Bioinspired Tribotronic Resistive Switching Memory for Self-Powered

Research Article Cite This: ACS Appl. Mater. Interfaces 2017, 9, 43822−43829

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Bioinspired Tribotronic Resistive Switching Memory for Self-Powered Memorizing Mechanical Stimuli Yihui Sun,†,‡ Xin Zheng,†,‡,⊥ Xiaoqin Yan,*,‡ Qingliang Liao,‡ Shuo Liu,‡ Guangjie Zhang,‡ Yong Li,‡ and Yue Zhang*,‡,§ ‡

State Key Laboratory for Advanced Metals and Materials, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China § Beijing Municipal Key Laboratory of New Energy Materials and Technologies, University of Science and Technology Beijing, Beijing 100083, China ⊥ College of Materials & Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China S Supporting Information *

ABSTRACT: Haptic memory, from the interaction of skin and brain, can not only perceive external stimuli but also memorize it after removing the external stimuli. For the mimicry of human sensory memory, a self-powered artificial tactile memorizing system was developed by coupling bionic electronic skin and nonvolatile resistive random access memory (RRAM). The tribotronic nanogenerator is utilized as electronic skin to transform the touching signal into electric pulse, which will be programmed into the artificial brain: RRAM. Because of the advanced structural designs and accurate parameter matching, including the output voltages and the resistances in different resistive states, the artificial brain can be operated in self-powered mode to memorize the touch stimuli with the responsivity up to 20 times. For demonstrating the application potential of this system, it was fabricated as an independently addressed matrix to realize the memorizing of motion trace in two-dimensional space. The newly designed self-powered nonvolatile system has broad applications in next-generation high-performance sensors, artificial intelligence, and bionics. KEYWORDS: haptic memory, electronic skin, resistive random access memory, triboelectric nanogenerator, bioinspired

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Traditional flash memory,12 phase change material (PCM),13 and other memory solutions14 are attempted in electronic skin to carry out the goal of sensing−memorizing−integrating. Compared with other memory devices, resistive random access memory (RRAM) device is more promising as an excellent candidate for its superior memory performance: high scalability, low operating energy, great endurance, long retention time, and simple device structure.15−18 The two-terminal geometry of RRAM with a metal−insulator−metal (MIM) architecture makes it very convenient to couple with other devices.19,20 More inspiringly, the resistive switching memory, or sometimes termed memristor, can be used in neuron computing to simulate synaptic plasticity.21−24 So RRAM has a broader application in cooperating with electronic skin to imitate the interaction of human skin and human brain. The resistive switching memory, as the signal storage unit, is also chosen to integrate with resistive pressure sensors to achieve touching memory,25 but the complex writing and reading operations with

INTRODUCTION

With the springing up of flexible electronic devices, all kinds of sensors become research hotspots, such as electronic skin, which promises to be an excellent substitute for natural skin to perceive external stimuli.1−3 Considerable research focused on the sensing performance of electronic skin to achieve high sensitivities, fast response, low energy consumption (even selfpower), and assembling.4 For example, the introduction of single-electrode triboelectric nanogenerator (TENG) provides an exciting strategy to achieve high response and self-powered touch sensor, and multicells can be assembled to detect the movement path, kinematic velocity, and accelerated speed.5−10 However, for a complete sensing system, there are two sections that contain a signal detecting unit and signal storage unit. Just like human touching behavior, the skin sends the signal to the brain after it suffers from the external stimuli and the brain is responsible for memorizing the significant sensation information such as force, pain, shape, and texture.11 So, the volatile characteristic of electronic skin limited its applications toward emulating the exquisite tactile sensation of natural skin, and a suitable signal storage unit is desired. © 2017 American Chemical Society

Received: October 8, 2017 Accepted: November 21, 2017 Published: November 21, 2017 43822

DOI: 10.1021/acsami.7b15269 ACS Appl. Mater. Interfaces 2017, 9, 43822−43829

Research Article

ACS Applied Materials & Interfaces

Figure 1. Performance of Au/Ta2O5/AZO device. (a) Schematic illustration of the Au/Ta2O5/AZO junction structure. (b) Endurance of I−V curve with 80 cycles. (c) Endurance of resistances in HRS and LRS under 0.1 V read voltage with 80 cycles. (d) Cumulative distribution of the SET/ RESET voltages.

resistance at the given state.28 The resistive state cannot change back to HRS until the applied voltage is over 0.75 V, which is “erasing process”. Identically, it can also retain its resistive state under the threshold voltage or without electrical power. Quantized conductance, where the conductance is in units of the quantum of conductance G0=2e2/h, has been found in filamentary resistive switching.29 The I−V curves in the reset process are chosen to compare with the G0 boundary (Figure S3). The current jumps (at ∼0.75 V) crossing the G0 boundary represents the breaking of CF, which also confirms the filamentary resistive switching.30 Meanwhile, the device demonstrates reliable endurance of over 80 cycles as shown in Figure 1b. The endurance measurements that ensured the switching between on and off states were highly controllable, reversible, and reproducible.31,32 The resistances in LRS and HRS are particularly important for the output voltage of TENG. The different resistances will result in the different output voltages of TENG. So, the resistances in LRS and HRS are measured at 0.1 V read voltage as presented in Figure 1c. The resistances almost keep about 1 × 105 Ω in HRS and 1 × 104 Ω in LRS. Whats̀ more, the output voltages must higher than the SET voltage to drive the device switching from HRS to LRS. Cumulative distribution of the SET and RESET voltages illustrates that the SET voltage is ranging from −0.61 V to −0.92 V, whereas the RESET voltage is ranging from 0.365 to 0.875 V (Figure 1d). In general, there is compliance current (CC) to confine the growth of conductive filament in RS process, which is a big limit for its practical application.33 The compliance current is set by external device, which makes the system more complex and non-self-powered. In our work, the device can operate reliably without compliance current, which is necessary for our fully self-powered integrated system. An intelligent detector and power strategy can extremely simplify the integrated system. The high performed, selfpowered TENG (PTFE/Al) has been successfully fabricated with Al electrodes array (1.0 cm × 1.0 cm for each cell) as described in Figure 2a. The charges, generated by the frication

pressure partly cripple its superiority, and the continuously energy supply is another problem. Herein, the resistive random access memory (Au/Ta2O5/ AZO) and single electrode triboelectric nanogenerator (PTFE/ Al) are integrated to develop a self-powered sensing− memorizing−integrated system to simulate the cooperation of human skin and human brain. The TENG is used as signal detector (like human skin) and power source, while the RRAM is used as recorder (like human brain). After modulating the performance of each cell, the integrated system successfully achieves the touch sensing and memorizing. A 5 × 5 matrix of the device array is designed to detect the motion trajectory, and the signal can be saved even after the touching has ended. The fabricated system may propel research in electronic skin and artificial intelligence.

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RESULTS AND DISCUSSION Like the brain in haptic memory, the RRAM is the key in the sensing−memorizing−integrated system, which is different from traditional sensors. So, Ta2O5 is selected to sandwich between the Au squared top electrode and AZO bottom electrode to develop a RRAM device (Figure 1a), while reliable switching up to a trillion cycles has been demonstrated in TaOx-based devices.26,27 The memory device exhibits a typical hysteresis loop in the current−voltage (I−V) curve at positive and negative voltages (Figure 1b). An increasing negative bias is applied on top electrode and abruptly raising of current is occurred at −0.75 V. The sharp change in I−V curve in resistive switching behavior owes to the formation of conducting filaments in the Ta2O5−x film, differing from the homogeneous resistive switching. The device switches its resistance from high resistive state (HRS) to low resistive state (LRS), which is called “programming process”, and the threshold point of voltage is defined as Vset. It keeps in LRS even though the bias is moved, which shows nonvolatile feature. By that, after the device was switched on or off, the written data can be memorized, and no electrical power is needed to maintain the 43823


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Figure 2. Performance of PTFE/Al devices. (a) Schematic diagram of single-electrode TENG. (b) Output voltage with different loading resistances. (c)The measured short-circuit current density with the size of 1.0 cm × 1.0 cm. (d) Finite element simulation of the potential distribution in the TENG for the different separation distances between the skin and PTFE film.

of human skin and PTFE, result in the output voltage/current for the electrostatic induction to achieve the touching sensor.34 This self-powered touching sensing can reliability service hundreds times with 6 Hz touching as shown in Figure S4b. The cell size has a strong influence on the output of TENG, and the open-circuit voltage (Voc) with different electrode sizes is recorded in Figure S4c. The open-circuit voltage increases from 80 to 175 to 250 V, whereas the electrode size increases from 0.25 to 0.5 to 1.0 cm2. Regulating cell size can be utilized to modulate the performance of TENG, especially the output voltage. The large output voltage not only leads to a large driving force for the resistive switching of RRAM but also has the potential of breaking RRAM and small space resolution. Here, a suitable electrode size of 1.0 cm × 1.0 cm is accepted to explore the matching with RRAM. Besides, the effective output power of the TENG also depends on the loading resistance.35 Figure 2b shows the resistance dependence of output voltage with the resistance increasing from 1 KΩ to 1 GΩ. The output voltage of the device rises up with increasing loading resistance. The output voltage is the key factor of matching TENG with RRAM. The RRAM integrates with TENG as an external load, and the output voltage of TENG with the loading resistance of HRS must be large enough to drive RRAM switching its resistance from HRS to LRS. The output voltage is 1.2 V with the loading resistance of 100 KΩ, which could meet the requirement of integration. Because of the coupling between contact electrification and electrostatic induction, the inducted charge of TENG is decisive for its output. In the short-circuit (SC) condition, the inducted charge density is a function of short-circuit current density and time, which is given by the following:

Δσ =

∫t

t

JSC dt 0

Where JSC is the short-circuit current density, t and t0 are the time with and without contact, respectively, and Δσ is the accumulated charge density. With a 0.5 Hz cyclic pressing on the TENG, it produces a short-circuit current density of ∼200 μA/m2 and accumulated charge density of approximately −30 μC/m2 as shown in Figure 2c and Figure S4d. The inducted charge will generate a potential gradient in the load and drive work. To obtain a more quantitative understanding of the working principle, we employed finite element simulations to calculate the electric potential distribution in the TENG and the charge transfer between the AZO electrode and ground by COMSOL Multiphysics. The model constructed here has the same structure and dimensions (1.0 cm × 1.0 cm for TENG and 1.0 mm × 1.0 mm for RRAM). According to the measurement as displayed in Figure S4d, the triboelectric charge densities on the skin and PTFE film were assumed to be +30 and −30 μC/ m2, respectively, whereas the AZO film is grounded. The electric potential distributions between skin and PTFE (in the top panels), and between Au electrode and AZO (in the bottom panels) are calculated under different separation distances of 0.1, 1.0, 2.0, and 3.0 mm, respectively, as described in Figure 2d. When the skin and PTFE fully contact with each other, the electric potentials on both of panels approach zero. The electric potential difference was found to increase dramatically with increasing the separation distance. When they are separated by 3.0 mm, the electric potential on the skin surface can reach 1500 V. For the single-electrode-TENG, the electric potential difference between Au and AZO is more important, which is the actual output of TENG. Meanwhile, the 43824


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Figure 3. (a) Schematic diagram of the integrated system. (b) Current response with touching, releasing, and another touching operation at the reading voltage of 0.1 V, and the performance after electrically erasing. (c) Response time at touching operation.

electric potential gradient in Ta2O5 film is the most important driving force to drive the active ion in Ta2O5 migrating to achieve the switching from HRS to LRS. When the skin and PTFE are separated by 3.0 mm, the electric potential difference between AZO and Au can reach approximately −180 V. For that the conductivity of Ta2O5 in AC/DC module is zero, the calculated potential difference is close to open-circuit voltage, which matches the experimental data. On this basis, the Au/Ta2O5/AZO RRAM device and PTFE/ Al TENG device are combined as a touching sensing− memorizing−integrated system, and the structure diagram is illustrated in Figure 3a. The TENG is used as the smart signal sensor and power source, whereas the RRAM is the nonvolatile artificial brain. The bias of 0.1 V, which cannot switch the resistance of RRAM, is applied for real time observing the touch-response, as shown in Figure 3b. At first, the current keeps at 5 × 10−7 A. Although the finger touches the surface of PTFE, the current is suddenly elevated to 1 × 10−5 A, which exhibits an excellent touching response and the responsivity reaches 20 times (the current ratio after and before touching). After releasing the pressure, the current still maintains unchanged, which shows the nonvolatile characteristic. The nonvolatile property is necessary for haptic memory to record the touching signal. Identically, another touching was added to show the outstanding ability of anti-interference. After electrically erasing, the memory device was reset into HRS, and the current at 0.1 V bias went back to 5 × 10−7 A. The ability of reverting to HRS reveals the potential application in

reusable integrated system. The detailed response process is magnified in Figure 3c. The response time is about 1.46 s, which contains three processes: the transform process from pressure signal to electrical signal, the electrical program process and reading process. Meanwhile, a series of measurements without external bias, containing of responsive test, nonvolatile test, anti-interference test and recoverable test, are carried out as presented in Figure 4: (1) a read voltage of 0.1 V is applied on the RRAM device to confirm the HRS of RRAM; (2) a finger-contact operation is made on the top surface of PTFE to write the touching signal into RRAM without any external energy; (3) a read voltage of 0.1 V is applied and RRAM is found to have been switched from HRS to LRS, which verified the successfully touchprogramming; (4, 5) “touch programming” and “reading” again after first recording to test its anti-interference performance. The anti-interference performance indicates that only the first touching can be memorized and the subsequent touching is useless, which can effectively prevent the missing of recorded data, just like the fingerprint identification; (6) an electrical erasing is applied in RRAM to switch the memory back to HRS, which is detailed showed in Figure 4 and certified in (7). The touching sensing−memorizing−integrated system successfully achieves touching-programing and electrical erasing, which illustrates the function of haptic memory and reusability. The nonvolatile characteristic, after repealing touching or applying another touching, makes sense for its applications. There are two key issues in this integrated system: 43825


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Figure 4. Series of measurements, containing of responsive test, nonvolatile test, anti-interference test and recoverable test. In steps 1, 3, 5, and 7, 0.1 V bias was applied to read the resistive state and cannot change the resistive state. In steps 2 and 4, mechanical stimuli was programmed by touching without any bias. In step 6, a reset voltage was applied to erase the touching signal.

integrated system. So, the TENG can obtain a high output voltage when the RRAM is in HRS, and achieve a low output voltage when the RRAM is in LRS. The high output voltage is used as programing voltage to record the touching signal, whereas the low output voltage doesn’t work. By that, the shortcoming of the bipolar output of TENG can be effectively overcome. In experiment, the fabricated system successfully realized touch programming and haptic memory as described in Figure 4. The opposite output voltage and another touch have no effect on the recorded data. For further confirmation and interpretation by calculating simulation, COMSOL Multiphysics with the same parameters as above is used to analyze the electric potential distribution in Ta2O5 film with a ruptured and connected conductive filament. Figure 5 describes the total running process. The skin is positively charged and the PTFE is negatively charged, after they rub against each other. The negative charge in PTFE surface repels the electrons from Al in TENG device into Au in RRAM device, when skin and PTFE are separated, as analyzed in Figure 4. The repelled electrons in Au electrode results in the potential difference between Au electrode and AZO electrode. When the conductive filament is ruptured and the RRAM device is in HRS, the potential difference between Au and AZO can reach approximately −5 V (the separated distance between skin and PTFE is 3 mm). The potential difference is high enough to drive the conductive

One is how to make sure that the output voltage of TENG could drive the memory device switching from HRS to LRS. For the self-powered characteristic of our system without compliance current, excessive output voltage of TENG would lead to permanent breakdown and result in RRAM failure. So, the resistance of RRAM in HRS is controlled to maintain at ∼1 × 105 Ω. Meanwhile, different cell sizes of TENG are adopted to modulate its output to meet the threshold voltage of RRAM as presented in Figure S4c. The output voltage with the cell size of 1 cm2 can reach ∼1.2 V at a loading resistance of 1 × 105 Ω, which is larger than the set voltage of RRAM and woǹt give rise to the failure of RRAM. The other concern is how to prevent the opposite output voltage switching the memory back to HRS. In TENG, the positive electric potential and negative electric potential appear alternately, which is detrimental for recording. For instance, a positive electric potential can be used to program the data into memory, but the subsequent negative electric potential may erase it, and as a result, the data still won’t be recorded. Rectifier and other solutions can be used to avoid this phenomenon, but it will also make the sensor system more complex, and bring in other problems. The RRAM can perfectly overcome the shortcoming for the different resistances in HRS and LRS. As measured in Figure 2b, the output voltage of TENG will increase with the load resistance, and the RRAM can be regarded as an external load of TENG in RRAM-TENG 43826


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Figure 5. Mechanism diagram of integrated system in a fully cycle: (a) Full contact between human skin and the PTFE film; (b) two surfaces separated; (c) two surfaces separated by a large distance; (d) two surfaces contacted again. (e, f) Simulated potential distribution with the cracked conductive filament (HRS) and formed conductive filament (LRS). (g) Simplified mechanism diagram.

filament connecting and RRAM device switching from HRS to LRS. As skin is drawn closer to PTFE, the electrons are attracted from the Au electrode to Al electrode. The potential difference resulted from the equal electrons can only achieve 4 × 10−4 V, as simulated in Figure 5f. The small potential difference is useless for resistive switching of RRAM. So, the skin touching can only switch the memory device once: from HRS to LRS, which can effectively avoid the disadvantage of bipolar output voltage of TENG. The simplified mechanism can be found in Figure 5g. Meanwhile, the constructed system can also be reused after external electrically erasing. In our system, RRAM and TENG devices are integrated to utilize respective characteristics and to overcome respective shortages to realize the functions of completely self-powered, nonvolatile sensing and memorizing. The cell (integrating RRAM and TENG) matrix with 3 × 3 pixels in size of 1 cm × 1 cm is constructed as can be seen in Figure 6a. Before press, the current read from RRAM device is only ∼1 × 10−6 A, which demonstrates the HRS in RRAM. When touch operation is applied on the middle cell of the matrix, its read current becomes ∼1 × 10−5 A, whereas others remain unchanged. More uplifting is that this change in resistance can be maintained, even though the finger is taken away, or touch again. To get it vivid, the finger is reddened, and the surface of PTFE turns incarnadine. Meanwhile, the stamp of the finger will stay in the surface, even though the finger is taken away, which is just like the cooperation of human skin and brain. When touching something, we can not only feel the shape of it but also remember it. Our integrated system can also realize this function, which may be applied in electric skin and artificial intelligence (AI) device.

Figure 6. (a) Photograph of cell matrix with 3 × 3 pixels and relevant current mapping before, in and after press. The bottom panels of b−e correspond to the illustration of the finger moving along the paths of letters “U”, “S”, “T”, and “B”. The top panels are the calculated current peak areas for each PTFE/Al−Au/Ta2O5/AZO sample in response to the motion trajectory of letters “U”, “S”, “T”, and “B”.

To illustrate the broadened applications of the integrated system, we fabricated a 5 × 5 matrix of the device array. Human finger or hand is slid across the top surface of PTFE matrix, and the outputs are generated to drive the RRAM memorizing the 43827


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ACS Applied Materials & Interfaces output signals. As exhibited in Figure 6b−e, when the finger moves on the PTFE matrix along the path of letter “U”: electrode 51 (E51) → electrode 41 (E41) → electrode 31 (E31) → electrode 21 (E21) → electrode 11 (E11) → electrode 12 (E12) → electrode 13 (E13) → electrode 14 (E14) → electrode 15 (E15) → electrode 25 (E25) → electrode 35 (E35) → electrode 45 (E45) → electrode 55 (E55), the generated triboelectric signal can be recorded by each RRAM cell. All the processes are self-powered. Then, a voltage of 0.1 V is applied in RRAM to read the recorded signal in each cell. The read currents are collected and arranged according to the position of each cell as summarized in the bottom panel of Figure 6b. The cells related to the motion trajectory clearly possessed higher current and the letter “U” can be observed, which proved the possibility of trace memorization. Then, other moving trajectories of letter “S”, “T”, and “B” was also performed on the surface of the TENG matrix, as shown in Figure 6c−e. Accordingly, the measured currents from each RRAM cell were summarized and the results were in good agreements with the motion trajectory.

kV operating voltage was utilized to study the morphologies of the samples. The electrical properties of the device were performed by Keithley 4200 testing system with a limited compliance current of 100 mA to avoid permanent damaging. The valence states of Ta and O elements were analyzed by X-ray photoelectron spectroscopy (XPS, ESCALAB 250 Xi). The numerical simulation of the potential distribution in PTFE and Ta2O5 was completely done using COMSOL.

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S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.7b15269. Characterization of RRAM device, analysis of quantized conductance, and performance of TENG (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Tel: +86 10 62334725. Fax: +86 10 62332011. *E-mail: [email protected].

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CONCLUSION In summary, a fully self-powered smart skin integrating with RRAM and TENG has been developed to successfully memorize the mechanical stimuli. The mechanical touching signal was transformed into electrical signal by TENG, and was subsequently written into RRAM. The resistances of the CCfree RRAM in HRS and LRS were maintained at 1 × 105 Ω and 1 × 104 Ω, respectively. At the loading resistance of 1 × 105 Ω, the output voltage of TENG is just a little larger than the SET voltage of RRAM to make sure the resistive switching of RRAM without permanent breakdown. Meanwhile, at the loading resistance of 1 × 104 Ω, the output voltage of TENG makes no sense for the RRAM, which could automatically exclude the concomitant reverse stimuli from TENG or another triboelectric signal. By that, the integrated system could be programmed by finger touch to come true the function of haptic memory and ideal anti-interference ability, exhibiting the coupling of sensing and recording to simulate the interaction of skin and brain. The responsivity and response time reached about 20 times and 1.46 s, respectively. Moreover, the achieved integrated system array demonstrated the capability of memorizing the finger motion trajectory in two-dimensional space. The rise of completely self-powered, sensing-memorizing-integrated system may open new avenues for the next generation high-performance smart systems for applications in smart sensors, electronic skins, and artificial intelligence.

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ASSOCIATED CONTENT

ORCID

Xiaoqin Yan: 0000-0003-4553-6012 Yue Zhang: 0000-0001-7772-3280 Author Contributions †

Y.S. and X.Z. contributed equally to this work. Y.S. proposed the initial idea and completed the experiment. X.Z. was responsible for the theory analysis and the application of haptic memory system. X.Y. supervised the project and improved the original idea and the theory analysis. Q.L. helped design the experiment. S.L. helped fabricate the micro/nanostructures in the RRAM units. G.Z. helped test the TENG units. Y.L. grew the Ta2O5 in the RRAM. Y.Z. supervised the project and contributed the data analysis and discussion. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by National Natural Science Foundation of China (51372023, 51772024, 51702014), the National Key Research and Development Program of China (2016YFA0202701), the National Major Research Program of China (2013CB932602), the Program of Introducing Talents of Discipline to Universities (B14003), Beijing Municipal Science & Technology Commission, and the Fundamental Research Funds for Central Universities.

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EXPERIMENTAL SECTION

Fabrication of RRAM-TENG-Integrated System. The RRAM devices used in this work with a size of 1 mm * 1 mm were fabricated on AZO (1 μm)/glass substrates with electrodes patterned using traditional photolithography. First, a 300 nm Ta2O5 layer was deposited by RF sputtering (100 W) using a Ta2O5 ceramic target in an Ar/O2 gas mixture at room temperature. The argon partial pressure and oxygen partial pressure are 32 and 8 sccm, respectively. Then, a 100 nm top Au electrode was then deposited by photolithography, thermal evaporation, and lift-off processes. For the TENG, the Al foils with different sizes (1.0 cm × 1.0 cm, 0.5 cm × 1.0 cm, and 0.5 cm × 0.5 cm) were adhered on the PTFE surface. The Au electrode of RRAM and Al electrode of TENG are simply connected by copper wire. Characterization and Electrical Measurements. Field-emission scanning electron microscopy (FESEM) (Quanta 3D FEG) with a 20

REFERENCES

(1) Schwartz, G.; Tee, B. C.; Mei, J.; Appleton, A. L.; Kim, D. H.; Wang, H.; Bao, Z. Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nat. Commun. 2013, 4, 1859. (2) Mannsfeld, S. C.; Tee, B. C.; Stoltenberg, R. M.; Chen, C. V.; Barman, S.; Muir, B. V.; Sokolov, A. N.; Reese, C.; Bao, Z. Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nat. Mater. 2010, 9, 859−864. (3) Lipomi, D. J.; Vosgueritchian, M.; Tee, B. C.; Hellstrom, S. L.; Lee, J. A.; Fox, C. H.; Bao, Z. Skin-like pressure and strain sensors based on transparent elastic films of carbon nanotubes. Nat. Nanotechnol. 2011, 6, 788−792. (4) Yao, S.; Zhu, Y. Nanomaterial-enabled stretchable conductors: strategies, materials and devices. Adv. Mater. 2015, 27, 1480−1511.

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Research Article


(26) Lee, M. J.; Lee, C. B.; Lee, D.; Lee, S. R.; Chang, M.; Hur, J. H.; Kim, Y. B.; Kim, C. J.; Seo, D. H.; Seo, S.; Chung, U. I.; Yoo, I. K.; Kim, K. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5‑x/TaO2‑x bilayer structures. Nat. Mater. 2011, 10, 625−630. (27) Yang, J. J.; Strukov, D. B.; Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 2013, 8, 13−24. (28) Sun, Y. H.; Yan, X. Q.; Zheng, X.; Liu, Y. C.; Zhao, Y. G.; Shen, Y. W.; Liao, Q. L.; Zhang, Y. High on-off ratio improvement of ZnObased forming-free memristor by surface hydrogen annealing. ACS Appl. Mater. Interfaces 2015, 7, 7382−7388. (29) Zhu, X. J.; Su, W. J.; Liu, Y. W.; Hu, B. L.; Pan, L.; Lu, W.; Zhang, J. D.; Li, R. W. Observation of conductance quantization in oxide-based resistive switching memory. Adv. Mater. 2012, 24, 3941− 3946. (30) Long, S. B.; Lian, X. J.; Cagli, C.; Cartoixà, X.; Rurali, R.; Miranda, E.; Jiménez, D.; Perniola, L.; Liu, M.; Suñe,́ J. Quantum-size effects in hafnium-oxide resistive switching. Appl. Phys. Lett. 2013, 102, 183505. (31) Sun, Y. H.; Yan, X. Q.; Zheng, X.; Liu, Y. C.; Shen, Y. W.; Zhang, Y. Influence of carrier concentration on the resistive switching characteristics of a ZnO-based memristor. Nano Res. 2016, 9, 1116− 1124. (32) Sun, Y. H.; Yan, X. Q.; Zheng, X.; Li, Y.; Liu, Y. C.; Shen, Y. W.; Ding, Y.; Zhang, Y. Effect of carrier screening on ZnO-based resistive switching memory devices. Nano Res. 2017, 10, 77−86. (33) Huang, C. H.; Huang, J. S.; Lin, S. M.; Chang, W. Y.; He, J. H.; Chueh, Y. L. ZnO1‑x nanorod arrays/ZnO thin film bilayer structure: from homojunction diode and high-performance memristor to complementary 1D1R application. ACS Nano 2012, 6, 8407−8414. (34) Meng, B.; Tang, W.; Too, Z. H.; Zhang, X. S.; Han, M. D.; Liu, W.; Zhang, H. X. A transparent single-friction-surface triboelectric generator and self-powered touch sensor. Energy Environ. Sci. 2013, 6, 3235. (35) Tang, W.; Meng, B.; Zhang, H. X. Investigation of power generation based on stacked triboelectric nanogenerator. Nano Energy 2013, 2, 1164−1171.

(5) Yang, Y.; Zhang, H.; Lin, Z. H.; Zhou, Y. S.; Jing, Q.; Su, Y.; Yang, J.; Chen, J.; Hu, C.; Wang, Z. L. Human skin based triboelectric nanogenerators for harvesting biomechanical energy and as selfpowered active tactile sensor system. ACS Nano 2013, 7, 9213−9222. (6) Zhu, G.; Zhou, Y. S.; Bai, P.; Meng, X. S.; Jing, Q.; Chen, J.; Wang, Z. L. A shape-adaptive thin-film-based approach for 50% highefficiency energy generation through micro-grating sliding electrification. Adv. Mater. 2014, 26, 3788−3796. (7) Zhang, C.; Tang, W.; Zhang, L. M.; Han, C. B.; Wang, Z. L. Contact Electrification Field-Effect Transistor. ACS Nano 2014, 8, 8702−8709. (8) Zhang, C.; Wang, Z. L. Tribotronics-A new field by coupling triboelectricity and semiconductor. Nano Today 2016, 11, 521−536. (9) Pang, Y. K.; Li, J.; Zhou, T.; Yang, Z. W.; Luo, J. J.; Zhang, L. M.; Dong, G. F.; Zhang, C.; Wang, Z. L. Flexible transparent tribotronic transistor for active modulation of conventional electronics. Nano Energy 2017, 31, 533−540. (10) Zhang, C.; Zhang, L. M.; Tang, W.; Han, C. B.; Wang, Z. L. Tribotronic Logic Circuits and Basic Operations. Adv. Mater. 2015, 27, 3533−3540. (11) Johansson, R. S.; Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 2009, 10, 345−359. (12) Li, J.; Zhang, C.; Duan, L.; Zhang, L. M.; Wang, L. D.; Dong, G. F.; Wang, Z. L. Flexible Organic Tribotronic Transistor Memory for a Visible and Wearable Touch Monitoring System. Adv. Mater. 2016, 28, 106−110. (13) Chen, X.; Iwamoto, M.; Shi, Z.; Zhang, L.; Wang, Z. L. SelfPowered Trace Memorization by Conjunction of Contact-Electrification and Ferroelectricity. Adv. Funct. Mater. 2015, 25, 739−747. (14) Kim, B. Y.; Lee, W. H.; Hwang, H. G.; Kim, D. H.; Kim, J. H.; Lee, S. H.; Nahm, S. Resistive Switching Memory Integrated with Nanogenerator for Self-Powered Bioimplantable Devices. Adv. Funct. Mater. 2016, 26, 5211−5221. (15) Yao, J.; Lin, J.; Dai, Y.; Ruan, G.; Yan, Z.; Li, L.; Zhong, L.; Natelson, D.; Tour, J. M. Highly transparent nonvolatile resistive memory devices from silicon oxide and graphene. Nat. Commun. 2012, 3, 1101. (16) Huang, C. H.; Huang, J. S.; Lai, C. C.; Huang, H. W.; Lin, S. J.; Chueh, Y. L. Manipulated Transformation of Filamentary and Homogeneous Resistive Switching on ZnO Thin Film Memristor with Controllable Multistate. ACS Appl. Mater. Interfaces 2013, 5, 6017−6023. (17) Linn, E.; Rosezin, R.; Kugeler, C.; Waser, R. Complementary resistive switches for passive nanocrossbar memories. Nat. Mater. 2010, 9, 403−406. (18) Waser, R.; Aono, M. Nanoionics-based resistive switching memories. Nat. Mater. 2007, 6, 833−840. (19) Jo, S. H.; Kim, K. H.; Lu, W. High-density crossbar arrays based on a Si memristive system. Nano Lett. 2009, 9, 870−874. (20) Yang, Y. C.; Pan, F.; Liu, Q.; Liu, M.; Zeng, F. Fully roomtemperature-fabricated nonvolatile resistive memory for ultrafast and high-density memory application. Nano Lett. 2009, 9, 1636−1643. (21) Strukov, D. B.; Snider, G. S.; Stewart, D. R.; Williams, R. S. The missing memristor found. Nature 2008, 453, 80−83. (22) Ohno, T.; Hasegawa, T.; Tsuruoka, T.; Terabe, K.; Gimzewski, J. K.; Aono, M. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 2011, 10, 591−595. (23) Wang, Z. Q.; Xu, H. Y.; Li, X. H.; Yu, H.; Liu, Y. C.; Zhu, X. J. Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor. Adv. Funct. Mater. 2012, 22, 2759−2765. (24) Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010, 10, 1297−1301. (25) Zhu, B.; Wang, H.; Liu, Y.; Qi, D.; Liu, Z.; Wang, H.; Yu, J.; Sherburne, M.; Wang, Z.; Chen, X. Skin-Inspired Haptic Memory Arrays with an Electrically Reconfigurable Architecture. Adv. Mater. 2016, 28, 1559−1566. 43829


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