Zinc oxide and indium-gallium-zinc-oxide bi-layer synaptic device with highly linear long-term potentiation and depression characteristics

Zinc oxide and indium-gallium-zinc-oxide bi-layer synaptic device with highly linear long-term potentiation and depression characteristics


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ABSTRACT The electrical properties, resistive switching behavior, and long-term potentiation/depression (LTP/LTD) in a single indium-gallium-zinc-oxide (IGZO) and bi-layer IGZO/ZnO (ZnO:


zinc oxide) memristors were investigated for synapse application. The use of the oxide bi-layer memristors, in particular, improved electrical properties such as stability, memristor


reliability, and an increase in synaptic weight states. The set voltage of bi-layer IGZO/ZnO memristors was 0.9 V, and the reset voltage was around − 0.7 V, resulting in a low-operating


voltage for neuromorphic systems. The oxygen vacancies in the X-ray photoelectron spectroscopy analysis played a role in the modulation of the high-resistance state (HRS) (oxygen-deficient)


and the low-resistance state (oxygen-rich) region. The VRESET of the bi-layer IGZO/ZnO memristors was lower than that of a single IGZO, which implied that oxygen-vacancy filaments could be


easily ruptured due to the higher oxygen vacancy peak HRS layer. The nonlinearity of the LTP and LTD characteristics in a bi-layer IGZO/ZnO memristor was 6.77% and 11.49%, respectively,


compared to those of 20.03% and 51.1% in a single IGZO memristor, respectively. Therefore, the extra ZnO layer in the bi-layer memristor with IGZO was potentially significant and essential


to achieve a small set voltage and a reset voltage, and the switching behavior to form the conductive path. SIMILAR CONTENT BEING VIEWED BY OTHERS IMPLEMENTATION OF THRESHOLD- AND


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IMPLANTATION Article Open access 11 December 2020 INTRODUCTION In complex situations, the human brain can process information in parallel and accurately recognize objects and visual


information1,2. Neuromorphic information processing systems are expected to be the next-generation computing technology that can overcome the limitations of traditional information


processing systems, such as von Neumann computing. The use of memory structures is the most notable distinction between neuromorphic systems and traditional information processing systems.


Several transistors and capacitors have been used in conventional information processing systems to emulate a single synapse, increasing power consumption, and limiting integration


density3,4,5. Many kinds of research are currently being studied to develop a single device with a new concept that simultaneously performs inherent learning and data storage functions


without memory devices6,7,8,9,10,11. Because of its ability to modulate conductance, resistive switching random access memory (ReRAM) has recently emerged as the most promising candidate for


synaptic devices. Furthermore, because of its simple structure, fast switching speed, lower power consumption, and higher scalability, ReRAM with a metal–insulator–metal structure capable


of realizing synaptic networks have an advantage of integration at high density (4F2)12,13,14. Among the various dielectric materials used as resistive switching (RS) layers, such as hafnium


oxide15,16, tantalum oxide17,18 and titanium oxide19,20,21, indium-gallium-zinc-oxide (IGZO) and zinc oxide (ZnO) have attracted much attention as one of the most promising due to its


outstanding good uniformity for large area deposition, low cost, and multiple functional abilities22,23. Furthermore, an advantage of IGZO material is the ease with which the RS behavior can


be switched as the mobility of the carrier increases due to an increase in indium concentration, allowing for high-speed operation. Following a high driving current, the increased


concentration of indium in the IGZO layer increased mobility, while the gallium concentration controlled the amount of oxygen vacancy24,25. ZnO is an excellent material of chemical


stability, and has a wide direct bandgap and high transparency properties. However, because donor defects in ZnO materials contribute to n-type conduction, high resistive ZnO switching films


are difficult to achieve. The low-resistance state (LRS) layer is positioned on top of the bi-layer structure, resulting in good switching behavior. However, memristor devices for


hardware-based neural networks should consider the linear and symmetric changes in conductance with the number of potentiation and depression pulses. Furthermore, the SET pulse applied as


the pre-synaptic input can determine the consumed energy per the weight update for the network training. The relatively high SET amplitudes and long pulse width must be further improved for


application in energy-efficient and largescale neuromorphic device arrays. The potentiation and depression characteristics of single-layer memristor devices are frequently nonlinear,


resulting in less efficient neural network processing. It is believed that the bi-layer memristor device’s structure achieves high linearity of the potentiation and depression


characteristics. The electrical properties of the ReRAM with an additional oxide layer between the bottom electrode and the IGZO layers, on the other hand, are yet to be investigated. The


use of oxide bi-layers improved electrical properties such as stability, memristor reliability, and an increase in synaptic weight states26,27,28. This paper proposed bi-layer IGZO/ZnO


memristors to improve the electrical characteristics and synaptic linearity in long-term potentiation/depression (LTP/LTD) characteristics compared with a single IGZO memristor. The


conduction mechanism of the charge transport behavior at HRS and LRS was validated by the role of the potential barrier between the bottom electrode and HRS material. To our knowledge, the


high linearity in LTP/LTD characteristics of the bi-layer IGZO/ZnO memristors is more linear than that of other reported devices29,30,31,32. METHODS A single structure of memristor crossbar


was fabricated to a square type, as shown in Fig. 1, which could be integrated at high density. The Ti layer, which served as the bottom electrode, was deposited at 100 nm using a radio


frequency (RF) sputtering system and patterned using photolithography’s lift-off process. Memristors are classified into two types: single IGZO memristors and bi-layer IGZO/ZnO memristors.


The single-layer IGZO was deposited using the following RF sputtering system. First, 50 nm-thick IGZO used as an HRS was deposited with the molecular composition of In:Ga:Zn = 1:1:1 at a


working pressure of 2 mTorr (Ar gas flow rate: 30 standard cubic centimeters per min (sccm)) without substrate heating (substrate temperature < 50 °C). The 10 nm-thick IGZO for an LRS was


then deposited with the molecular composition In:Ga:Zn = 2:2:7, and the other conditions were the same as described above. To control the amount of oxygen vacancy, the oxygen gas flow was


adjusted as 20 sccm for HRS and 1 sccm for LRS when the IGZO layers were deposited. For the bi-layer IGZO/ZnO structure, a 50 nm ZnO layer was deposited using an atomic layer deposition


system with DEZn (diethylzinc, Zn(CH2CH3)2) and an H2O of source precursors at an 80 °C chamber temperature, followed by a 10 nm IGZO LRS deposition using an RF sputtering system. Finally,


the Ti layer was deposited as the top electrode using the RF sputtering system, which is identical to the bottom electrode process through photolithographic patterning to form a crossbar


type device. Figure 1b showed the top-view images of the field emission scanning electron microscopy (FE-SEM, Hitachi, Japan, S-4800) in a 20 μm × 20 μm crossbar structure. The electrical


properties of a single IGZO and bi-layer IGZO/ZnO memristors were measured at room temperature using the Keysight B1500A semiconductor device analyzer. For the transition from HRS to LRS,


the current compliance was used. The Keysight B1530 waveform generator/fast measurement unit, which also has current and voltage measurement functions, was used to apply voltage pulses


across the single IGZO and bi-layer IGZO/ZnO memristors. Precision and long-term sampling measurements were used to measure the current across the diffusive memristor and to measure the


voltage at the output of the waveform generator. The long-term sampling measurements allowed the application of pulse signals down to 100 ns and precise current measurement at a sampling


rate of 50 ns. To minimize the disturbance on device conductance, a write pulse with amplitude 2 V was applied across a single IGZO and bi-layer IGZO/ZnO memristor, and then a short duration


(20 or 50 ms) was used as the read voltage. Positive/negative pulses were applied at a rate of 50 for single IGZOs and 100 for bi-layer IGZO/ZnO memristors, and the current was measured


after each stimulation pulse. All equipment in the setup is controlled by the Agilent Easy Expert software. RESULTS AND DISCUSSION Figure 2 shows the RS behaviors of 10 cycles in a single


IGZO and bi-layer IGZO/ZnO memristors. This result shows the typical bipolar RS characteristics with the bottom electrode grounded in electrical measurements. The memristor devices are


initially in HRS mode. A voltage sweep from 0 to 3 V (meaning arrow “1” in the figure) is applied to the top electrode with a compliance current of 1 mA, and the RS behaviors of the single


IGZO and bi-layer IGZO/ZnO memristors change from HRS to LRS. The compliance current is set to prevent memristor devices from being permanently damaged by a sudden increase in current


levels. Once again, the voltage sweep is applied from 2 to 0 V (meaning arrow “2” in the figure) to measure the current and the RS behaviors of the single IGZO and bi-layer IGZO/ZnO


memristors that remain in the LRS and show a high current level. The arrows “1” and “2” in the figure are referred to as “SET,” and they indicate that the RS behaviors of the single IGZO and


bi-layer IGZO/ZnO memristors change from HRS to LRS. The process of changing from arrow “3” to “4” in the figure is called “RESET,” and it means that the RS behaviors of the single IGZO and


bi-layer IGZO/ZnO memristors change from LRS (arrow “3”) to HRS (arrow “4”). Because conductance values (the inverse of resistance values) are used as synaptic weights in binarized neural


networks, these findings can help them. The set voltages of the single IGZO and bi-layer IGZO/ZnO memristors are 1 V and 0.9 V, respectively. In contrast, the reset voltages of a single IGZO


and bi-layer IGZO/ZnO memristors are about − 1.8 V and − 0.7 V, respectively. Therefore, in the case of IGZO/ZnO structure, the memristor device can be used as a low operation, which can


improve the RS speed because the donor defects in ZnO materials contribute to the n-type conduction. Figure 3 shows the atomic force microscopy results of a single IGZO (1:1:1), IGZO


(2:2:7), and ZnO films, and bi-layer memristors for a measurement area of 1 × 1 μm. The root-mean-square (RMS) roughness value of a single IGZO (1:1:1), IGZO (2:2:7), and ZnO films is 0.112 


nm, 0.145 nm, and 2.247 nm, respectively. The RMS roughness of a single IGZO memristor and a bi-layer IGZO/ZnO memristor is 0.121 nm and 2.176 nm, respectively. These findings imply that


small grains with a nanometric dimension of 33 can be used to observe a typical polycrystalline ZnO film. Figure 4 depicts the results of the Hall measurements on carrier concentration,


resistivity, and Hall mobility of a single IGZO (1:1:1), IGZO (2:2:7), and ZnO film. The electrical properties of the carrier concentration, resistivity, and Hall mobility for a single IGZO


(1:1:1) and IGZO (2:2:7) films are greater than for a single ZnO film33,34. In particular, the resistivity of IGZO (2:2:7) decreases with the increase of gallium (Ga) content because Ga3+


forms strong bonds with oxygen33,34. The resistivity of each layer for a single IGZO and bi-layer IGZO/ZnO memristors is proportional to the reciprocal of the product of carrier


concentration _N_ and mobility _μ_ as reported in Ref.35. It can confirm the conjecture of controlling the carrier concentration via oxygen-related defects associated with Ga. X-ray


photoelectron spectroscopy (XPS, Thermo Fisher Scientific, USA, K-Alpha+) measurements were performed to investigate the chemical composition of the single IGZO and bi-layer IGZO/ZnO


memristors, to verify the proportions of the oxygen vacancy. Figure 5 depicts the XPS analysis result of the oxygen vacancy peak (O1) spectra in the surface after deposition of each single


HRS and LRS layer for the single IGZO and bi-layer IGZO/ZnO memristors using the Gaussian peak fitting. The proportions of the O1 of the HRS layer for a single IGZO and bi-layer IGZO/ZnO


memristors are 45.2 percent and 38.2 percent, respectively, while the proportion of the LRS layer for both memristors is around 43.4 percent. Because the Ga/Zn ratio determines the oxygen


concentration in HRS and LRS layers during IGZO sputter-deposition, increasing the Ga/Zn ratio increases the number of non-oxygen vacancies in the memristor devices, resulting in a lower


conductivity layer36. The chemical composition obtained from the XPS analysis result plays an important role in distinguishing each HRS and LRS layer for the RS performance in the bi-layer


memristor structure. The oxygen vacancies modulate the HRS layer (oxygen-rich) into the LRS layer (oxygen-deficient). The VRESET of the bi-layer IGZO/ZnO memristors is lower than that of a


single IGZO, which implied that the oxygen vacancy filaments could be easily ruptured due to the lower oxygen vacancy peak HRS layer. To verify the mechanism of the RS behaviors for a single


IGZO and bi-layer IGZO/ZnO memristors, the corresponding I–V characteristics of the SET and RESETs are plotted in Fig. 6. A linear fitting slope based on experimental data for the single


IGZO and bi-layer IGZO/ZnO memristors is close to one, indicating a linear relationship between the current and applied voltage37. The charges originating at the metal electrode interface


are thought to be trapped by the HRS layer’s empty trap sites of IGZO and ZnO. As the electric field across memristor devices increases, the steep current for a single IGZO is followed by a


quadratic term (I ∝ V2) with the increase of the injected charges when the conductive filaments form between two electrodes, as shown Fig. 6. When the empty trap sites are gradually occupied


completely, the slope of the fitting line decreases by about 2, indicating that the conduction enters the trap-free space charge limited current (SCLC). It implies that the SCLC is dominant


because the majority of injected electrons contribute to the current component38,39,40,41,42,43,44. However, the slope of the fitting line at the high electric field for bi-layer IGZO/ZnO


memristor is found to be 4.0, which means that the Schottky emission mechanism is dominant. In a high electric field, the Schottky emission mechanism may be caused by oxygen vacancies near


the metal/metal-oxide interface. The Schottky emission and ohmic mechanisms dominate the I–V characteristics of the RESET, as illustrated in Fig. 6. The switching behaviors of a single IGZO


and bi-layer IGZO/ZnO memristors were controlled by the interface properties due to the Schottky emission mechanism. It should be noted that the SCLC is the primary conduction mechanism in


the SET for single-layer IGZO and bi-layer IGZO/ZnO memristors. We conclude the Schottky emission mechanism observed in a single IGZO and bi-layer IGZO/ZnO memristors at the RESET is


attributed to the interface barrier. Figure 7 shows the long-term potentiation/depression (LTP/LTD) characteristics with applied positive/negative pulses for an amplitude of 2 V in the two


memristors. The positive (2 V, 400 ns) or negative (− 2 V, 400 ns) voltage pulses with the interval time (4.5 μs) are applied on the memristor devices, and then, the current is measured by a


reading voltage pulse (0.2 V, 1 μs) after each pulse. Depending on the input spiking signal, the LTP and LTD characteristics exhibit gradual potentiation and depression in synaptic weight,


which can be used to determine whether memristors can learn or not. When a potentiating input signal train of positive pulses with an amplitude of 2 V is applied to the top Ti metal of the


memristor synapse (pre-neuron), the synaptic weight changes progressively as the current increases, indicating that oxygen vacancies are injected into the RS layer, and this process is then


formed between TE and BE for potentiation. It can emulate the potential of oxygen vacancies for neuromorphic computing, which enhances the synapse weight by releasing neurotransmitters. When


a depressing input signal train consisting of negative pulses with an amplitude of − 2 V is applied to the top metal, the synaptic weight is gradually depressed, and the conductive path


formed by the oxygen vacancies moves away from the bottom metal, causing the current to decrease. The nonlinearity for the memristor devices is quantitatively given by Eq. (1) $$Nonlinearity


= average\left[ {\left| {\frac{{G - G_{Linear} }}{{G_{Linear} }}} \right| \times 100 \% } \right]$$ (1) where G is the change in the conductance of memristive devices (equivalently,


synaptic weight), and GLinear is the linear change in conductance (determining training accuracy45). The nonlinearity of LTP and LTD characteristics in the bi-layer IGZO/ZnO memristor is


6.77% and 11.49%, respectively, while these are for in a single IGZO memristor is 20.03% and 51.1%, respectively46. When the number of synaptic weight states and total plasticity can be


increased, oxygen vacancies closed to the metal/metal-oxide interface at the bottom electrode in the bi-layer IGZO/ZnO memristor are largely generated, appreciably increasing the effective


Schottky barrier height as discussed in Fig. 7. As shown in Fig. 7, donor defects in ZnO materials that contributed to the n-type conduction are expected to have a more linear conductance


response during LTD (b). The transition mechanism from conductive filamentary switching for a single IGZO and bi-layer IGZO/ZnO memristors is shown in Fig. 8, implying that the mobility


difference between the two materials in the bi-layer structure, as discussed in Fig. 4, is critical. Therefore, the high electron conductivity of the ZnO layer in the bi-layer IGZO/ZnO


memristor plays an important role in charge carriers to be injected easily under a small set voltage and a reset voltage switching behavior to form the conductive path. Not only the


nonlinearity but also the asymmetry ratio is a critical factor in determining the learning accuracy47. The symmetric weight update between LTP and LTD in the memristor devices is defined by,


$$Asymmetry \,\,ratio = \frac{{G_{Potentiation} @ \left( {N_{x} } \right)}}{{G_{Depression} @ \left( {N_{max} - N_{x} } \right)}}$$ (2) where G is the same as Eq. (1), Nmax is total pulse


number between Gmin and Gmax, and Nx is a certain pulse number, indicating asymmetry ratio is “1” in ideal case48. Figure 9 shows the cumulative probability results of asymmetry ratios for


the single IGZO and bi-layer IGZO/ZnO memristors. The average asymmetry ratio for a single IGZO memristor and a bi-layer IGZO/ZnO memristor is 0.877 and 1.20, respectively. The standard


deviations normalized to the mean ratio for two memristors, on the other hand, are 0.213 and 0.031, respectively. As a result, the comprehensive nonlinearity and asymmetry results are shown


in Table 1, indicating that a bi-layer IGZO/ZnO memristor made by inserting a ZnO layer for the HRS layer is more suitable for ideal synaptic devices. It has the potential to be a desirable


option to be used in implementing neural networks in the future. CONCLUSION We investigated the electrical characteristics, RS behavior, and the LTP/LTD of ZnO and IGZO and bi-layer


memristors for high-performance synaptic devices. The set and reset voltages of the bi-layer IGZO/ZnO memristors are 1 V, and − 0.7 V, achieving low operating voltage to realize the


neuromorphic systems. The oxygen vacancies played a role in the modulation of the HRS layer (oxygen-deficient) and the LRS layer (oxygen-rich) region. The VRESET of the bi-layer IGZO/ZnO


memristors was lower than that of a single IGZO, which implied that the oxygen vacancy filaments could be easily ruptured due to the higher oxygen vacancy peak in the HRS layer. When


compared to a single IGZO memristor, the nonlinearity and asymmetry ratios of the LTP and LTD characteristics for the bi-layer IGZO/ZnO memristor improved significantly. These findings were


significant because they revealed the difference in mobility between two materials in a bi-layered structure. As a result, the ZnO layer’s role in the bi-layer IGZO/ZnO memristor was


potentially significant and necessary for charge carriers to be injected easily under a small set voltage and a reset voltage, as well as the switching behavior to form the conductive path.


DATA AVAILABILITY The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. REFERENCES * Hebb, D. O. _The


organization of behavior: a neuropsychological theory_ (Psychology Press, 2005). Google Scholar  * Gerstner, W., Ritz, R. & Van Hemmen, J. L. Why spikes? Hebbian learning and retrieval


of time-resolved excitation patterns. _Biol. Cybern._ 69, 503–515 (1993). CAS  PubMed  MATH  Google Scholar  * Indiveri, G., Chicca, E. & Douglas, R. A VLSI array of low-power spiking


neurons and bistable synapses with spike-timing dependent plasticity. _IEEE Trans. Neural Netw._ 17, 211–221 (2006). PubMed  Google Scholar  * Hahnloser, R. H., Sarpeshkar, R., Mahowald, M.


A., Douglas, R. J. & Seung, H. S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. _Nature_ 405, 947–951 (2000). ADS  CAS  PubMed  Google


Scholar  * Diorio, C., Hasler, P., Minch, A. & Mead, C. A. A single-transistor silicon synapse. _IEEE Trans. Electron Devices_ 43, 1972–1980 (1996). ADS  CAS  Google Scholar  * Yakopcic,


C., Taha, T. M., Subramanyam, G., Pino, R. E. & Rogers, S. A memristor device model. _IEEE Electron Device Lett._ 32, 1436–1438 (2011). ADS  Google Scholar  * Lanza, M. _et al._


Recommended methods to study resistive switching devices. _Adv. Electron. Mater._ 5, 1800143 (2019). Google Scholar  * Beck, A., Bednorz, J., Gerber, C., Rossel, C. & Widmer, D.


Reproducible switching effect in thin oxide films for memory applications. _Appl. Phys. Lett._ 77, 139–141 (2000). ADS  CAS  Google Scholar  * Liu, S., Wu, N. & Ignatiev, A.


Electric-pulse-induced reversible resistance change effect in magnetoresistive films. _Appl. Phys. Lett._ 76, 2749–2751 (2000). ADS  CAS  Google Scholar  * Kim, S. G., Han, J. S., Kim, H.,


Kim, S. Y. & Jang, H. W. Recent advances in memristive materials for artificial synapses. _Adv. Mater. Technol._ 3, 1800457 (2018). Google Scholar  * Mohammad, B. _et al._ State of the


art of metal oxide memristor devices. _Nanotechnol. Rev._ 5, 311–329 (2016). CAS  Google Scholar  * Burr, G. W. _et al._ Neuromorphic computing using non-volatile memory. _Adv. Phys. X_ 2,


89–124 (2017). Google Scholar  * Waser, R. & Aono, M. Nanoionics-based resistive switching memories. _Nat. Mater._ 6, 833–840 (2007). ADS  CAS  PubMed  Google Scholar  * Yoshida, C.,


Tsunoda, K., Noshiro, H. & Sugiyama, Y. High speed resistive switching in Pt∕ TiO2∕ Ti N film for nonvolatile memory application. _Appl. Phys. Lett_ 91, 223510 (2007). ADS  Google


Scholar  * Syu, Y.-E. _et al._ Atomic-level quantized reaction of HfOx memristor. _Appl. Phys. Lett._ 102, 172903 (2013). ADS  Google Scholar  * Wang, H. _et al._ Bio-inspired synthesis of


mesoporous HfO2 nanoframes as reactors for piezotronic polymerization and Suzuki coupling reactions. _Nanoscale_ 11, 5240–5246 (2019). CAS  PubMed  Google Scholar  * Kim, S. _et al._


Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. _Nano Lett._ 15, 2203–2211 (2015). ADS  CAS  PubMed  Google Scholar


  * Li, X. _et al._ Electrode-induced digital-to-analog resistive switching in TaOx-based RRAM devices. _Nanotechnology_ 27, 305201 (2016). PubMed  Google Scholar  * Seo, K. _et al._ Analog


memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. _Nanotechnology_ 22, 254023 (2011). ADS  PubMed  Google Scholar


  * Gao, L. _et al._ Fully parallel write/read in resistive synaptic array for accelerating on-chip learning. _Nanotechnology_ 26, 455204 (2015). PubMed  Google Scholar  * Park, J. _et al._


TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing. _IEEE Electron Device Lett._ 37,


1559–1562 (2016). ADS  CAS  Google Scholar  * Fan, Y.-S., Liu, P.-T. & Hsu, C.-H. Investigation on amorphous InGaZnO based resistive switching memory with low-power, high-speed, high


reliability. _Thin Solid Films_ 549, 54–58 (2013). ADS  CAS  Google Scholar  * Chen, M.-C. _et al._ Influence of electrode material on the resistive memory switching property of indium


gallium zinc oxide thin films. _Appl. Phys. Lett._ 96, 262110 (2010). ADS  Google Scholar  * Kimizuka, N. & Yamazaki, S. _Physics and technology of crystalline oxide semiconductor


CAAC-IGZO: fundamentals_ (John Wiley & Sons, 2016). Google Scholar  * Kim, M.-S. _et al._ Effects of the oxygen vacancy concentration in InGaZnO-based resistance random access memory.


_Appl. Phys. Lett._ 101, 243503 (2012). ADS  Google Scholar  * Kim, S. _et al._ Engineering synaptic characteristics of TaOx/HfO2 bi-layered resistive switching device. _Nanotechnology_ 29,


415204 (2018). ADS  PubMed  Google Scholar  * Wang, J., Zhuge, X. & Zhuge, F. Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence.


_Sci. Technol. Adv. Mater._ 22, 326–344 (2021). PubMed  PubMed Central  Google Scholar  * Li, J. _et al._ Tuning analog resistive switching and plasticity in bilayer transition metal oxide


based memristive synapses. _RSC Adv._ 7, 43132–43140 (2017). ADS  CAS  Google Scholar  * Park, S.,_ et al_. Neuromorphic speech systems using advanced ReRAM-based synapse. _IEEE


International Electron Devices Meeting_ (IEEE, 2013) * Romero, L. P. _et al._ Training fully connected networks with resistive memories: impact of device failures. _Faraday Discuss._ 213,


371–391 (2019). ADS  CAS  PubMed  Google Scholar  * Thakur, C. S. _et al._ Large-scale neuromorphic spiking array processors: a quest to mimic the brain. _Front. Neurosci._ 12, 891 (2018).


PubMed  PubMed Central  Google Scholar  * Yu, S., _et al._ Scaling-up resistive synaptic arrays for neuro-inspired architecture: challenges and prospect. _IEEE International Electron Devices


Meeting (IEDM)_ (IEEE, 2015) * Hsu, C.-M., Tzou, W.-C., Yang, C.-F. & Liou, Y.-J. Investigation of the high mobility IGZO thin films by using co-sputtering method. _Materials_ 8,


2769–2781 (2015). ADS  CAS  PubMed Central  Google Scholar  * Zeng, Y. _et al._ Study on the Hall-effect and photoluminescence of N-doped p-type ZnO thin films. _Mater. Lett._ 61, 41–44


(2007). CAS  Google Scholar  * Zan, H. W., Tsai, W. W., Chen, C. H. & Tsai, C. C. Effective mobility enhancement by using nanometer dot doping in amorphous IGZO thin-film transistors.


_Adv. Mater._ 23, 4237–4242 (2011). CAS  PubMed  Google Scholar  * Chai, Z., Liu, Y., Lu, X. & He, D. Reducing adhesion force by means of atomic layer deposition of ZnO films with


nanoscale surface roughness. _ACS Appl. Mater. Interfaces._ 6, 3325–3330 (2014). CAS  PubMed  Google Scholar  * Lim, E. W. & Ismail, R. Conduction mechanism of valence change resistive


switching memory: a survey. _Electronics_ 4, 586–613 (2015). CAS  Google Scholar  * Mondal, S., Chueh, C.-H. & Pan, T.-M. Current conduction and resistive switching characteristics of


Sm2O3 and Lu2O3 thin films for low-power flexible memory applications. _J. Appl. Phys._ 115, 014501 (2014). ADS  Google Scholar  * Yu, L.-E. _et al._ Structure effects on resistive switching


al/TiOx/al devices for RRAM applications. _IEEE Electron Dev. Lett._ 29, 331–333 (2008). ADS  CAS  Google Scholar  * Liu, Q. _et al._ Resistive switching memory effect of Zr O2 films with


Zr+ implanted. _Appl. Phys. Lett._ 92, 012117 (2008). ADS  Google Scholar  * Peng, H. & Wu, T. Nonvolatile resistive switching in spinel ZnMn2O4 and ilmenite ZnMnO3. _Appl. Phys. Lett._


95, 152106 (2009). ADS  Google Scholar  * Lee, H. _et al._ Low-power and nanosecond switching in robust hafnium oxide resistive memory with a thin Ti cap. _IEEE Electron Device Lett._ 31,


44–46 (2009). ADS  Google Scholar  * Chen, C., Yang, Y., Zeng, F. & Pan, F. Bipolar resistive switching in Cu/AlN/Pt nonvolatile memory device. _Appl. Phys. Lett._ 97, 083502 (2010). ADS


  Google Scholar  * Ismail, M. _et al._ Forming-free bipolar resistive switching in nonstoichiometric ceria films. _Nanoscale Res. Lett._ 9, 45. https://doi.org/10.1186/1556-276X-9-45


(2014). Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Wang, I. T., Chang, C. C., Chiu, L. W., Chou, T. & Hou, T. H. 3D Ta/TaOx /TiO2/Ti synaptic array and linearity tuning


of weight update for hardware neural network applications. _Nanotechnology_ 27, 365204. https://doi.org/10.1088/0957-4484/27/36/365204 (2016). Article  CAS  PubMed  Google Scholar  * Bae,


J. H., Lim, S., Park, B. G. & Lee, J. H. High-density and near-linear synaptic device based on a reconfigurable gated schottky diode. _IEEE Electron Device Lett._ 38, 1153–1156.


https://doi.org/10.1109/Led.2017.2713460 (2017). Article  ADS  CAS  Google Scholar  * Ielmini, D. Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and


neural networks. _Microelectron. Eng_ 190, 44–53. https://doi.org/10.1016/j.mee.2018.01.009 (2018). Article  CAS  Google Scholar  * Yu, S. _Neuro-inspired computing using resistive synaptic


devices_ (Springer, 2017). Google Scholar  * Min, S.-Y. & Cho, W.-J. High-performance resistive switching in solution-derived IGZO: N memristors by microwave-assisted nitridation.


_Nanomaterials_ 11, 1081 (2021). CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS This work supported by the National Research Foundation of Korea (NRF)


grant funded by the Korea government (MSIT) (NRF - 2019M3F3A1A01074449). It was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of


Korea (NRF - 2019S1A5C2A03081332). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon,


Republic of Korea Hyun-Woong Choi, Ki-Woo Song, Seong-Hyun Kim, Kim Thanh Nguyen, Sunil Babu Eadi & Hi-Deok Lee * Department of Semiconductor Processing Equipment, Semiconductor


Convergence Campus of Korea Polytechnic College, 41-12, Songwon-Gil, Kongdo-Eup, Anseong, Kyunggi-Do, Republic of Korea Hyuk-Min Kwon Authors * Hyun-Woong Choi View author publications You


can also search for this author inPubMed Google Scholar * Ki-Woo Song View author publications You can also search for this author inPubMed Google Scholar * Seong-Hyun Kim View author


publications You can also search for this author inPubMed Google Scholar * Kim Thanh Nguyen View author publications You can also search for this author inPubMed Google Scholar * Sunil Babu


Eadi View author publications You can also search for this author inPubMed Google Scholar * Hyuk-Min Kwon View author publications You can also search for this author inPubMed Google Scholar


* Hi-Deok Lee View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS H.W.C. conducted most of the experiments and wrote the manuscript, including


preparing figures and electrical characterization; H.W.C., K.W.S., S.H.K., K.T.N. and S.B.E. prepared the original draft of the manuscript; H.M.K. and H.D.L. initiated the work, provided


the main idea, supervised the entire process, and reviewed the manuscript; H.D.L. supported the funding acquisition; All authors analyzed and discussed the results. All authors have read and


agreed to the published version of the manuscript. CORRESPONDING AUTHORS Correspondence to Hyuk-Min Kwon or Hi-Deok Lee. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no


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ARTICLE Choi, HW., Song, KW., Kim, SH. _et al._ Zinc oxide and indium-gallium-zinc-oxide bi-layer synaptic device with highly linear long-term potentiation and depression characteristics.


_Sci Rep_ 12, 1259 (2022). https://doi.org/10.1038/s41598-022-05150-w Download citation * Received: 22 August 2021 * Accepted: 04 January 2022 * Published: 24 January 2022 * DOI:


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