Subfilamentary Networks Cause Cycle-to-Cycle Variability in

Jun 29, 2017 - The repetition rate was 5 Hz, and the spot size was 1.5 mm2 at a target-to-substrate distance of 60 mm in an oxygen atmosphere of 10–...
0 downloads 12 Views 7MB Size
Subfilamentary Networks Cause Cycle-to-Cycle Variability in Memristive Devices Christoph Baeumer,*,† Richard Valenta,† Christoph Schmitz,† Andrea Locatelli,‡ Tevfik Onur Menteş,‡ Steven P. Rogers,§ Alessandro Sala,‡ Nicolas Raab,† Slavomir Nemsak,† Moonsub Shim,§ Claus M. Schneider,† Stephan Menzel,† Rainer Waser,†,∥ and Regina Dittmann† †

Peter Gruenberg Institute, Forschungszentrum Juelich GmbH and JARA-FIT, 52425 Juelich, Germany Elettra-Sincrotrone, S.C.p.A, S.S 14-km 163.5 in AREA Science Park, Basovizza, 34149 Trieste, Italy § Department of Materials Science and Engineering and Materials Research Laboratory, University of Illinois, Urbana, Illinois 61801, United States ∥ Institute for Electronic Materials, IWE2, RWTH Aachen University, 52074 Aachen, Germany ‡

S Supporting Information *

ABSTRACT: A major obstacle for the implementation of redox-based memristive memory or logic technology is the large cycle-to-cycle and device-to-device variability. Here, we use spectromicroscopic photoemission threshold analysis and operando XAS analysis to experimentally investigate the microscopic origin of the variability. We find that some devices exhibit variations in the shape of the conductive filament or in the oxygen vacancy distribution at and around the filament. In other cases, even the location of the active filament changes from one cycle to the next. We propose that both effects originate from the coexistence of multiple (sub)filaments and that the active, current-carrying filament may change from cycle to cycle. These findings account for the observed variability in device performance and represent the scientific basis, rather than prior purely empirical engineering approaches, for developing stable memristive devices. KEYWORDS: resistive switching, memristive devices, variability, PEEM, graphene

R

such as oxygen vacancies, which may tune the resistance of the oxide layer or the resistance of the electrode/oxide interface.1,9,10 The stochastic nature of the resulting conductive filament has been suspected phenomenologically to result from an interplay of the thermodynamic stability of the filament,11 generation/recombination effects of oxygen vacancies,12 and especially the shape and oxygen vacancy distribution of the conductive filament.13−15 It has therefore been attempted to achieve higher switching uniformity employing filament precursors16 or preferred sites for oxygen vacancy enrichment.17,18 Importantly, and despite the promising results obtained empirically, changes of the microscopic structure of a single conductive filament, which might cause the C2C variability in redox-based memristive devices, have not yet been directly observed in experiment. In addition, it has to be considered if the location of the conductive filament may

edox-based memristive devices are one of the most attractive emerging memory technologies in terms of scaling, power consumption, and speed, even allowing multibit operation, logic-in-memory applications, and neuromorphic computing architectures.1−6 In these devices, external electrical stimuli cause changes of the resistance of an oxide layer sandwiched between two metal electrodes. In the simplest application, the device can be set into a low resistance state (LRS) and reset into a high resistance state (HRS), which may encode a logical one and zero, respectively. The major obstacle delaying large-scale implementation of memristive devices into state-of-the art memory or logic applications, however, is their large cycle-to-cycle (C2C) and device-to-device (D2D) variability of both LRS and HRS resistance values.7,8 These variabilities, which typically follow a log-normal distribution,8 describe the stochastic nature of the switching process within one cell, resulting in different resistances obtained for each switching cycle and different resistances obtained for different cells on the same chip. The switching process in transition metal oxides is believed to be driven by the nanoscale motion of donor-type defects © 2017 American Chemical Society

Received: March 27, 2017 Accepted: June 28, 2017 Published: June 29, 2017 6921

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

www.acsnano.org

Article

ACS Nano

(negative bias at the top electrode).10,25 The C2C variability of these memristive devices can be seen in the resistances obtained during 150 switching cycles (Figure 1c,d), which show a rather typical distribution of obtained LRS and HRS values with median resistance values of 5.7 × 104 and 1.2 × 108 Ω, respectively. These values are typical of SrTiO3 cells operated at high current compliances.10,25,26 The standard deviations normalized to the mean resistance values 0.042 and 0.056, respectively, which are typical values for redox-based memristive devices.7 In pulsed operation, we obtain similar distributions (Supporting Information, Figure S1). For operation at different current compliances, we obtain an inverse relationship between the LRS resistance value and the current compliance; that is, higher currents lead to lower resistance states (Supporting Information, Figure S1). As is typically observed,13 the normalized standard deviation increases steeply with the LRS resistance value, reaching 0.4 for 0.5 mA current compliance, indicating that lower programming currents lead to increased C2C variability. Interestingly, the HRS resistance distributions remain similar for all current compliances tested. Presumably, these observations can be explained by smaller LRS filaments created with lower currents,13 whereas the HRS appears to be dominated by area-dependent leakage, which is independent of the filament size. Even for a filament-dominated HRS resistance, one would expect the variability to be independent of the current compliance because more conductive filaments are easier to reoxidize due to the increase in temperature during Reset.27 In the following, we will focus on results obtained with comparably high current compliances to facilitate the observation of the filament. The variability mechanisms derived from these measurements also have to be considered for lowpower and highly scaled devices. We investigated these devices using photoelectron emission microscopy (PEEM) aiming at verifying the microscopic origin of the observed variability. To this purpose, we carried out combined XPEEM measurements (Nanospectroscopy beamline at Elettra Synchrotron Laboratory, Trieste, Italy28) and threshold photoemission PEEM measurements using illumination by a mercury lamp (NanoESCA, Scienta Omicron GmbH, Taunusstein, Germany). For the threshold photoemission analysis, several devices were switched into the LRS or HRS using the quasistatic I−V sweeps shown in Figure 1 in air and mounted into the PEEM for each separate switching step as no biasing is possible within this instrument, whereas the XPEEM measurements were performed operando employing the same Set and Reset operations within the instrument. A threshold photoemission PEEM image of a representative graphene/Al2O3/SrTiO3/Nb:SrTiO3 device in the LRS is shown in Figure 2a. Here, the Al2O3 layer was incorporated to guarantee sufficient retention of the device (compare Methods for details).29 Within the rather homogeneous graphene area, a region with enhanced contrast is clearly visible. The intensity of this feature varies reversibly with the resistance state after additional Reset and Set operations (Figure 2a−d), substantiating the hypothesis that this feature is the conductive filament and demonstrating that this contrast did not arise from inhomogeneities in the graphene layer or residues from the transfer and photolithography processes. Extracting threshold photoemission spectra from the filament and the surrounding allows the determination of the energy difference between the secondary electron cutoff (the rising edge of the spectra) and the Fermi level. The spectra were

change during operation of redox-based memristive devices,19,20 as has been previously shown for so-called electrochemical metallization (ECM) cells,21,22 where also highly nonsymmetrical filament shapes have been observed.23,24 In this article, we employ spectromicroscopic photoemission threshold analysis and operando XAS analysis on the recently introduced model system of graphene/SrTiO3/Nb:SrTiO3 memristive devices10 to identify the microscopic origin of variability. We find that a change of the shape of the conductive filament or variations in the oxygen vacancy distribution within the filament are indeed responsible for the observed variability. Interestingly, we even observe that the location of the active filament may change from one cycle to the next, suggesting that each switching event can be understood as a competition between multiple filaments.

RESULTS AND DISCUSSION The device schematic of our model system and a representative I−V curve are shown in Figure 1a,b. As we demonstrated recently, the graphene top electrodes employed here allow the characterization of buried layers using surface-sensitive electron spectromicroscopy, as graphene scarcely dampens the intensity of low-energy photoelectrons.10 The switching mechanism in such devices has been demonstrated to be based on oxygen removal from the SrTiO3 lattice during the current-compliancelimited Set operation (positive bias at the top electrode) and oxygen reincorporation during the gradual Reset operation

Figure 1. (a) Schematic of the device geometry. A SrTiO3 layer (blue) is sandwiched between a Nb:SrTiO3 bottom electrode (dark gray) and graphene top electrode (gray honeycomb lattice). The graphene electrode is contacted through a metal lead, which is electrically separated from the continuous bottom electrode, allowing for biasing inside PEEM instruments. (b) Quasistatic I− V curve of a representative graphene/SrTiO3/Nb:SrTiO3 device operated with a current limit of 30 mA controlled by Keithley 2611A source meter. The bottom electrode serves as virtual ground, whereas the bias is applied to the graphene top electrode. (c) LRS and HRS resistance extracted from 150 switching cycles of the same device (resistance value at 80 mV). (d) Weibull plot of the resistance distribution for the measurement shown in (c). 6922

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano

Figure 3. (a) Average value of the device resistance before and after each PEEM measurement as a function of the photoemission threshold difference ΔΦ between filament and reference area. The error bars indicate the measured resistances before and after the PEEM measurement. The dotted line is a guide for the eye only. The average value is used because the device resistance changed during investigation. (b) Schematic band diagram for the LRS and HRS demonstrating how the SrTiO3 photoemission threshold, valence band (VB), and conduction band (CB) shift for different oxygen vacancy concentrations. Occupied states are indicated in black, and unoccupied states are white. Panel (b) was modified with permission from ref 30. Copyright 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Figure 2. (a) PEEM image of a graphene/Al2O3/SrTiO3 device in the LRS at an electron energy E − EF of 3.4 eV. Scale bar, 5 μm. (b) PEEM image of the same device after Reset. (c,d) PEEM images after one additional Set and Reset operation, respectively. Insets: Magnified photoemission threshold maps of the area around the conductive filament. The maps were obtained by fitting the threshold spectrum for each pixel.31 (e) Threshold photoelectron spectra extracted from the filament and a reference region close to the filament. (f) Evolution of device resistance during the switching cycle for the PEEM investigation.

fitted with a convolution of Gauss and Heaviside functions to extract the relative energy of the photoemission threshold.30−32 As surface adsorbates may have a large impact on the absolute value of the photoemission threshold,33 only relative changes between the filament and the unaltered surrounding will be considered in the following. Figure 2e shows that for both LRS and HRS, the photoemission threshold of the filament ΦF is significantly lower than the reference photoemission threshold of the unaltered surrounding ΦR. It is evident that in the LRS, the energy difference ΔΦ = ΦF − ΦR is significantly more negative than that in the HRS. Figure 2f indicates that the resistances obtained during these cycles before each PEEM measurement fall into the same range as the resistance distribution shown in Figure 1c,d. Comparing the average value of the resistances (R) measured before and after the PEEM experiment with ΔΦ, it can be concluded that the change in the photoemission threshold tracks the actual device resistance. Essentially, an almost exponential dependence between ΔΦ and R is observed

(Figure 3a). The photoemission threshold of the LRS is approximately 0.10 to 0.15 eV lower than that for the HRS, indicating a direct correlation between photoemission threshold and device resistance. This correlation can be understood based on the band alignment in the device and the relative position of the Fermi energy in the SrTiO3 band gap. In the LRS, the presence of oxygen vacancies is known to lower the Schottky barrier at the electrode/oxide interface1 as they are shallow donors in SrTiO3, and their presence at the oxide/electrode interface shifts the Fermi energy closer to the SrTiO 3 conduction band edge. This shift of the Fermi energy toward the vacuum level results in more mobile carriers in the conduction band and a reduction of the photoemission threshold (Figure 3b). As most typically considered conduction mechanisms across a Schottky barrier, such as thermionic emission or field emission, depend on the barrier height in an exponential manner, the exponential dependence between observed ΔΦ and measured R is in line with this interpretation. 6923

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano Even though this trend is based only on a limited number of data points and the simplified assumption of exponential dependence, the observed shift of the threshold photoemission spectra corresponds well to our earlier work, where we estimated a difference in the barrier height of 0.07 eV in addition to a significant barrier narrowing for the LRS based on the concentration of oxygen vacancies in the filament.10 Together, barrier lowering and narrowing are in good agreement with the photoemission threshold differences observed in this work. As the photoemission threshold determined by PEEM experiments typically corresponds to the topmost layer, one may question whether or not the graphene layer, rather than the SrTiO3, may be responsible for the observed changes. However, our experimental observation contradicts the expected shift of the graphene work function (Supporting Information, Figure S2).34 It therefore appears that the measured photoemission threshold represents the band alignment in the SrTiO3 layer. This interpretation also corresponds to the observation that the X-ray absorption analysis of the SrTiO3 layer is possible through the graphene electrode, indicating that the secondary electrons used to acquire the PEEM image pass through the graphene (ref 10 and Supporting Information, Figure S3). For the device under investigation, our X-ray absorption analysis shows that the identified filament indeed exhibits the characteristic signature of reduced SrTiO3, validating the interpretation that this is the conductive filament (Supporting Information, Figure S3). Additional, small filaments below the lateral resolution may exist in the cell, but strongly reduced features, which might contribute to the current flow of the device, should still be visible in the PEEM image in Figure 2a (broadened because of the limited resolution) due to the strong contrast. No such features are present in this device, further supporting the assignment of the conductive filament. Coming back to the question of C2C variability, it is very obvious from this experiment that the microscopic details of the filament change from cycle to cycle and may in turn be responsible for differences in the resistance. Figure 3a shows that LRS II exhibits a lower filament photoemission threshold and a lower resistance than LRS I. The same is true for HRS I and HRS II. This trend therefore confirms that changes in the oxygen vacancy concentration at the interface may be responsible for the C2C variability. In addition, the shape of the filament and the distribution of regions of different photoemission threshold therein are significantly different in each state (insets of Figure 2a−d), confirming that the shape of the filament can vary, as well. Operando XPEEM analysis of a similar device shows a noncircular, irregular filament shape (Figure 4a−d) of approximately 200 nm in diameter, similar to recent findings for filaments in Ta2O5 and HfO2.35 Here, we chose a sample without an additional Al2O3 layer to allow unambiguous interpretation of the O K-edge; sufficient retention was guaranteed through the use of Sr-rich SrTiO3 (see Methods for details).26 The filament observed here exhibits a rather complex shape and varying oxygen vacancy concentration across and around its core, as is evident from the map of the O K-edge A/B2 peak ratio plotted in Figure 4c,d. Within the filament, there are regions of very different (high or medium) vacancy concentration. On average, the oxygen vacancy concentration is higher in the LRS than in the HRS, as is evident from the reduced peak A in the O K-edge spectrum and

Figure 4. (a) PEEM image of a graphene/SrTiO3/Nb:SrTiO3 device under consideration with a photon energy of 538.2 eV. The diagonal brighter stripe is the footprint of the synchrotron beam. (b) PEEM image of the same device imaged at a higher magnification (photon energy of 538.2 eV). The red arrow indicates the conductive filament. (c) Image representing the ratio of LRS PEEM images acquired at photon energies of 530.2 (A) and 537.1 eV (B2), as indicated by the labels in the O K-edge spectrum shown in (e). (d) Image representing the ratio of HRS PEEM images acquired at photon energies of 530.2 (A) and 537.1 eV (B2). (e) O K-edge for the filament (averaged over the entire filament) in the LRS (red line) and the surrounding device area (black line). (f) O K-edge for the filament in the HRS (blue line) and the surrounding device area (black line) extracted from the same ROI as in (e). (g) Ti L-edge for the filament in the LRS (red line) and the surrounding device area (black line). (h) Ti L-edge for the filament in the HRS (blue line) and the surrounding device area (black line). All were background corrected by a linear fit of the pre-edge and subsequent normalization by fitting a third-order polynomial to the post-edge using the Athena software.36

the reduction of the first peak of the Ti L-edge spectrum (Ti L3, t2g peak) (Figure 4e−h). From a comparison of the A/B2 peak 6924

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano ratio with reference measurements, the Ti3+ concentration can be deduced.10 In the LRS, the average Ti3+ concentration is about 9%, in good agreement with our previous findings.10 For the red area visible in Figure 4c, however, we find even a higher concentration, approximately 24% Ti3+ (Supporting Information, Figure S4). This observation confirms that conductive filaments do not necessarily exhibit a symmetric shape with a homogeneous gradient from a high oxygen vacancy concentration in the center to a low concentration at the edge but rather possess a fine structure with regions with higher and lower vacancy concentration. We expect that the filament fine structure could be more complex than shown in Figure 4, demanding even higher lateral resolution to be fully resolved. When repeating the threshold photoemission investigation for a second device (Figure 5), we found further evidence supporting the aforementioned C2C variability in memristive devices. During the first switching cycle (Set to LRS I and Reset to HRS I), we found a conductive filament in the center of the device, which exhibited the expected increase of the filament

photoemission threshold during Reset (Figure 5a,b,e). Surprisingly, at the following Set operation, the filament photoemission threshold did not change; it remained indicative of the HRS state. Instead, we found that a new filament with a low photoemission threshold had formed at the edge of the device (Figure 5c). During Reset, the photoemission threshold of this filament increased (Figure 5c,d,f), corresponding to a decrease in the oxygen vacancy concentration. Based on the observation of a change in the position of the conductive filament during repeated cycling, it is clear that the LRS and HRS may vary from filament to filament, which may account for the observation of multimodal resistance distributions.19,37 Summarizing, the filamentary structure observed in our devices is clearly more complex than so far assumed. Each filament possesses a nonsymmetric shape and a fine structure of higher and lower oxygen vacancy concentrations in the lateral direction. Additionally, we found that more than one location within the device area may exhibit an increased amount of oxygen vacancies. It is noteworthy that for both devices shown in Figures 2 and 5, many small regions of enhanced contrast appear upon repeated cycling, indicating that a multitude of such reduced regions can develop, which can be thought of as prefilaments. The PEEM investigation shows that in the LRS, these prefilaments possess an intermediate photoemission threshold lower than their surroundings but still much higher than the conductive filament, indicating that the current flow will still be dominated by the main filament (Figure 6a−c). During Reset, these prefilaments are reoxidized (Figure 6d).

Figure 5. (a) PEEM image of another graphene/Al2O3/SrTiO3 device in the LRS at an electron energy E − EF of 3.45 eV. Scale bar, 5 μm. (b) PEEM image after a Reset operation. (c,d) PEEM images after additional Set and Reset operations, respectively. Insets: Magnified photoemission threshold map of the area around the suspected conductive filaments. (e) Threshold photoelectron spectra extracted from the filament active during the first switching cycle. (f) Threshold photoelectron spectra extracted from the filament active during the second switching cycle.

Figure 6. (a) PEEM image of the LRS II of the device in Figure 2. Colored rectangles indicate regions of interest (ROIs) for extracted spectra. (b) Photoemission spectra extracted from the ROIs indicated in (a). For the filament indicated in red, a much more pronounced threshold shift is evident than for the other features. (c) Photoemission threshold map for the entire device area confirming the observations in panel (b). (d) Photoemission threshold differences between LRS II and HRS II for each feature indicated in (a). 6925

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano

and more conductive, resulting in a self-accelerating process, as their growth is facilitated at higher temperature. Here, the temperature increase due to Joule heating of neighboring prefilaments may also come into play. Once one filament is sufficiently conductive, the desired current compliance is reached, lowering the voltage drop over the device and thus lowering the driving force, and the growth of the other filaments stops.42−44 Upon repeated cycling, all filamentsthe conducting filament and the prefilamentsare reoxidized during the gradual Reset operation as evidenced in Figure 6d. For the conductive filament, the temperature acceleration due to Joule heating27 will be most pronounced. Therefore, the previously most conductive filament may under some circumstances be oxidized more strongly than individual prefilaments. In the next cycle, the Set operation causes all prefilaments to grow anew in a self-accelerated manner, which results in an amplification of stochastic differences between the (partially) reoxidized filaments, such that the location of the conductive filament may randomly change to a neighboring filament (Figure 2 and Figure 7c) or a filament farther away (Figure 5 and Figure 7d), especially if the previous conductive filament was fully oxidized during Reset. Although we cannot exclude that the location of the filament changes because the quality of the graphene−SrTiO3 contact may suffer during switching, we believe that this is a realistic scenario for metal electrodes, as well, given that similar scenarios have been observed for ECM cells.21,22,40 In addition to the filament shape variations, the possibility of random changes of the active filament position demand for a careful filament control to reduce C2C variability in redox-based memristive devices such as demonstrated by Hayakawa et al.17 and Niu et al.45 As the shape and oxygen vacancy concentration of the filaments in the devices presented here are very different for each device, addressing these factors may also be necessary to reduce D2D variability. In addition, the occurrence of multiple filaments suggests that for aggressively scaled devices, variability caused by multifilamentary networks may be decreased as the number of possible filaments scales with the area.40 Even for small devices with only one main active filament, we expect that the shape and especially the oxygen vacancy concentration within the filament will change from cycle to cycle due to the subfilamentary structure. This indicates that the nature of variability in redox-based memristive devices is more complicated than realized in previous descriptions through fluctuations in the radius of a single filament.13 Further, current drift-diffusion device simulationswhile successfully describing important aspects like the gradual Resethave not yet considered such nonsymmetric and variable filament shapes for the sake of simplicity,46,47 suggesting that more-dimensional models may be necessary to accurately describe all nanoscale processes in memristive devices.48

It is very likely that boththe appearance of multiple filaments and their complex shapeare related to the presence of nanoscale subfilamentary structures,38,39 similar to the scenario recently described for ECM cells40 and as schematically depicted in Figure 7. For this drawing, the recently

Figure 7. Schematic representation of the filamentary changes causing the C2C variability. (a) Representative device in the HRS. Rather many prefilaments can be seen. (b) Representative device in the LRS. A rather large region with many prefilaments and one conductive filament connecting top and bottom electrode can be seen. In another location, single prefilaments may exist. (c) Situation I (“Changes in the shape of the filament”): after Reset and Set, the original conductive filament does not connect bottom and top electrode, but another conductive filament in the immediate surrounding was created. (d) Situation II (“Changes of the filament location”): alternatively, Reset and Set may create a conductive filament in a new location farther away from the original filament.

published atomically resolved images and spectroscopic information on subfilaments in SrTiO3 memristive devices were used as a guideline.41 The switching process in SrTiO3 is governed by the oxygen exchange between oxide layer and electrode,25 drift and diffusion of oxygen vacancies within the SrTiO3,9 and localized Joule heating27 and can therefore be described as a thermally assisted oxygen migration driven by the electric field. During the forming process, a multitude of prefilaments appears in regions where oxygen evolution reactions at the electrode/oxide interface are favorable, such as defects in the oxide or at the interface, as has been observed experimentally.41−43 These filaments grow and become more

CONCLUSIONS In conclusion, our direct observation of the conductive filament after repeated switching allowed us to confirm that the nonregular shape of the filament and the variable concentration of oxygen vacancies therein are the microscopic origin of the C2C variability in memristive devices. In addition, the location of the active filament may also vary during operation, contributing further to the variability. These processes can be explained through the competitive growth of multiple prefilaments during forming and repetitive switching, as each 6926

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano

differently. During the Reset sweeps, no current compliance was necessary. Spectromicroscopy. The threshold photoemission characterization was performed using a NanoESCA with illumination by a mercury lamp. The XPEEM experiments have been performed at the Nanospectroscopy beamline at Elettra Synchrotron Laboratory, Trieste, Italy, and the beamline UE56/1-SGM at BESSY II (Berlin, Germany) using secondary electrons as the detection method. Various series of images were taken at increasing photon energies with a step size of 0.1 eV for the Ti L-edge and 0.2 eV for the O K-edge. The image stacks were analyzed, and spectra were extracted using the IGOR Pro software. The spatial resolution as described in ref 50 (full width at half-maximum of a Gaussian profile convoluted with a step function, which was fitted to a sharp edge within the PEEM image) was determined to be 110 nm for the NanoESCA measurements and 30 nm for the measurements at the Nanospectroscopy beamline. All spectra in the main text and in the Supporting Information were background corrected by a linear fit of the pre-edge and subsequent normalization by fitting a third-order polynomial to the post-edge using the Athena software.36 To avoid artifacts from normalization, we always refer to the peak A intensity normalized to the intensity of peak B2 at 538 eV. The relative peak ratio trends described here are the same before and after normalization.

prefilament may act as the preferred site for the evolution of the new conductive filament in the next cycle. Our experimental findings provide the scientific basis for empirical pathways already pursued in the engineering of memristive devices and may therefore accelerate their educated design based on a physical understanding of the microscopic processes giving rise to variability.

METHODS Device Fabrication. Twenty nanometer single-crystalline undoped SrTiO3 thin films with 2−3% Sr excess and 2 nm singlecrystalline undoped, stoichiometric SrTiO3 thin films covered with 1 nm of Al2O3 were fabricated via pulsed laser deposition on 0.5 wt % Nb:SrTiO3 substrates (CrysTec GmbH, Germany). The singlecrystalline SrTiO3 target was ablated by a KrF excimer laser (λ = 248 nm) with a repetition rate of 5 Hz and a spot size of 2 mm2 at a target-to-substrate distance of 44 mm. The laser fluence was 0.67 or 1.05 J cm−2 for Sr-rich and stoichiometric films, respectively. SrTiO3 films were grown in an oxygen atmosphere of 0.1 mbar at a substrate temperature of 800 °C. The film growth was monitored using reflection high-energy electron diffraction. The Al2O3 was deposited ex situ by pulsed laser deposition with a laser fluence of 2.1 J cm−2, a repetition rate of 5 Hz, and a spot size of 1.5 mm2 at a target-tosubstrate distance of 60 mm in an oxygen atmosphere of 10−4 mbar. Stoichiometric SrTiO3/Nb:SrTiO3 devices typically exhibit extremely poor retention of the LRS,26,29 which makes time-consuming measurements like our PEEM investigation impossible. Therefore, we circumvented this limitation by using heterostructures of Al2O3/ SrTiO3/Nb:SrTiO3 or Sr-rich SrTiO3 layers, both of which we demonstrated to possess strongly improved retention.26,29 The switching mechanism, on the other hand, remained the same in all device types described above. The reason we chose a sample without Al2O3 for Figure 4 is that interpretation of the O K-edge is more direct without an additional oxide on top of the SrTiO3. We also found that the ionic nature and extremely thin layer thickness of the Al2O3 layer did not result in measurable changes between STO layers with and without Al2O3 capping, but we decided to ensure maximum clarity of the O K-edge interpretation. In a next step, graphene grown by chemical vapor deposition was deposited on the SrTiO3 surface as described elsewhere.49 For the structuring of top electrodes, the graphene layer was patterned through optical lithography and oxygen plasma etching. Before photoresist lift-off, a 30 nm Y:ZrO2 insulating layer was deposited via pulsed laser deposition at room temperature. The repetition rate was 5 Hz, and the spot size was 1.5 mm2 at a target-to-substrate distance of 60 mm in an oxygen atmosphere of 10−4 mbar. The laser fluence was 2.1 J cm−2. Afterward, the graphene electrode was partially covered by an additional 80 nm Y:ZrO2 insulating layer (optical lithography and pulsed laser deposition). This insulating layer allows for contacting the graphene with Pt/Au leads, which are separated from the continuous bottom electrode. The leads connect to the graphene in one specific position, leaving most of the graphene uncovered, allowing for spectromicroscopic investigation of the SrTiO3 layer. The leads are prepared via optical lithography and electron beam evaporation of 10 nm Pt followed by 130 nm Au. Electrical Characterization. For electrical characterization, the Pt/Au leads were contacted with W whisker probes or through Al wire bonding. The Nb:SrTiO3 substrate served as an electrically grounded bottom electrode and was contacted through silver paste. Both ex situ and operando I−V sweeps were performed with a Keithley 2611A sourcemeter. The different sweeps were performed using the following voltage cycles: 0 V to positive voltages (maximum +5 V) for forming and Set, 0 V to negative voltages (maximum −5 V) for Reset, and +0.2 V to −0.2 V for read-out. Unless stated otherwise, the device resistance was obtained from the slope of a linear fit of the read-out sweeps between −0.1 and +0.1 V. The step size was 20 mV, and the holding time before measurement was 5 ms; the current compliance for the forming step and the Set process was 35 mA unless indicated

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b02113. C2C variability measurements in pulsed operation; discussion of graphene contribution to the photoemission threshold; XPEEM analysis of the filament from Figure 2; and spatial distribution of the Ti3+ contribution (PDF)

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

Christoph Baeumer: 0000-0003-0008-514X Steven P. Rogers: 0000-0003-1554-4142 Moonsub Shim: 0000-0001-7781-1029 Author Contributions

C.B. and R.D. conceived and designed the experiments. C.B., R.V., S.P.R., and N.R. fabricated the samples. C.B. and R.V. performed the threshold photoelectron analysis. C.B., C.S., A.L., T.O.M., A.S., and N.R. performed the XPEEM experiments. C.B. and R.V. analyzed the experimental data. C.B. wrote the manuscript. S.N., M.S., C.M.S., S.M., R.W., and R.D. supervised the research. All authors discussed the results and commented on the manuscript. Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS Funding from the DFG (German Science Foundation) within the collaborative research center SFB 917 “Nanoswitches” is gratefully acknowledged. C.B. and R.D. also acknowledge funding from the W2/W3 program of the Helmholtz association. M.S. acknowledges support from the National Science Foundation under Grant No. DMR-1507170. We thank Dr. D. Wouters for critical reading of the manuscript, and J. Hackl and M.I. Khan for support during the synchrotron experiments at Bessy II. 6927

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano

Application. 2015 In 2015 Symposium on VLSI Technology (VLSI Technology); Proceedings of the Symposium on VLSI Technology, Kyoto, Japan, June 16−18, 2015; IEEE: Kyoto, Japan, 2015; 2-2, pp 12−13. (18) Cho, S.; Yun, C.; Tappertzhofen, S.; Kursumovic, A.; Lee, S.; Lu, P.; Jia, Q.; Fan, M.; Jian, J.; Wang, H.; Hofmann, S.; MacManusDriscoll, J. L. Self-assembled Oxide Films with Tailored Nanoscale Ionic and Electronic Channels for Controlled Resistive Switching. Nat. Commun. 2016, 7, 12373. (19) Degraeve, R.; Roussel, P.; Goux, L.; Wouters, D.; Kittl, J.; Altimime, L.; Jurczak, M.; Groeseneken, G. Generic Learning of TDDB Applied to RRAM for Improved Understanding of Conduction and Switching Mechanism through Multiple Filaments. In 2010 IEEE International Electron Devices Meeting (IEDM 2010); Proceedings of the IEEE International Electron Devices Meeting, San Francisco, CA, December 6−8, 2010; IEEE: San Francisco, CA, 2010; 28.4, pp 632− 635. (20) Park, G.-S.; Kim, Y.-B.; Park, S. Y.; Li, X. S.; Heo, S.; Lee, M. J.; Chang, M.; Kwon, J. H.; Kim, M.; U-In Chung; Dittmann, R.; Waser, R.; Kim, K. In Situ Observation of Filamentary Conducting Channels in an Asymmetric Ta2O5‑x/TaO2‑x Bilayer Structure. Nat. Commun. 2013, 4, 2382. (21) Arita, M.; Takahashi, A.; Ohno, Y.; Nakane, A.; TsurumakiFukuchi, A.; Takahashi, Y. Switching Operation and Degradation of Resistive Random Access Memory Composed of Tungsten Oxide and Copper Investigated Using In-Situ TEM. Sci. Rep. 2015, 5, 17103. (22) Kudo, M.; Arita, M.; Ohno, Y.; Takahashi, Y. Filament Formation and Erasure in Molybdenum Oxide During Resistive Switching Cycles. Appl. Phys. Lett. 2014, 105, 173504. (23) Celano, U.; Goux, L.; Belmonte, A.; Giammaria, G.; Opsomer, K.; Detavernier, C.; Richard, O.; Bender, H.; Irrera, F.; Jurczak, M.; Vandervorst, W. Progressive vs. Abrupt Reset Behavior in Conductive Bridging Devices: A C-AFM Tomography Study. In 2014 IEEE International Electron Devices Meeting (IEDM 2014); Proceedings of the IEEE International Electron Devices Meeting, San Francisco, CA, December 15−17, 2014; IEEE: San Francisco, CA, 2014; 14.1, pp 351−354. (24) Yang, Y.; Gao, P.; Li, L.; Pan, X.; Tappertzhofen, S.; Choi, S.; Waser, R.; Valov, I.; Lu, W. D. Electrochemical Dynamics of Nanoscale Metallic Inclusions in Dielectrics. Nat. Commun. 2014, 5, 4232. (25) Cooper, D.; Baeumer, C.; Bernier, N.; Marchewka, A.; La Torre, C.; Dunin-Borkowski, R. E.; Menzel, S.; Waser, R.; Dittmann, R. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by in situ TEM. Adv. Mater. 2017, 29, 1700212. (26) Raab, N.; Bäumer, C.; Dittmann, R. Impact of the CationStoichiometry on the Resistive Switching and Data Retention of SrTiO3 Thin Films. AIP Adv. 2015, 5, 047150. (27) Marchewka, A.; Waser, R.; Menzel, S. A 2D Axisymmetric Dynamic Drift-Diffusion Model for Numerical Simulation of Resistive Switching Phenomena in Metal Oxides. In 2016 International Conference On Simulation of Semiconductor Processes and Devices (SISPAD); Proceedings of the 2016 International Conference On Simulation of Semiconductor Processes and Devices (SISPAD), Nuremberg, Germany, September 6−8, 2016; IEEE: Nuremberg, Germany, 2016; O9.2, pp 145−148. (28) Mentes, T. O.; Zamborlini, G.; Sala, A.; Locatelli, A. Cathode Lens Spectromicroscopy: Methodology and Applications. Beilstein J. Nanotechnol. 2014, 5, 1873−1886. (29) Baeumer, C.; Schmitz, C.; Ramadan, A. H. H.; Du, H.; Skaja, K.; Feyer, V.; Muller, P.; Arndt, B.; Jia, C.; Mayer, J.; De Souza, R. A.; Michael Schneider, C.; Waser, R.; Dittmann, R. Spectromicroscopic Insights for Rational Design of Redox-Based Memristive Devices. Nat. Commun. 2015, 6, 8610. (30) Skaja, K.; Bäumer, C.; Peters, O.; Menzel, S.; Moors, M.; Du, H.; Bornhöfft, M.; Schmitz, C.; Jia, C.-L.; Schneider, C. M.; Mayer, J.; Waser, R.; Dittmann, R. Avalanche-Discharge-Induced Electrical Forming in Tantalum Oxide-Based Metal-Insulator-Metal Structures. Adv. Funct. Mater. 2015, 25, 7154−7162.

REFERENCES (1) Waser, R.; Aono, M. Nanoionics-Based Resistive Switching Memories. Nat. Mater. 2007, 6, 833−840. (2) Sawa, A. Resistive Switching in Transition Metal Oxides. Mater. Today 2008, 11, 28−36. (3) Waser, R.; Dittmann, R.; Staikov, G.; Szot, K. Redox-Based Resistive Switching Memories - Nanoionic Mechanisms, Prospects, and Challenges. Adv. Mater. 2009, 21, 2632−2663. (4) Yang, J. J.; Pickett, M. D.; Li, X.; Ohlberg, D. A. A.; Stewart, D. R.; Williams, R. S. Memristive Switching Mechanism for Metal/Oxide/ Metal Nanodevices. Nat. Nanotechnol. 2008, 3, 429−433. (5) Wong, H. S. P.; Lee, H.-Y.; Yu, S.; Chen, Y.-S.; Wu, Y.; Chen, P.S.; Lee, B.; Chen, F. T.; Tsai, M.-J. Metal-Oxide RRAM. Proc. IEEE 2012, 100, 1951−1970. (6) Linn, E.; Rosezin, R.; Kügeler, C.; Waser, R. Complementary Resistive Switches for Passive Nanocrossbar Memories. Nat. Mater. 2010, 9, 403−406. (7) Wouters, D. J.; Chen, Y.-Y.; Fantini, A.; Raghavan, N. Reliability Aspects. In Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications; Ielmini, D., Waser, R., Eds.; Wiley-VCH: Weinheim, Germany, 2016; pp 539−561. (8) Medeiros-Ribeiro, G.; Perner, F.; Carter, R.; Abdalla, H.; Pickett, M. D.; Williams, R. S. Lognormal Switching Times for Titanium Dioxide Bipolar Memristors: Origin and Resolution. Nanotechnology 2011, 22, 095702. (9) Marchewka, A.; Waser, R.; Menzel, S. Physical Simulation of Dynamic Resistive Switching in Metal Oxides Using a Schottky Contact Barrier Model. In 2015 International Conference On Simulation of Semiconductor Processes and Devices (SISPAD); Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), Washington DC, September 9−11, 2015; IEEE: Washington DC, 2015; 15-5, pp 297−300. (10) Baeumer, C.; Schmitz, C.; Marchewka, A.; Mueller, D. N.; Valenta, R.; Hackl, J.; Raab, N.; Rogers, S. P.; Khan, M. I.; Nemsak, S.; Shim, M.; Menzel, S.; Schneider, C. M.; Waser, R.; Dittmann, R. Quantifying Redox-Induced Schottky Barrier Variations in Memristive Devices via in Operando Spectromicroscopy with Graphene Electrodes. Nat. Commun. 2016, 7, 12398. (11) Kim, K. M.; Yang, J. J.; Strachan, J. P.; Grafals, E. M.; Ge, N.; Melendez, N. D.; Li, Z.; Williams, R. S. Voltage Divider Effect for the Improvement of Variability and Endurance of TaOx Memristor. Sci. Rep. 2016, 6, 20085. (12) Yu, S.; Guan, X.; Wong, H. On the Switching Parameter Variation of Metal Oxide RRAM-Part II: Model Corroboration and Device Design Strategy. IEEE Trans. Electron Devices 2012, 59, 1183− 1188. (13) Fantini, A.; Goux, L.; Degraeve, R.; Wouters, D. J.; Raghavan, N.; Kar, G.; Belmonte, A.; Chen, Y.; Govoreanu, B.; Jurczak, M. Intrinsic Switching Variability in HfO2 RRAM. In 2013 5th IEEE International Memory Workshop (IMW2013); Proceedings of the 5th IEEE International Memory Workshop, Monterey, CA, May 26−29, 2013; IEEE: Monterey, CA, 2013; pp 30−33. (14) Balatti, S.; Ambrogio, S.; Gilmer, D. C.; Ielmini, D. Set Variability and Failure Induced by Complementary Switching in Bipolar RRAM. IEEE Electron Device Lett. 2013, 34, 861−863. (15) La Torre, C.; Fleck, K.; Starschich, S.; Linn, E.; Waser, R.; Menzel, S. Dependence of the SET Switching Variability on the Initial State in HfOx-Based RERAM. Phys. Status Solidi A 2016, 213, 316− 319. (16) Choi, B. J.; Torrezan, A. C.; Norris, K. J.; Miao, F.; Strachan, J. P.; Zhang, M.-X.; Ohlberg, D. A. A.; Kobayashi, N. P.; Yang, J. J.; Williams, R. S. Electrical Performance and Scalability of Pt Dispersed SiO2 Nanometallic Resistance Switch. Nano Lett. 2013, 13, 3213− 3217. (17) Hayakawa, Y.; Himeno, A.; Yasuhara, R.; Boullart, W.; Vecchio, E.; Vandeweyer, T.; Witters, T.; Crotti, D.; Jurczak, M.; Fujii, S.; Ito, S.; Kawashima, Y.; Ikeda, Y.; Kawahara, A.; Kawai, K.; Wei, Z.; Muraoka, S.; Shimakawa, K.; Mikawa, T.; Yoneda, S. Highly Reliable TaOx ReRAM with Centralized Filament for 28-nm Embedded 6928

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929

Article

ACS Nano (31) Mathieu, C.; Barrett, N.; Rault, J.; Mi, Y. Y.; Zhang, B.; De Heer, W. A.; Berger, C.; Conrad, H. E.; Renault, O. Microscopic Correlation between Chemical and Electronic States in Epitaxial Graphene on SiC(000i). Phys. Rev. B: Condens. Matter Mater. Phys. 2011, 83, 235436. (32) Renault, O.; Brochier, R.; Roule, A.; Haumesser, P.-H.; Kromker, B.; Funnemann, D. Work-Function Imaging of Oriented Copper Grains by Photoemission. Surf. Interface Anal. 2006, 38, 375− 377. (33) Tsukada, K.; Inoue, H.; Katayama, F.; Sakai, K.; Kiwa, T. Changes in Work Function and Electrical Resistance of Pt Thin Films in the Presence of Hydrogen Gas. Jpn. J. Appl. Phys. 2012, 51, 015701. (34) Tian, H.; Chen, H.-Y.; Gao, G.; Yu, S.; Liang, J.; Yang, Y.; Xie, D.; Kang, J. F.; Ren, T.-L.; Zhang, Y.; Wong, H.-S. P. Monitoring Oxygen Movement by Raman Spectroscopy of Resistive Random Access Memory with a Graphene-Inserted Electrode. Nano Lett. 2013, 13, 651−657. (35) Celano, U.; Op de Beeck, J.; Clima, S.; Luebben, M.; Koenraad, P. M.; Goux, L.; Valov, I.; Vandervorst, W. Direct Probing of the Dielectric Scavenging-Layer Interface in Oxide Filamentary-Based Valence Change Memory. ACS Appl. Mater. Interfaces 2017, 9, 10820− 10824. (36) Ravel, B.; Newville, M. ATHENA, ARTEMIS, HEPHAESTUS: Data Analysis for X-ray Absorption Spectroscopy using IFEFFIT. J. Synchrotron Radiat. 2005, 12, 537−541. (37) Gonzalez, M. B.; Acero, M. C.; Beldarrain, O.; Zabala, M.; Campabadal, F. Analysis of the Switching Variability in Ni/HfO2Based RRAM Devices. IEEE Trans. Device Mater. Reliab. 2014, 14, 769−771. (38) Buckwell, M.; Montesi, L.; Hudziak, S.; Mehonic, A.; Kenyon, A. J. Conductance Tomography of Conductive Filaments in Intrinsic Silicon-Rich Silica RRAM. Nanoscale 2015, 7, 18030−18035. (39) Celano, U.; Goux, L.; Degraeve, R.; Fantini, A.; Richard, O.; Bender, H.; Jurczak, M.; Vandervorst, W. Imaging the ThreeDimensional Conductive Channel in Filamentary-Based Oxide Resistive Switching Memory. Nano Lett. 2015, 15, 7970−7975. (40) Celano, U.; Goux, L.; Belmonte, A.; Opsomer, K.; Detavernier, C.; Jurczak, M.; Vandervorst, W. Conductive Filaments Multiplicity as a Variability Factor in CBRAM. In 2015 IEEE International Reliability Physics Symposium (IRPS 2015); Proceedings of the IEEE International Reliability Physics Symposium, Monterey, CA, April 19−23, 2015; IEEE: Monterey, CA, 2015; MY-7, pp 835−837. (41) Du, H.; Jia, C.; Koehl, A.; Barthel, J.; Dittmann, R.; Waser, R.; Mayer, J. Nanosized Conducting Filaments Formed by atomic-scale defects in Redox-Based Resistive Switching Memories. Chem. Mater. 2017, 29, 3164−3173. (42) Lenser, Ch.; Kuzmin, A.; Purans, J.; Kalinko, A.; Waser, R.; Dittmann, R. Probing the Oxygen Vacancy Distribution in Resistive Switching Fe-SrTiO3 Metal-Insulator-Metal-Structures by Micro-XRay Absorption Near-Edge Structure. J. Appl. Phys. 2012, 111, 076101. (43) Koehl, A.; Wasmund, H.; Herpers, A.; Guttmann, P.; Werner, S.; Henzler, K.; Du, H.; Mayer, J.; Waser, R.; Dittmann, R. Evidence for Multifilamentary Valence Changes in Resistive Switching SrTiO3 Devices Detected by Transmission X-Ray Microscopy. APL Mater. 2013, 1, 042102. (44) Menzel, S.; Klopstra, B.; Kügeler, C.; Böttger, U.; Staikov, G.; Waser, R. A Simulation Model of Resistive Switching in Electrochemical Metallization Memory Cells. MRS Online Proc. Libr. 2009, 1160, 101−106. (45) Niu, G.; Calka, P.; Auf der Maur, M.; Santoni, F.; Guha, S.; Fraschke, M.; Hamoumou, P.; Gautier, B.; Perez, E.; Walczyk, C.; Wenger, C.; Di Carlo, A.; Alff, L.; Schroeder, T. Geometric Conductive Filament Confinement by Nanotips for Resistive Switching of HfO2-RRAM Devices with High Performance. Sci. Rep. 2016, 6, 25757. (46) Larentis, S.; Nardi, F.; Balatti, S.; Gilmer, D. C.; Ielmini, D. Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM-Part II: Modeling. IEEE Trans. Electron Devices 2012, 59, 2468−2475.

(47) Nardi, F.; Balatti, S.; Larentis, S.; Gilmer, D. C.; Ielmini, D. Complementary Switching in Oxide-Based Bipolar Resistive-Switching Random Memory. IEEE Trans. Electron Devices 2013, 60, 70−77. (48) Marchewka, A.; Roesgen, B.; Skaja, K.; Du, H.; Jia, C. L.; Mayer, J.; Rana, V.; Waser, R.; Menzel, S. Nanoionic Resistive Switching Memories: On the Physical Nature of the Dynamic Reset Process. Adv. Electron. Mater. 2016, 2, 1500233. (49) Baeumer, C.; Rogers, S. P.; Xu, R.; Martin, L. W.; Shim, M. Tunable Carrier Type and Density in Graphene/PbZr0.2Ti0.8O3 Hybrid Structures through Ferroelectric Switching. Nano Lett. 2013, 13, 1693−1698. (50) Patt, M.; Wiemann, C.; Weber, N.; Escher, M.; Gloskovskii, A.; Drube, W.; Merkel, M.; Schneider, C. M. Bulk Sensitive Hard X-Ray Photoemission Electron Microscopy. Rev. Sci. Instrum. 2014, 85, 113704.

6929

DOI: 10.1021/acsnano.7b02113 ACS Nano 2017, 11, 6921−6929