thermal and electronic effects jointly contribute to the resistive collapse, and more surprisingly it is a Poissonian process with an exponential escape rate.
Abstract: The resistive switching induced in Mott materials by a strong applied voltage allows for artificial spiking neurons with great potential. However, controlling the resistive collapse is a challenge, as we lack physical understanding of the phenomenon. Here numerical simulations and experiments reveal how thermal and electronic effects jointly contribute to the phenomenon. The resistive collapse is intrinsically stochastic, and more surprisingly it is a Poissonian process with an exponential escape rate, just like the firing of actual biological neurons. This result provides an unexpectedly realistic aspect to the implementation of tomorrow's energy-efficient neurocomputing hardware.
11 citations
TL;DR: This work uses an electrical pump/probe protocol and takes advantage of the stochastic relaxation dynamics in VO2 to induce random metallization events and demonstrates that metal-insulator transitions can also be used for this purpose.
Abstract: Probabilistic computing is a paradigm in which data are not represented by stable bits, but rather by the probability of a metastable bit to be in a particular state. The development of this technology has been hindered by the availability of hardware capable of generating stochastic and tunable sequences of "1s" and "0s". The options are currently limited to complex CMOS circuitry and, recently, magnetic tunnel junctions. Here, we demonstrate that metal-insulator transitions can also be used for this purpose. We use an electrical pump/probe protocol and take advantage of the stochastic relaxation dynamics in VO2 to induce random metallization events. A simple latch circuit converts the metallization sequence into a random stream of 1s and 0s. The resetting pulse in between probes decorrelates successive events, providing a true stochastic digital sequence.
9 citations
TL;DR: In this article , current-induced spin-orbit torques in VO2/NiFe heterostructures were investigated using spin-torque ferromagnetic resonance, where the VO2 layer undergoes a prominent insulator-metal transition.
Abstract: The emergence of spin‐orbit torques as a promising approach to energy‐efficient magnetic switching has generated large interest in material systems with easily and fully tunable spin‐orbit torques. Here, current‐induced spin‐orbit torques in VO2/NiFe heterostructures are investigated using spin‐torque ferromagnetic resonance, where the VO2 layer undergoes a prominent insulator‐metal transition. A roughly twofold increase in the Gilbert damping parameter, α, with temperature is attributed to the change in the VO2/NiFe interface spin absorption across the VO2 phase transition. More remarkably, a large modulation (±100%) and a sign change of the current‐induced spin‐orbit torque across the VO2 phase transition suggest two competing spin‐orbit torque generating mechanisms. The bulk spin Hall effect in metallic VO2, corroborated by the first‐principles calculation of the spin Hall conductivity σSH≈−104ℏeΩ−1 m−1 , is verified as the main source of the spin‐orbit torque in the metallic phase. The self‐induced/anomalous torque in NiFe, with opposite sign and a similar magnitude to the bulk spin Hall effect in metallic VO2, can be the other competing mechanism that dominates as temperature decreases. For applications, the strong tunability of the torque strength and direction opens a new route to tailor spin‐orbit torques of materials that undergo phase transitions for new device functionalities.
7 citations
TL;DR: In this paper , the authors present a survey of the state of the art in bioinformatics and biomedicine research, including the following papers: http://www.firstpage
Abstract: First Page
7 citations
TL;DR: In this article , optically induced sensing of the resistive state of a memristor based on a La0.7Sr0.3MnO3/BaTiO 3/In2O3:SnO2 (90:10) heterostructure with a 3 nm thick BaTiO3 ferroelectric barrier is presented.
Abstract: Memristors based on oxide tunnel junctions are promising candidates for energy efficient neuromorphic computing. However, the low power sensing of the nonvolatile resistive state is an important challenge. We report the optically induced sensing of the resistive state of a memristor based on a La0.7Sr0.3MnO3/BaTiO3/In2O3:SnO2 (90:10) heterostructure with a 3 nm thick BaTiO3 ferroelectric barrier. The nonvolatile memristive response originates from the modulation of an interfacial Schottky barrier at the La0.7Sr0.3MnO3/BaTiO3 interface, yielding robust intermediate memristive states. The Schottky barrier produces a photovoltaic response when illuminated with a 3.3 eV UV LED, which depends on the state. The open circuit voltage Voc correlates linearly with the resistance of each state, enabling active sensing of the memristive state at light power densities as low as 20 mW/cm2 and temperatures up to 100 K. This opens up avenues for the efficient and minimally invasive readout of the memory states in hybrid devices.
6 citations
TL;DR: In this paper , a nonlocal spin Seebeck signal was found in VO2 films that appears below 30'K and that increases with a decrease in temperature, showing a nonhysteretic dependence on the in-plane external magnetic field.
Abstract: The low temperature monoclinic, insulating phase of vanadium dioxide is ordinarily considered nonmagnetic, with dimerized vanadium atoms forming spin singlets, though paramagnetic response is seen at low temperatures. We find a nonlocal spin Seebeck signal in VO2 films that appears below 30 K and that increases with a decrease in temperature. The spin Seebeck response has a nonhysteretic dependence on the in-plane external magnetic field. This paramagnetic spin Seebeck response is discussed in terms of prior findings on paramagnetic spin Seebeck effects and expected magnetic excitations of the monoclinic ground state.
6 citations
TL;DR: In this article , an optically induced resistive switching based on a CdS/V3O5 heterostructure is proposed to overcome the stochastic nature of the ion migration.
Abstract: Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. In most of the cases, an electric field is applied to a two terminal dielectric material device and leads to the formation of a low resistance filament due to ion migration. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. Here, we report an optically induced resistive switching based on a CdS/V3O5 heterostructure which can overcome this issue. V3O5 is known to have a second order insulator to metal transition around Tc ≈ 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V3O5 produces a nonvolatile resistive switching at room temperature. The initial high resistance can be recovered by reaching the high temperature metallic phase, i.e., temperatures above Tc. Interestingly, this resistive switching becomes volatile around the Tc. By locally manipulating the volatile and nonvolatile resistive switching using electric field and light, this system is a promising platform for hardware based neuromorphic computing implementations.
3 citations
TL;DR: In this paper , the authors compared the properties of FeSi with those of the Kondo insulator SmB6 to address the question of whether FeSi is a d-electron analogue of an felectron Kondo-insulator and, in addition, a "topological Kondoinsulator" (TKI).
Abstract: Recently, evidence for a conducting surface state (CSS) below 19 K was reported for the correlated d-electron small gap semiconductor FeSi. In the work reported herein, the CSS and the bulk phase of FeSi were probed via electrical resistivity ρ measurements as a function of temperature T, magnetic field B to 60 T, and pressure P to 7.6 GPa, and by means of a magnetic field-modulated microwave spectroscopy (MFMMS) technique. The properties of FeSi were also compared with those of the Kondo insulator SmB6 to address the question of whether FeSi is a d-electron analogue of an f-electron Kondo insulator and, in addition, a "topological Kondo insulator" (TKI). The overall behavior of the magnetoresistance of FeSi at temperatures above and below the onset temperature TS = 19 K of the CSS is similar to that of SmB6. The two energy gaps, inferred from the ρ(T) data in the semiconducting regime, increase with pressure up to about 7 GPa, followed by a drop which coincides with a sharp suppression of TS. Several studies of ρ(T) under pressure on SmB6 reveal behavior similar to that of FeSi in which the two energy gaps vanish at a critical pressure near the pressure at which TS vanishes, although the energy gaps in SmB6 initially decrease with pressure, whereas in FeSi they increase with pressure. The MFMMS measurements showed a sharp feature at TS ≈ 19 K for FeSi, which could be due to ferromagnetic ordering of the CSS. However, no such feature was observed at TS ≈ 4.5 K for SmB6.
3 citations
TL;DR: In this article , the role of percolation in metal-insulator transition (MIT) was investigated using scanning microwave impedance microscopy to directly determine the metallic phase fraction p and relate it to the macroscopic conductance G.
Abstract: Using the extensively studied V2O3 as a prototype system, we investigate the role of percolation in metal-insulator transition (MIT). We apply scanning microwave impedance microscopy to directly determine the metallic phase fraction p and relate it to the macroscopic conductance G, which shows a sudden jump when p reaches the percolation threshold. Interestingly, the conductance G exhibits a hysteretic behavior against suggesting two different percolating processes upon cooling and warming. Based on our image analysis and model simulation, we ascribe such hysteretic behavior to different domain nucleation and growth processes between cooling and warming, which is likely caused by the decoupled structural and electronic transitions in V2O3 during MIT. Our work provides a microscopic view of how the interplay of structural and electronic degrees of freedom affects MIT in strongly correlated systems.
2 citations
TL;DR: This tutorial describes the present state of thermal management of neuromorph computing technology and describes the challenges and opportunities of the energy-efficient implementation of neuromorphic devices.
Abstract: Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating searching for hardware-based systems that imitate the brain. This tutorial describes the present state of thermal management of neuromorphic computing technology and describes the challenges and opportunities of the energy-efficient implementation of neuromorphic devices. The introduction is presented in chapter 1, where we briefly describe the main features of brain-inspired computing and quantum materials for implementing neuromorphic devices. In chapter 2 we discuss the brain criticality and resistive switching-based neuromorphic devices. Chapter 3 presents the energy and electrical considerations for spiking-based computation. We address the fundamental features of the brain's thermal regulation in chapter 4. Hereafter, we analyse the physical mechanisms for thermal management (chapter 5) and thermoelectric control of materials and neuromorphic devices (chapter 6). At the end we describe challenges and new avenues for implementing energy-efficient computing. This article is protected by copyright. All rights reserved.
TL;DR: In this paper , the influence of controlled defects on the magnetic properties of La0.67Sr0.33MnO3 (LSMO) thin films has been studied and it has been found that irradiation reduces the ferromagnetic ordering temperature, decreases the total magnetization, enhances the coercivity, and induces exchange bias below 50 K.
TL;DR: In this article , it was shown that the magnetic domain structure during the magnetization reversal of the FM layer controls the exchange bias (EB) phenomena exploited in most spintronic devices, although still is lack of full understanding.
Abstract: Interfacial proximity effects in antiferromagnetic/ferromagnetic (AFM/FM) bilayers control the exchange‐bias (EB) phenomena exploited in most spintronic devices, although still is lack of full understanding. Discordant results, including different exchange‐bias field (HE), coercivity (HC), or blocking temperature (TB) found even in similar systems, are usually ascribed to uncontrolled parameters, namely dissimilar interfacial defects, structure, and thicknesses. Here, it is shown in the very same sample that the magnetic domain structure during the magnetization reversal of the FM layer controls those mentioned effects. Simultaneous transport and vectorial‐resolved magnetic measurements performed in a V2O3/Co system during warming after different field cooling (FC) procedures exhibit a strong dependence on the FC angle and the domain structure of the FM layer. Remarkably, magnetization reversal analysis reveals 35 K of variation in TB and up to a factor of two in HE. These observations can be explained within the random‐field model for the interfacial exchange coupling with a fixed AFM domain structure in contact with a variable (angle‐dependent) FM domain structure. The results highlight the importance of the domain structure and magnetization reversal of the FM layer (not previously considered) in the EB phenomena, with potential to tailor interfacial effects in future spintronic devices.
TL;DR: In this article , a model based on atomic vibration instability was proposed to describe the variation of magnetometry properties with nanoparticle size. But the model was not applied to the case of multiferroic BFO nanoparticles.
TL;DR: In this paper , a convolutional neural network (CNN) was used to detect the fractal intertwining of metal and insulator domains during a metal-insulator transition.
Abstract: The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length and time scales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder -- a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of VO2. We then apply our trained convolutional neural network architecture to new optical microscope images of a different VO2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way to using the model to control the pattern formation via, e.g., tailored hysteresis protocols. We also introduce a distribution-based confidence measure on the results of a multi-label classifier, which does not rely on adversarial training. In addition, we propose a new machine learning based criterion for diagnosing a physical system's proximity to criticality.
TL;DR: In this paper , the structural stability of virus-host interactome networks based on the graphical representation of virus−host protein interactions as vertices or nodes connected by commonly shared proteins is studied.
Abstract: Abstract Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus–host interactome networks based on the graphical representation of virus–host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus–host interaction merges with virus–protein and host–protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus–host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus–host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.
TL;DR: In this article , the authors designed a nanostructured material that exhibits very unusual hysteresis loops due to a transition between vortex and double pole states, and they found this interaction mainly mediated by magnetostatic interaction that favors antiparallel alignment of the Py layers and exchange interaction that oscillates between ferromagnetic and AFM couplings.
Abstract: Controlling the magnetic ground states at the nanoscale is a long-standing basic research problem and an important issue in magnetic storage technologies. Here, we designed a nanostructured material that exhibits very unusual hysteresis loops due to a transition between vortex and double pole states. Arrays of 700 nm diamond-shaped nanodots consisting of Py(30 nm)/Ru(tRu)/Py(30 nm) (Py, permalloy (Ni80Fe20)) trilayers were fabricated by interference lithography and e-beam evaporation. We show that varying the Ru interlayer spacer thickness (tRu) governs the interaction between the Py layers. We found this interaction mainly mediated by two mechanisms: magnetostatic interaction that favors antiparallel (antiferromagnetic, AFM) alignment of the Py layers and exchange interaction that oscillates between ferromagnetic (FM) and AFM couplings. For a certain range of Ru thicknesses, FM coupling dominates and forms magnetic vortices in the upper and lower Py layers. For Ru thicknesses at which AFM coupling dominates, the magnetic state in remanence is a double pole structure. Our results showed that the interlayer exchange coupling interaction remains finite even at 4 nm Ru thickness. The magnetic states in remanence, observed by magnetic force microscopy (MFM), are in good agreement with corresponding hysteresis loops obtained by the magneto-optic Kerr effect (MOKE) and micromagnetic simulations.
TL;DR: In this paper , a combination of experiments and theory is used to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra, and the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change in the relative peak positions of the O K-edge XA spectra.
Abstract: Transition metal oxides (TMOs) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentration, whose experimental determination remains a challenging task. Here, we focus on cobaltites, in particular La1–xSrxCoO3−δ (LSCO), and present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiments and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change in the relative peak positions of the O K-edge XA spectra. We also identify an additional geometrical fingerprint that captures both the changes in the Co–O bond length and Co–O–Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration, in the absence of any structural transformation. Our study shows that, in order to predict the complex transport properties of TMOs, it is crucial to gain a detailed understanding of their oxygen defect density.
01 May 2022
TL;DR: In this article , a VO2-based phase-change metasurface self regulates its emissivity in response to the ambient temperature, which increases to near unity when operating above the insulator-to-metal phase transition temperature.
Abstract: We demonstrate a VO2 based phase-change metasurface that self regulates its emissivity in response to the ambient temperature. The mid-infrared emissivity increases to near unity when operating above the insulator-to-metal phase transition temperature of VO2.
TL;DR: In this article , the electric and magnetic properties of a sample with complex electromagnetic responses are investigated by taking a series of magnetic hysteresis loops and magnetoresistance measurements, which can be compared to MFMMS data to identify features having electric or magnetic origin.
Abstract: Low-field microwave absorption techniques are ultrasensitive, nondestructive methods for probing electric and magnetic properties of solids. Nonresonant low-field microwave absorption techniques such as magnetic field modulated microwave spectroscopy (MFMMS) can easily detect electromagnetic phase transitions in minute and inhomogeneous samples. While this technique can easily and almost selectively identify superconducting transitions, magnetic phase transitions produce more varied responses. Here, we present a technique to investigate the electric and magnetic properties of a sample with complex electromagnetic responses. This technique involves taking a series of magnetic hysteresis loops and magnetoresistance measurements. These can be compared to MFMMS data to identify features having electric or magnetic origin. This approach is applied to magnetite $({\mathrm{Fe}}_{3}{\mathrm{O}}_{4})$, which possesses an electric, magnetic, and structural phase transition across its Verwey transition. By measuring high-quality ${\mathrm{Fe}}_{3}{\mathrm{O}}_{4}$ thin films in MFMMS and complementary techniques, the previously inscrutable MFMMS signal is analyzed. Furthermore, a model of the MFMMS signal can be calculated from the magnetic and electric data, which reproduces most of the features of the experimentally obtained MFMMS signal. This technique broadens the capabilities of MFMMS beyond the detection of superconductors.
TL;DR: In this paper , Hoffmann et al. presented an overview of the challenges and opportunities of nanoscience research and proposed a framework to address the challenges in the context of quantum computing.
Abstract: Opportunities and challenges Axel Hoffmann,1 Shriram Ramanathan,2 Julie Grollier,3 Andrew D. Kent,4 Marcelo Rozenberg,5 Ivan K. Schuller,6, 7 Oleg Shpyrko,6 Robert Dynes,6 Yeshaiahu Fainman,8 Alex Frano,6 Eric E. Fullerton,9, 8 Giulia Galli,10, 11 Vitaliy Lomakin,9, 8 Shyue Ping Ong,12 Amanda K. Petford-Long,11, 13 Jonathan A. Schuller,14 Mark D. Stiles,15 Yayoi Takamura,16 and Yimei Zhu17 1)Materials Research Laboratory and Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA 2)School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, USA 3)Unité Mixte de Physique CNRS/Thales, Université Paris-Saclay, 91767 Palaiseau, France 4)Center for Quantum Phenomena, Department of Physics, New York University, New York 10003, USA 5)Université Paris-Saclay, CNRS Laboratoire de Physique des Solides, Orsay 91405, France 6)Department of Physics, University of California–San Diego, La Jolla, California 92093, USA 7)Center for Advanced Nanoscience, University of California–San Diego, La Jolla, California 92093, USA 8)Department of Electrical and Computer Engineering, University of California–San Diego, La Jolla, California 92093, USA 9)Center for Memory and Recording Research, University of California–San Diego, La Jolla, California 92093, USA 10)Pritzker School of Molecular Engineering and Department of Chemistry, The University of Chicago, Chicago Illinois 60637, USA 11)Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA 12)Department of NanoEngineering, University of California–San Diego, La Jolla, California 92093, USA 13)Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, USA 14)Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA 15)Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-6202, USA 16)Department of Materials Science and Engineering, University of California, Davis, Davis, California 95616, USA 17)Department of Consdensed Matter Physics and Materials Science, Brookhaven National Laboratory, Upton, New York 11973, USA
TL;DR: Fukami et al. as discussed by the authors proposed a probabilistic computing circuit that can solve optimization problems using stochastic, hence volatile, nonvolatile working memory based on magnetic tunnel junction (MTJ).
Abstract: Spintronics nonvolatile working memory based on magnetic tunnel junction (MTJ) has been shown to reduce the power of CMOS-based microprocessors orders of magnitude, making it suitable for IoT [1], AI, and other applications. Such MTJs are scalable down to 2.3 nm without resorting to new materials [2-5], an essential feature for future development. When we make MTJs volatile , a new computing scheme emerges that could solve problems potentially more efficiently than silicon-based digital information processing. We have made a proof-of-concept probabilistic computing circuit that can solve optimization problems using stochastic, hence volatile, MTJs [6]. The time scale involved in these stochastic MTJs has been addressed [7, 8]. It also provides a means to experimentally access the switching exponents under magnetic field and current [9]. If time allows, I will touch upon a neuromorphic approach to mimic a “spiking neural network” where a short time constant is involved [10]. Work done in collaboration with S. Fukami, S. Kanai, B. Jinnai, and the CSIS team and supported in part by, JST-OPERA
TL;DR: Díez et al. as mentioned in this paper demonstrate a general mechanism controlling the exchange bias phenomena in antiferromagnetic/ferromagnetic (AFM/FM) heterostructures, which allows to enhance the existing devices relying on exchange bias or to design innovative ones that were not possible before.
Abstract: Interfacial Exchange Phenomena Driven by Ferromagnetic Domains In article number 2200331, José Manuel Díez, José Luis F. Cuñado, Julio Camarero, and co-workers demonstrate a new general mechanism controlling the exchange bias phenomena in antiferromagnetic/ferromagnetic (AFM/FM) heterostructures: the magnetic texture of the FM layer during reversal determines and controls the exchange bias, its temperature dependence, and other related phenomena. It allows to enhance the existing devices relying on exchange bias or to design innovative ones that were not possible before.
TL;DR: In this paper , it was shown that the ramp reversal memory (RRM) is the outcome of a local increase in transition temperature of the microscopic-scale phase boundaries that are created during temperature ramp reversal (from heating to cooling) within the insulator-metal phase coexistence regime.
Abstract: The recently discovered ramp reversal memory (RRM) is a nonvolatile memory effect observed in correlated oxides with temperature-driven insulator--metal transitions (IMT). It appears as a resistance increase at predefined temperatures that are set or erased by simple heating--cooling (i.e., ramp reversal) protocols. Until now RRM was measured for two materials: ${\mathrm{VO}}_{2}$ and ${\mathrm{NdNiO}}_{3}$. A heuristic model suggests that the RRM is caused by a local transition temperature increase at boundaries of spatially separated metallic and insulating domains during ramp reversal. However, there is no experimental measure of the magnitude of the effect, which is crucial for the development of a theoretical account of the RRM. Here we show that ${\mathrm{V}}_{2}{\mathrm{O}}_{3}$ also shows RRM, including all related features, highlighting the generality of the effect. Moreover, an analysis of the RRM as an effective (average) increase of the critical temperature provides a quantitative measure of its magnitude as a function of temperature and ramp reversal protocols. We provide clear evidence that the RRM is the outcome of a local increase in transition temperature of the microscopic-scale phase boundaries that are created during temperature ramp reversal (from heating to cooling) within the insulator--metal phase coexistence regime.