Tunable Resistive Switching Enabled by Malleable Redox Reaction in

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Functional Inorganic Materials and Devices

Tunable Resistive Switching Enabled by Malleable Redox Reaction in Nano-Vacuum Gap Xinglong Ji, Chao Wang, Kian Guan Lim, Chun Chia Tan, Tow Chong Chong, and Rong Zhao ACS Appl. Mater. Interfaces, Just Accepted Manuscript • Publication Date (Web): 22 May 2019 Downloaded from http://pubs.acs.org on May 27, 2019

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ACS Applied Materials & Interfaces

Tunable Resistive Switching Enabled by Malleable Redox Reaction in Nano-Vacuum Gap Xinglong Ji, Chao Wang, Kian Guan Lim, Chun Chia Tan, Tow Chong Chong, and Rong Zhao* a Department

of Engineering Product Design, Singapore University of Technology and Design,

8 Somapah Road, Singapore, 487372, Singapore * Corresponding author: [email protected] Abstract Neuromorphic computing has emerged as a highly promising alternative to the conventional computing. The key to constructing a large-scale neural network in hardware for neuromorphic computing is to develop artificial neuron with leaky integrate-and-fire behavior and artificial synapse with synaptic plasticity using nanodevices. So far, these two basic computing elements have been built in separate devices using different materials and technologies, which poses a significant challenge to system design and manufacturing. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap between bottom electrode and a mixedionic-electronic-conductor (MIEC) layer. Through redox reaction on the MIEC surface, metallic filament dynamically grew within the nano-vacuum gap. The nano-vacuum gap provided an additional control factor for controlling the evolution dynamics of metallic filament by tuning electron tunneling efficiency, analogy to a pseudo-three terminal device, resulting in tunable switching behavior in various forms from volatile to non-volatile switching in a single device. Our device demonstrated cross-functions, in particularly, tunable neuronal firing and synaptic plasticity on demand, providing seamless integration for building largescale artificial neural network for neuromorphic computing. Keywords: Nano-vacuum gap device, Tunable resistive switching, Malleable redox reaction, Neuromorphic network, Artificial synapse, Artificial neuron

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Introduction Neuromorphic computing has been widely regarded as a promising alternative to the conventional computing

1–3.

With a highly parallel neural network structure, neuromorphic

machines collocate memory cells and processing units, thus promising to have a higher computing efficiency and consume less power compared to Von Neumann machines

4–6.

To

construct a neuromorphic system, it is critical to build the basic functional elements: artificial synapse with synaptic plasticity behavior and artificial neuron with firing activity

7–9.

Increasing efforts have been devoted in developing the two elements using nanodevices in order to build large scale neuromorphic systems. Till now, artificial neuron and artificial synapse were developed separately by using different materials and mechanisms, including phase change insulator

13,14,

10,11,

resistive switching, atomic switch 12, insulator-to-metal transition of Mott

spin-transfer torque magnetic memory

15–17.

Among these approaches, volatile

resistive switching has been actively studied to emulate synaptic plasticity or neuronal firing activity by utilizing the formation and spontaneous rapture of metallic filament (MF), providing a new approach to mimic the transient process in biological nervous system, such as short-term plasticity and leaky integrate-and-fire

18–20.

Despite significant advances, the use of diverse

material systems and mechanisms has posed a huge challenge to the construction of large scale artificial neural network due to the high complexity of system design and manufacturing. For neuromorphic computing, it is most desirable for a single device exhibiting both volatile and nonvolatile behavior, which would potentially have the ability to mimic the basic processing and learning operations of mammalian brain with unified material system and device structure. In this work, we designed a resistive device embedded with an innovative nano-vacuum gap structure that achieved both non-volatile and volatile switching behaviors in a single device. The nano-vacuum gap was created by electrically driving highly mobile metal layer into a solid electrolyte layer with high ion mobility. MF dynamically grew within the vacuum gap through


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redox reaction, different from the standard resistive devices. The nano-vacuum gap provides an additional factor for controlling the redox reaction rate by tuning the strength of electron tunneling efficiency to influence the continuous growth or degeneration of MF. By varying the distance of nano-vacuum gap, the evolving dynamics of MF were fine controlled, resulting in the co-existence of multiple conductive states and complex switching behavior in various forms in a single device. In addition, the nano-vacuum gap device exhibited varied retention time from several micro seconds to tens of hours by applying different operation scheme. The abundant switching behaviors from volatile to non-volatile enabled our nano-vacuum gap device to operate cross-functionally from neuromorphic to memory applications. In particularly, we successfully demonstrated synaptic plasticity and neuronal firing activity in a single device, paving the way towards the seamless integration of large-scale artificial neural network for neuromorphic computing.

Experimental details Device fabrication. The proposed highly tunable cross-functional device has an initial structure of TiW/Ag/Ge2Sb2Te5(GST)/Pt. Silicon wafer with an ~ 1 μm thermal oxidized layer was used as the substrate. 35 nm-thick TiW and Ag with different thicknesses (10 nm, 30 nm and 50 nm) were deposited on the Si/SiO2 substrate using a magnetron sputtering system, serving as bottom electrode and active metal layer, respectively. Subsequently, 60 nm-thick SiO2 was deposited on the bottom electrode, followed by photo-lithography and liftoff to form the insulation layer and contacting vias (2 × 2 µm2). Then glassy GST and Pt were deposited and patterned together to form the functional layer and top electrode, respectively. After the fabrication, an electrical initialization process was performed to form the Ag-Ge2Sb2Te5 (Ag-GST) MIEC layer and the nano-vacuum gap. During the electrical formation of vacuum gap, a positive voltage bias was applied from the bottom electrode to the top electrode. The output current was monitored in



real-time as shown in Figure S1a and b. Initially, the current was in microampere level and increased nonlinearly. At a certain voltage, the current dropped by more than 8 orders of magnitude to the picoampere level, indicating the formation of a nano-vacuum gap. The final nano-vacuum gap device has a structure of TiW/ nano-vacuum gap/MIEC/Pt. Electrical performance characterization. The I-V characteristic of the nano-vacuum gap devices was measured using a Keithley 4200 semiconductor parameter analyzer (4200-SCS) with a Cascade Microtech Summit 11000 probe station. Considering that the area of the hysteresis loops is frequency dependent 21, we performed all the direct current (DC) sweeping at a same sweeping rate. The pulse mode measurement of the nano-vacuum gap devices was carried out by using a Keithley 4225-PUM (Pulse Measurement Unit). Microstructure characterization. The microstructure of the nano-vacuum gap devices with different final states were characterized. We used Dual-beam Focused Ion Beam system to prepare cross sectional samples of the nano-vacuum gap devices and conducted a progressive milling with small ion current to identify the interested areas. The thickness of the crosssectional samples was finally milled to ~60 nm for high-resolution analyses purpose. Low beam current was applied during sample preparation to prevent the influence on the physical situation of Ag. A Tecnai F20 Transmission Electron Microscope (TEM) system was used for the microstructure characterization at an accelerating voltage of 200 kV. The bright field images were collected for the nano-vacuum gap region to identify the conductive filament. Electron dispersive X-ray Spectroscopy (EDX) line scan was performed for material component and element distribution characterizations.

Results and discussion


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Figure 1. Highly tunable switching behaviors with different gap distance. (a) Cross-point structure for nano-vacuum gap device demonstration. Inset: Optical micrograph of the 6 × 6 cross-point array, the effective switching area is 2 × 2 µm2. (b) Scanning electron micrograph of a nano-vacuum gap device. Top image shows the top view; bottom image shows the enlarged cross-sectional view. (c) Non-volatile resistive switching I-V characteristic of Gap-10 device. With different compliance current, different resistance states were achieved. Inset: Logarithmic plotting. (d) Volatile-to-nonvolatile switching I-V characteristic of Gap-30 device. For volatile switching with low compliance current, the high resistance state was restored automatically. For non-volatile switching triggered by larger compliance current, a reset operation is essential to restore the high resistance state. (e) Volatile switching I-V characteristic of Gap-50 device, showing typical threshold switching for the positive polarity sweep, and cut-off characteristic for the negative polarity sweep.



A cross-point array architecture was adopted for the demonstration of the cross-functional device, as shown in Figure 1a. In an as-fabricated device, a silver (Ag) layer was sandwiched between the GeSbTe (GST) layer and bottom electrode (TiW). To form the nano-vacuum gap, an electrical initialization operation was performed by applying a voltage bias to the bottom electrode and grounding the top electrode, which is schematically described in Figure S1a (supplementary information). It should be noted that the electrical initialization in this work is to drive the active Ag layer into GST layer to form the vacuum gap, which is different from the electroforming of conductive path in conventional resistive memories or memristors. The glassy GST with over stoichiometry of chalcogen has been demonstrated to be very beneficial for Ag ionization and migration 22. Therefore, under the driving force of external electric field, the Ag layer was fully dissolved into the glassy GST, forming the nano-vacuum gap in our device (Figure 1b). The I-V curve during electrical initialization is shown in Figure S1b (supplementary information). By designing device with different thickness of Ag layer, the nano-vacuum gap distance and Ag concentration can be precisely controlled. The chemical dissolution of Ag significantly changed the electrical property of GST layer. Pure amorphous GST material presents a very low conductivity due to the disorder induced localized electronic state 23. However, with the doping of Ag, the GST in the effective area with local enrichment of Ag+ ions became mixed-ionic-electronic-conductor (MIEC) with much higher conductivity. The resistances of Ag-GST MIEC with different Ag concentrations are shown in Figure S1c (supplementary information). In this paper, the Ag concentrations of all devices were fixed at 50 at.% to ensure high conductivity of MIEC. Devices with different nano-vacuum gap distances were fabricated. We specially chose the three groups of devices with gap distance of 10 nm, 30 nm and 50 nm (referred as Gap-10, Gap-30, and Gap-50) to present as they showed distinct switching behaviors. After electrical initialization of the vacuum gap, the devices were switched repeatedly by applying voltage bias


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to the top electrode (opposite to electrical initialization polarity). In Figure 1c, the Gap-10 device showed a non-volatile resistive switching and switched to different resistance states by adjusting compliance current. For device with a 30 nm nano-vacuum gap, a two-step switching behavior was observed (Figure 1d). When a low compliance current (10 µA) was applied, the device only displayed the first step volatile switching with resistance automatically returned to the high resistance state. And the device maintained in OFF state with high resistance during negative sweeping. When a high compliance current (100 µA) was applied, the second step non-volatile switching was triggered, which need a reset operation to switch back to high resistance state. This indicates that Gap-30 device exhibited a volatile-to-nonvolatile transition within 2 V. By further increasing the nano-vacuum gap distance to 50 nm, a pure volatile switching was observed under 2 V with a typical hysteretic I-V during positive sweeping; while the current maintained constant low during negative sweeping, as shown in Figure 1e. The endurance of Gap-50 device is up to 106 cycles, while still maintaining stable volatile switching and large ON/OFF ratio (Figure S2, supporting information). The above experimental observation revealed that the resistive switching behavior was highly associated with the nanovacuum gap distance. Under low voltage operation (< 2 V), the resistive switching transited from pure non-volatile for small nano-vacuum gap (Gap-10) to volatile-to-nonvolatile for medium nano-vacuum gap (Gap-30) and pure volatile for large nano-vacuum gap distance (Gap-50). It is worth noting that if we increased the voltage to high values (~ 4 V), Gap-50 device also presented a non-volatile switching (Figure S3, supporting information). Considering the low power requirement of many electronic applications, our study was limited to a reasonable operation voltage range (i.e, within 2 V).



Figure 2. Tunability of volatile switching under single pulse. (a) Switch-on voltages (threshold voltage) depends on different pulse widths (from 1 μs to 900 μs). (b) Time-solved current response under a single pulse (500 μs) operation with Sub-Vth, Near-Vth, and Super-Vth amplitudes, respectively. (c) Tine-resolved dynamic current change in Near-Vth region with different pulse widths.

The above study showed that the nano-vacuum gap provides an additional design flexibility for tuning the switching behaviors comparing with the standard memristive devices. Besides the non-volatile resistive switching that has been actively studied for memristor or memory applications, the incorporation of volatile switching into the same device has provided abilities for wider applications. In this part, we investigated the tunability of the nano-vacuum gap device on the volatile switching. Here, Gap-50 device was chosen as a demonstrator due to a larger tuning window of the volatile switching. Firstly, pulse I-V measurement was performed to determine the threshold voltages under different pulse widths (Figure S4). The pulse width dependent threshold voltages in Figure 2a showed that switch-on time exponentially decreased


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from 900 µs to 1 µs with a slight increase in threshold voltage, indicating a highly nonlinear switching kinetics. At a fixed pulse width, there were three tuning regions with reference to the threshold voltage, namely sub-threshold (Sub-Vth), near-threshold (Near-Vth), and superthreshold (Super-Vth). As an example, Figure 2b plots the time-resolved current response to a 500 µs pulse. No obvious current response was observed in the Sub-Vth region. Increasing the pulse amplitude to the Near-Vth region, the output current gradually increased, showing a dynamic switch-on process. When a Super-Vth voltage was applied, the device was immediately switched on with current increased to 10 µA sharply. It should be noted that the current spike during pulse rising and the current recoil during pulse falling are resulted from the charging and discharging of parasitic capacitor in our device

24,25.

The dynamic change of current in

Near-Vth region was further explored by varying the pulse width (Figure 2c). With the increase of pulse width, the ON current continuously increased from 0.1 µA until that a saturation current (~1 µA) was reached. The different time-resolved switching behaviors of the three regions provided a high tunability to implement different functions in a single device. For instance, the dynamic changing behavior of Near-Vth operation is highly desired for memristor application

20;

with the fast speed to reach the saturation current, the device can be used as

selector in Super-Vth operation region 26,27. As for the Sub-Vth operation, the unobvious response to the applied pulse would facilitate the emulation of synapse and neuron for neuromorphic applications, which will be the focus of the following discussion.



Figure 3. Tunability of switching behavior under pulse train. (a) Time-resolved output current responded to a Sub-Vth pulse train (pulse amplitude 0.8 V, pulse width 1 ms, pulse interval 500 μs, pulse number 100). Five regions with different switching behaviors were identified, including 1 No response, 2 No-to-volatile transition (inset), 3 Volatile switching, 4 Volatile-to-nonvolatile transition (inset), and 5 Nonvolatile switching. (b) Application map for the resistive device with different retention times. The numbers correspond to the switching regions in (a). With abundant switching dynamics, our device can fulfill requirements of different applications. (c) Schematic of full memristive neuromorphic network consisting of four key components: artificial synapse, artificial neuron, selector, and memory.

The weak excitation of Sub-Vth operation provided a good means to study the tunability of


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switching behaviors caused by accumulation effect under pulse train of the nano-vacuum gap device. Here, we chose Gap-30 device as a demonstrator because it showed much faster transition from volatile to non-volatile switching; while Gap-50 device exhibited very stable volatile switching at low voltage operation, which required more than 106 Sub-Vth pulses to trigger the non-volatile switching. Figure 3a shows the time-resolved output current responded to a train of 100 Sub-Vth pulses. Five distinct segments with different switching behaviors were identified as the number of pulses increased, which were 1. no response, 2. no-to-volatile transition, 3. volatile switching, 4. volatile-to-nonvolatile transition, and 5. nonvolatile switching, respectively. At the initial state (segment 1), because the excitation of Sub-Vth pulses was weak, no current response was detected, and the current output was limited by the floor level of measurement range. As the number of pulses increased, a nonlinear volatile resistive switching was triggered due to the accumulation effect (segment 2), showing no-to-volatile transition. After that, the device was continuously switched ON by the following pulses and presented a stable volatile switching (segment 3). With the increase of applied pulses, the on current in the segment 3 gradually increased until a sharp jump occurred. The final resistance of this segment reached 1.3 MΩ, around two orders of magnitude higher than the quantum contact resistance (12.9 kΩ), suggesting that the electron transfer was predominated by tunneling effect

28.

After the sharp nonlinear resistive switching (segment 4), the resistance

dropped from ~1.3 MΩ to 5 - 12 kΩ, triggering a volatile-to-non-volatile transition. The much lower resistance state indicates that an ohmic contact was formed. Finally, in segment 5, the low resistance was maintained and could not be restored to high resistance in a short time unless a reset operation was conducted. The retention time of the volatile and non-volatile switching was further measured. It was ~ milliseconds in average for the volatile switching in segment 3 and about tens of hours for the non-volatile switching in segment 5 (Figure S5, supporting information). According to



retention time, we mapped the five distinct segments with different switching behaviors in Figure 3b. In addition, we also extracted the retention times of various reported resistive switching devices and summarized in Figure 3b21,29–41. Apparently, the retention time determines the application scenarios of devices. Owning to the unique high tunability, our vacuum gap devices with tunable retention time can be manipulated to operate in different switching region and adapted to different applications (Figure S6, supporting information). In principle, a functional neuromorphic network can be constructed by four basic elements, namely selector, artificial synapse, artificial neuron, and memory (Figure 3c). The tunable abundant switching behaviors of our device enable it to function as all the four elements, making it possible to build artificial neural network based on the unified material system and device structure.

Figure 4. Transmission electron microscope (TEM) observation and mechanism of the tunable switching dynamics. (a) TEM image cross-sectional view of a Gap-50 device. (b) Cross-sectional TEM image of the device at non-volatile region and EDX line scan profile. A


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Ag metallic filament (MF) was found in the nano-vacuum gap, bridging the MIEC layer and bottom electrode. (c) Cross-sectional TEM image of the device at volatile region and EDX line scan profile. A raptured MF residue was found in the nano-vacuum gap. (d) Schematics to show the state of MF into four stages, including initial, nucleation, growth & tunneling, and contact, and the equivalent circuit of the nano-vacuum gap device. Inset shows the competition between oxidation and reduction processes at MF/MIEC interface.

The non-volatile switching with narrow nano-vacuum gap has been previously observed in superionic solid electrolyte atomic switch for memory application and its switching mechanism was studied by means of scanning tunneling microscope 42,43. However, the co-exist of volatile and non-volatile switching in the wide nano-vacuum gap devices and the transition between them were discovered in this work. Next, we attempt to unveil the microcosmic mechanism underneath the diverse resistive switching behaviors. Gap-50 devices were switched to different states (non-volatile and volatile) and analyzed by transmission electron microscopy (TEM). Figure 4a is a cross-sectional view of the device showing that the nano-vacuum gap was only formed in the contact area between Ag and GST; while the diffusion of Ag was well segregated by the SiO2 layer in the surrounding area. It worth noting that with large amount of Ag driven into the GST layer, the thickness of GST did not show obvious change, which may because glassy GST with a large number of intrinsic nonbonded anionic defects has a high solid solution of Ag.22 In the device at the non-volatile region (Figure 4b), a filament was found to form inside the nano-vacuum gap, bridging the MIEC layer and bottom electrode. The line scan profile analysis revealed that the filament was composed of metallic Ag (no obvious Ge, Sb or Te signal was found). An enlarged image of the filament captured by high resolution TEM (HRTEM) is shown in Figure S7, in which Ag nanocrystals were clearly observed in the filament. In the device switched to the volatile region (Figure 4c), a fully ruptured filament was



observed. From the TEM image and the EDX line scan profile, Ag residues could still be seen on the bottom electrode, but almost vanish on the interface of filament/MIEC due to faster diffusion rate on the MIEC surface

27.

According to the line scan profile, Ag was almost

uniformly distributed inside the MIEC except for a slightly higher Ag concentration at the MIEC/nano-vacuum gap interface. The TEM results provided us clues to the different switching behaviors, suggesting that they were highly related to the formation and annihilation of Ag MF in the nano-vacuum gap. At the initial state under small voltage bias, the wide nano-vacuum gap provides an excellent insulation, making the device behave like a nano-capacitor. As illustrated in an equivalent circuit in Figure 4d, a majority of the applied voltage is dropped across the nano-vacuum gap. Under the action of electric field, electrical charges are built in the nano-capacitor, where electrons accumulate on the bottom electrode (anode). Driven by the e-field, the ionized Ag+ drift to the interface between MIEC and nano-vacuum gap, resulting in the accumulation of positive charges

27.

With gradual increase voltage bias, electron tunneling will eventually

happen in the nano-vacuum gap, which is promoted by the localized e-field enhancement caused by Ag residues on the bottom electrode. The tunneling electrons with sufficient energy can overcome the reduction barrier and reduce the Ag+ ions in MIEC layer 44. The reduced Ag nucleus are unstable until a critical size is reached. During this nucleation stage, only a small number of electrons can tunnel through the nano-vacuum gap, showing weak tunneling current. Once a thermodynamically stable nucleus is formed, MF begins to grow and is affected by two processes: the reduction of Ag+ (Ag+ + e- → Ag) and the oxidation of Ag (Ag → Ag+ + e-). The redox reactions all occur on the filament/MIEC interface but are resulted by different mechanisms. The reduction of Ag is influenced by tunneling electrons, while the Ag oxidation is driven by thermal diffusion and interfacial potential between Ag and MIEC layer 45. Electron tunneling as a limiting factor can control the redox reaction, resulting in dynamic growth of


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MF. Smaller nano-vacuum gap distance provides a higher electric field, which improves tunneling efficiency and enhances the reduction process; while larger vacuum gap with lower tunneling efficiency limits the reduction of Ag+ ions. Under DC bias, the competition between the reduction and oxidation processes of Ag leads to various switching behaviors, as shown in Figure 1. At high voltage bias, due to high electron tunneling efficiency, the reduction reaction races ahead of the oxidation process, so that MF grows, and the device exhibits a high conductive state; while at low voltage bias, oxidation dominates due to low electron tunneling efficiency, resulting in the annihilation of MF and the device returning to a low conductive state. In the pulse train test, the strong reduction action is beneficial to the MF growth during the pulse duration, and the oxidation leads to the annihilation of MF during the pulse interval. The tunneling current and electron charge transfer in MIEC collectively determine the device current 46. When the electric field is strong enough for the MF to reach the bottom electrode, the thermodynamically stabilized MF will form an ohmic contact with the bottom electrode and shorten the nano-vacuum gap, resulting in non-volatile switching behavior. Based on the above discussion, we can summarize the state of MF into four stages: initial state, nucleation, growth & tunneling, and contact, as illustrated in Figure 4d. A phenomenal model based on the filament formation and annihilation dynamics was developed to verify the proposed mechanism. The simulation details are described in the supplementary part (Supplementary Note S1). Our simulation emphasized the competition between oxidation and reduction processes under electric field. According to Butler-Volmer equation, the redox activation energy ΔG and the charge transfer coefficient α were utilized to simplify the details of the redox reaction 29,47. Based on our phenomenal model, different switching behaviors from volatile to non-volatile were obtained, which is in good agreement with our test data, as shown in Figure S8 (supporting information). The successful simulation of the resistive switching behaviors shows that the proposed mechanism is a good explanation of our observations.



Figure 5. Cross-functional device emulating synaptic plasticity and neuronal firing. (a) Schematic of the biology neuron cell and functional artificial synapse and artificial neuron. For our cross-functional device, Sub-Vth pulse train (pulse amplitude 1.2 V, pulse duration 1 ms, pulse interval 0.5 ms) operation to realize the firing activity of artificial neuron (bottom); NearVth pulse train (pulse amplitude 1.5 V, pulse duration 0.2 ms and pulse interval 0.1 ms) operation to realize the synaptic plasticity of artificial synapse (top). (b) Leaky behavior after threshold switching depending on switch ON current. Inset: time-resolved relaxation time measurement. (c) Statistics of fire pulse number depending on pulse amplitude.


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The highly tunable switching behaviors of the nano-vacuum gap device opens the possibility of achieving different functions on demand. Here, we focus on addressing the challenge of implementing artificial synapse and artificial neuron in a single device, as described at the beginning of this article. Neuroscience studies have demonstrated that synapse has the ability to strengthen or weaken over time according to Hebb’s rule, and neuron can generate firing activity after accumulating enough membrane potential. Gap-50 device with stable volatile switching behavior was employed to emulate both plasticity of synapse and firing activity of neuron, by operating at Near-Vth region and Sub-Vth region, respectively (Figure 5a). When a positive Near-Vth pulse train was applied, the conductivity of our device increased continuously, which mimicked the potentiation behavior of synapse. By inverting the polarity of the input pulse train, the depression behavior was triggered. It is worth noting that the depression rate is higher than that of the potentiation process due to the spontaneous diffusion behavior of Ag MF. To realize artificial neuron, we utilized the no-to-volatile switching behavior of the nanovacuum gap device in Sub-Vth operation region, which simulated the firing activity of neuron after accumulating enough membrane potential. Multiple firing events were triggered continuously by applying a pulse sequence with multiple segments (Figure S9, supporting information). After each switching, the device was able to spontaneously return to resting state during the interval of segments, showing a typical leaky behavior (see Figure 5a). The relaxation time was measured by using a short stimulus pulse with the amplitude varying from 1.5 V to 2.1 V followed by a long reading pulse with an amplitude of 0.2 V, which will not change conduction state of Gap-50 device (Figure 5b). It can be seen that after the switch ON process, the low resistance state remained for a few milliseconds and then relaxed to a high resistance state. Through data fitting, the relaxation time was found to be exponentially dependent on the ON current, which might be related to the diffusion dynamic of MF. The



higher ON current caused the tunneling distance in the nano-vacuum gap to become smaller, so that it took a longer time for MF to spontaneously diffuse. Figure 5c provides statistical results of the number of fire pulses according to the pulse amplitude. Based on this phenomenon, the firing frequency can be easily modulated by changing the pulse amplitude. This demonstrates that the tunable nano-vacuum gap device with rich switching dynamics is a cross-functional device, which can realize the two key neuromorphic elements’ functions in a single device, enabling the construction of large-scale neuromorphic computing systems.

Conclusion In summary, by introducing a nano-vacuum gap structure, we have realized a cross-functional device with tunable switching behaviors. Via adjusting the nano-vacuum gap, the switching behavior can be manipulated from non-volatile to volatile. For volatile switching, three tuning regions were distinguished according to the threshold voltage, namely Sub-Vth, Near-Vth, and Super-Vth. The tunability of switching behaviors caused by accumulation effect under pulse train was further studied by using weak excitation of Sub-Vth. The device exhibited abundant switching dynamics under pulse train, and its relaxation time varied from several micro seconds to tens of hours. The broad range of retention time enabled our device to adapt to many applications on demand. The mechanism of tunable resistive switching was revealed by the observed TEM evidence, which was verified by a phenomenal simulation. It is the different states of the metallic filament in the nano-vacuum gap under electric field that resulted in the different electron transfer dynamics, leading to various switching behaviors. By operating the cross-functional device at Near-Vth and Sub-Vth ranges, we have successfully demonstrated synaptic plasticity of artificial synapse and firing activity of artificial neuron, providing a simple way for large scale neuromorphic network construction.


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Associated content Supporting Information Supporting Information is available on the ACS Publications website. Electroformation of the Nano-vacuum gap device; Pulse I-V to decide the threshold voltage of the Nano-vacuum gap device; By adjusting the vacuum gap distance and operation condition, different switching behaviors were realized; High resolution transmission electron microscope images of the metallic filament; Simulated current response under the action of pulse train; The phenomenal model based on the electrochemical formation and annihilation of a metallic filament in the Nano-vacuum gap.

Acknowledgement This work is supported by Singapore Ministry of Education Academic Research Fund Tier 2 (Grant number: MOE2016-T2-2-141) and A*STAR, Science and Engineering Research Council Public Sector Research Funding (Grant number: 1521200085). X. Ji and C. Wang contribute equally to this work.

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Highly tunable switching behaviors with different gap distance. (a) Cross-point structure for nano-vacuum gap device demonstration. Inset: Optical micrograph of the 6 × 6 cross-point array, the effective switching area is 2 × 2 µm2. (b) Scanning electron micrograph of a nano-vacuum gap device. Top image shows the top view; bottom image shows the enlarged cross-sectional view. (c) Non-volatile resistive switching I-V characteristic of Gap-10 device. With different compliance current, different resistance states were achieved. Inset: Logarithmic plotting. (d) Volatile-to-nonvolatile switching I-V characteristic of Gap-30 device. For volatile switching with low compliance current, the high resistance state was restored automatically. For non-volatile switching triggered by larger compliance current, a reset operation is essential to restore the high resistance state. (e) Volatile switching I-V characteristic of Gap-50 device, showing typical threshold switching for the positive polarity sweep, and cut-off characteristic for the negative polarity sweep. 155x99mm (300 x 300 DPI)


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Tunability of volatile switching under single pulse. (a) Switch-on voltages (threshold voltage) depends on different pulse widths (from 1 μs to 900 μs). (b) Time-solved current response under a single pulse (500 μs) operation with Sub-Vth, Near-Vth, and Super-Vth amplitudes, respectively. (c) Tine-resolved dynamic current change in Near-Vth region with different pulse widths. 158x99mm (300 x 300 DPI)



Tunability of switching behavior under pulse train. (a) Time-resolved output current responded to a Sub-Vth pulse train (pulse amplitude 0.8 V, pulse width 1 ms, pulse interval 500 μs, pulse number 100). Five regions with different switching behaviors were identified, including 1 No response, 2 No-to-volatile transition (inset), 3 Volatile switching, 4 Volatile-to-nonvolatile transition (inset), and 5 Nonvolatile switching. (b) Application map for the resistive device with different retention times. The numbers correspond to the switching regions in (a). With abundant switching dynamics, our device can fulfill requirements of different applications. (c) Schematic of full memristive neuromorphic network consisting of four key components: artificial synapse, artificial neuron, selector, and memory. 160x137mm (300 x 300 DPI)


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Transmission electron microscope (TEM) observation and mechanism of the tunable switching dynamics. (a) TEM image cross-sectional view of a Gap-50 device. (b) Cross-sectional TEM image of the device at nonvolatile region and EDX line scan profile. A Ag metallic filament (MF) was found in the nano-vacuum gap, bridging the MIEC layer and bottom electrode. (c) Cross-sectional TEM image of the device at volatile region and EDX line scan profile. A raptured MF residue was found in the nano-vacuum gap. (d) Schematics to show the state of MF into four stages, including initial, nucleation, growth & tunneling, and contact, and the equivalent circuit of the nano-vacuum gap device. Inset shows the competition between oxidation and reduction processes at MF/MIEC interface. 160x90mm (300 x 300 DPI)



Cross-functional device emulating synaptic plasticity and neuronal firing. (a) Schematic of the biology neuron cell and functional artificial synapse and artificial neuron. For our cross-functional device, Sub-Vth pulse train (pulse amplitude 1.2 V, pulse duration 1 ms, pulse interval 0.5 ms) operation to realize the firing activity of artificial neuron (bottom); Near-Vth pulse train (pulse amplitude 1.5 V, pulse duration 0.2 ms and pulse interval 0.1 ms) operation to realize the synaptic plasticity of artificial synapse (top). (b) Leaky behavior after threshold switching depending on switch ON current. Inset: time-resolved relaxation time measurement. (c) Statistics of fire pulse number depending on pulse amplitude. 160x163mm (300 x 300 DPI)


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Tunable Resistive Switching Enabled by Malleable Redox Reaction in

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