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  • 1
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    Online Resource
    The Electrochemical Society ; 2020
    In:  ECS Meeting Abstracts Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2066-2066
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2066-2066
    Abstract: Recently, memristive devices (e.g. CBRAM and ReRAM etc.) have been proposed as an alternative to C-MOSFET based neuromorphic device (i.e. neuron and synapse) to achieve a high neuronal density and low power consumption. Among memristive devices, the conductive bridge neuron has been a great attention due to its area efficiency, low power consumption ( 〈 500 fJ), and good C-MOS compatibility 1-3 . However, past researches have mainly focused on stochastic nature of conductive-bridge neurons. In particular, most of the stochastic conductive-bridge neuron have used capacitors to implement membrane potential, which requires a large integration area (~1000F 2 ). In this works, integrate property of Al 2 O 3 -based cap-less conductive-bridge neuron was demonstrated which allows a cost-efficient neuromorphic chip by using a same process technology as a memristor synapse, as shown in Fig 1 (a). Note that the Al 2 O 3 -based cap-less conductive-bridge neuron was the same as that of the Al 2 O 3 -based memristor synapse. In addition, we investigated the effect of negative-differential-resistance (NDR) characteristic on integrate property for our proposed neuron, depending on the compliance current level (i.e. a shape of metallic filaments in neuron device). The NDR slope was adjusted by varying a compliance current of a Al 2 O 3 -based cap-less conductive-bridge neuron. The current compliance (I cc ) of 1 mA showed the NDR slope of ~ 0.56 decade/ V , as shown in Fig. 1(b). For all spike amplitude (i.e. -0.6~-1.0 V ), the resistances of neuron device abruptly increased at a certain number of spikes, depending on the spike amplitude, and then it gradually increased with the number of spikes, as shown in Fig. 1(c). In addition, the spike number being necessary for achieving ~600 Ω decreased exponentially with increasing the number of spikes, as showed integrate property depending on input spike amplitude, as shown in Fig. 1(d). Otherwise, the current compliance (I cc ) of 0.1 mA, which was 10 times less than Fig 1 (b) presented the NDR slope of ~ 0.23 decade/ V , which was 2 times less than Fig. 1(b), as shown in Fig. 1(e). For all spikes amplitude, the resistance of neuron devices gradually increased with the number of spikes, wherein the resistance of neuron devices increased with the spike amplitude (i.e. negative voltage pulse amplitude), as shown in Fig. 1(f). The spike number being necessary for achieving ~10 kΩ decreased exponentially with increasing the number of spikes, as shown in Fig. 1(g). Comparing Fig. 1(d) with Fig. 1(g), the variation of the spike number being necessary for achieving ~600 Ω for the NDR slope of ~-0.56 decade/ V was much less that for the spike number being necessary for achieving ~10 kΩ for the NDR slope of ~-0.23 decade/ V . This result indicates that, for the Al 2 O 3 -based cap-less conductive-bridge neuron, the NDR slope dominantly determines the integrate nature. In our presentation, we will discuss the mechanism why the integrate property depended on the NDR slope via understanding a shape of conductive-metallic-filaments in Al 2 O 3 layer, which determine an integrate property of neuron devices. In particular, it will be reported that the shape of conductive-metallic-filaments strongly depended on the metal vacancy concentration in binary oxide (i.e. Al 2 O 3 ) layer. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2016M3A7B4910249). Reference "IEEE Transactions on Emerging Topics in Computational Intelligence," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 5, pp. C2-C2, Oct. 2018, doi: 10.1109/TETCI.2018.2867377. Jang, B. Attarimashalkoubeh, A. Prakash, H. Hwang and Y. Jeong, "Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions," in IEEE Transactions on Electron Devices, vol. 63, no. 6, pp. 2610-2613, June 2016, doi: 10.1109/TED.2016.2549359. Palma, M. Suri, D. Querlioz, E. Vianello and B. De Salvo, "Stochastic neuron design using conductive bridge RAM," 2013 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), Brooklyn, NY, 2013, pp. 95-100, doi: 10.1109/NanoArch.2013.6623051. Figure 1
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    ISSN: 2151-2043
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    Publication Date: 2020
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  • 2
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    Online Resource
    The Electrochemical Society ; 2020
    In:  ECS Meeting Abstracts Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2044-2044
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2044-2044
    Abstract: Abstract Recently, the demand for more data storage and fast processing has been dramatically increased for the big data markets such as artificial intelligence (AI), virtual reality (VR), autonomous car, and internet of things (IoT). Thus, a new memory such as a storage class memory (SCM) has been introduced since it can perform a reasonable latency compared to DRAM and a lower bit-cost than NAND flash memory [1]. Remind that, generally, a SCM has been fabricated with three-dimensional cross point memory cell array [2] . As a candidate memory cell for SCM, resistive-random-access-memory (ReRAM) has been proposed; i.e., called storage-type SCM. Santini, C. A. et al. demonstrated amorphous carbon oxide (a-CO x ) based ReRAM-cell having a reasonable memory window margin ( I on / I off 〉 100), a fast switching speed of 20~50 ns at ~ 100-nm-diameter memory cell size. However, it showed extremely a high forming voltage ( V forming ) of ~ 5.0 V and a high reset voltage ( V reset ) of ~ – 4.0 V [3]. In particular, the a-CO x based ReRAM-cell presented a different bi-stable memory characteristic for another typical ReRAM-cells; i.e., its bias directions of set and reset were opposite to a typical ReRAM, which mechanism was not evidently proved. In addition, the forming process for this ReRAM cell would be highly undesirable since it caused an extra burden for initializing memory-cells and degraded the write and erase endurance cycles [4]. Here, for the first time, we designed a forming-free Cu-doped amorphous-carbon-oxide based ReRAM cell, which did not need a forming process and we reviewed the memory operation mechanism by understanding electrical and chemical properties of the Cu-doped a-CO x based ReRAM cells. A typical a-CO x based ReRAM-cell needed a forming process; i.e., a forming voltage of – 2.20 V and a set voltage of – 1.05 V , as shown in Fig. 1 (a). Otherwise, a Cu-doped a-CO x based ReRAM-cell could achieve a forming-free process; i.e., a forming voltage (i.e. - 0.85 V ) was the same as a set voltage (i.e. – 0.85 V ), as shown in Fig. 1 (b). In addition, it demonstrated a write and erase endurance cycles of ~10 6 by sustaining a memory margin of ~1.3×10 2 , being able to be utilized for a commercial nonvolatile memory-cell, as shown in Fig. 1(c). To clarify the forming-free mechanism, the depth profiles of C, Cu, and O atom in the Cu-doped a-CO x memory cell were observed in detail under pristine, after set, and after reset process, which were obtained from intensity line-profiles of EELS/EDS elemental mapping images at C-K edges, O-K edges, Cu-Kα, Pt-La1, and W-La1. For the pristine state, C, Cu, and O atoms are uniformly distributed in the Cu-doped a-CO x layer, as shown in Fig. 1(d). In addition, after a set process, since a negative voltage was applied to the top Pt electrode, Cu atoms evidently moved and segregated toward the top Pt electrode, as shown in a of Fig. 1(e), while O atoms evidently migrated and pile up toward the bottom W electrode, as shown in b of Fig. 1(e). This result means that the conductive C-C sp 2 filaments in the Cu-doped a-CO x layer were produced when oxygen atoms migrated and piled up toward bottom W electrode and the conductive Cu-atom filaments were formed in the Cu-doped a-CO x layer since Cu atom moved and segregated toward the top Pt electrode. Hence, both conductive C-C sp 2 filaments and Cu-atom filaments were generated simultaneously in the Cu-doped a-CO x layer, achieving a set process without a forming process. On the other hand, after a reset process, since a positive voltage was applied to the top Pt electrode, Cu and O atoms were redistributed inside the Cu-doped a-CO x , resulting in breaking both C-C sp 2 filaments and Cu-atom filaments, as shown in a and b of Fig. 1(f). In our presentation, we will demonstrate and review the mechanisms between a set process without forming process and a reset process in detail by electrical and chemical composition depth profiles depending on the applied bias condition. In particular, we will demonstrate a different ReRAM behavior of the Cu-doped a-CO x based ReRAM from a typical ReRAM or CBRAM. Acknowledgement This material is based upon work supported by the Ministry of Trade, Industry & Energy(MOTIE, Korea) under Industrial Technology Innovation Program (10068055). Reference [1] Matsui, C. et al. Integration 2019, 69, 62-74. [2] Hady, F. T. et al. Proceedings of the IEEE 2017, 105, (9), 1822-1833. [3] Santini, C. A. et al. Nature Communications 2015, 6, (1), 8600. [4] Skaja, K. et al. Scientific Reports 2018, 8. Figure 1
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    ISSN: 2151-2043
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    Publication Date: 2020
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  • 3
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    The Electrochemical Society ; 2017
    In:  ECS Journal of Solid State Science and Technology Vol. 6, No. 9 ( 2017), p. N148-N154
    In: ECS Journal of Solid State Science and Technology, The Electrochemical Society, Vol. 6, No. 9 ( 2017), p. N148-N154
    Type of Medium: Online Resource
    ISSN: 2162-8769 , 2162-8777
    Language: English
    Publisher: The Electrochemical Society
    Publication Date: 2017
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  • 4
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    The Electrochemical Society ; 2020
    In:  ECS Meeting Abstracts Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2043-2043
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2043-2043
    Abstract: With the expanding use of artificial intelligence (AI), a higher integration density and lower power consumption is required of the neuromorphic devices. In particular, 2-terminal perpendicular spin-transfer-torque (p-STT) neuron has been reported as a candidate of spin neuron. [1-2] However, p-STT devices cannot reliably operate at ns and sub-ns scale because of large incubation delays. [3] In addition, the shared read/write path compromise the read reliability where the write operation can impose severe stress to the MgO tunneling barrier, leading to a possible time dependent degradation of the p-MTJ spin-valve. As a solution to these issues, Spin-Orbit-Torque (SOT) based MRAM was proposed where the read/write path is separated. The crystallinity of the W film used for SOT channel ex-situ annealed at 350 o C was investigated depending on the W thickness from 4 to 10 nm, as shown in Figs. 1(a) and (b), where the transition from β (A15 crystal structure) to α-phase (b.c.c.) occurs between thickness of 5-nm and 6-nm. Also, the resistivity of the W film was greatly reduced from 150 to 40 Ω cm at this thickness which further shows the transition from β to α-phase, as shown in Fig. 1(c). The the b.c.c. (110) crystallinity can be confirmed in the 6-nm thick W, as shown in Fig. 1(d). In our sample, β-phase tungsten was confirmed below 6-nm at ex-situ annealing temperature of 350 o C which is known to have the highest SOT efficiency ( 〉 0.3) [4]. The p-SOT neuron was patterned into a hall-bar to confirm the SOT efficiency depending on the crystal structure of the W film, as shown in Fig. 1(e). The hysteresis loop showed perpendicular magnetic anisotropy for both 4 and 6-nm W, but the change of the Hall resistance was negligible in the case of 6-nm W thick p-SOT neuron, as shown in Fig. 1(f). The normalized Hall resistance was measured as a function of nearly in-plane magnetic field (β=4 o : angle between external field and injected current) under a positive or negative current (±1 mA) to calulate the spin-torque per magnetic moment (τST), as shown in Figs. 1(i)-(k), where the slope of [B + (θ)-B - (θ)] is τ ST . The τST increases with applied current for both α and β-phase W, but the spin torque of α-phase is about 1/3 of the β-phase W. In addition, the spin hall angle of the β-phase W (-0.288) was about 6 times larger than that of the α-phase W (-0.045), as shown in Fig. 1(l). In our presentation, we will review in detail the integrate nature of the p-SOT neuron depending on the spike amplitude and crystal structure of the W. Acknowledgement *This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2016M3A7B4910249) and the Brain Korea 21 PLUS Program in 2014. Reference [1] Kondo, K., Choi, J ., Baek, J., and Jun, H. A two-terminal perpendicular spin-transfer torque based artificial neuron. J. Phys. D Appl. Phys . 51:504002. (2018) [2] Dong Won Kim, et al . Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron. Front. Neurosci . 14:309 (2020) [3] Ya-Jui Tsou, Jih-Chao Chiu, Huan-Chi Shih, Chee Wee Liu, Write Margin Analysis of Spin–Orbit Torque Switching Using Field-Assisted Method, Exploratory Solid-State Computational Devices and Circuits IEEE Journal on , vol. 5, no. 2, pp. 173-181, 2019. [4] Qiang Hao and Gang Xiao, Giant Spin Hall Effect and Switching Induced by Spin-Transfer Torque in a W/Co 40 Fe 40 B 20 /MgO Structure with Perpendicular Magnetic Anisotropy, PHYS. REV. APPLIED 3, 034009 (2015) Figure 1
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    ISSN: 2151-2043
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    Publication Date: 2020
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  • 5
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2058-2058
    Abstract: Recently, a neuromorphic-chip performing artificial intelligence (AI) has been intensively for the application field of pattern recognition, autonomous car, etc. Biological neurons, which is able to be described hardware-wise by a cross-point synapse array being connected with input and output neurons like a vector multiplying arithmetic operation. The conventional neuromorphic-chip had been developed by a cross-point synapse array using C-MOSFET integrated circuit, where a synapse was realized by SRAMs while a neuron was achieved by an integration of C-MOSFETs and capacitors [1] . Thus, it presented a detrimental fault such as a large synapse and neuron size. As a solution, a cross-point synapse array using memristors has been researched popularly[2, 3]. However, it produced a sneak current during potentiating or depressing a selected memristor-synapse. To eliminate a sneak current in a memristor-based-synapse array, a n-MOSFET being connected with a synapse cell has proposed as a selector, but it has presented three terminals operating simultaneously a synapse-cell and a selector. Hence, recently, it has been intensively researched that a selector (S) is stacked vertically with a memristor-cell (M), called 1S1M-based synapse array, as shown in Fig. 1(a). In our study, a synapse array was fabricated with the HfO 2 based memristor-cells being vertically stacked with super-linear threshold selectors, as shown in Fig. 1(b). The HfO 2 based memristor showed 3-bit resistances for potentiation and depression, being adjusted by a reset voltage in the negative-resistance-region (NDR) of the memristor synapse, as shown in Fig. 1 (c). The potentiation and depression nature such as linearity and symmetry was estimated with the spike width of 100 μs and input spike number of 100, showing a good linearity (i.e., 3.27 for potentiation and -6.14 for depression) and symmetry nature (i.e., 0.12 ), as shown in Fig. 1(d). The memristor-synapse nature of 1S1M presented 3-bit resistances for potentiation and depression, the dead region between ~-0.40 and ~+0.42 V, the switching-on threshold voltages of ~-0.40 for negative applied bias and ~+0.42 V for positive applied bias, the set voltage of 0.92 V, and the reset voltage of -1.05 V, as shown in Fig. 1(e). This result well demonstrated a cross-point memristor-synapse array being able to operate a half-bias or 1/3-bias scheme writing. In addition, the effect of our proposed neural network on the pattern recognition accuracy was estimated by simulation. The cross-point neural network without a super-linear selector (i.e., 1M array), as shown in Fig. 1(f), was compared by that with a super-linear selector (i.e., 1S1M array), as shown in Fig. 1(g). Our proposed neuron was designed with a HfO2 based neuron having an integrate nature and a sense amplifier using 7 n-MOSFETs and 3 p-MOSFETs, as shown in Fig. 1(h). The neural network fabricated with 1S1M array and HfO2 based neurons showed ~10 % than that fabricated with 1M array and HfO2 based neurons, as shown in Fig. 1(i). This result indicates that the implementation of a super-linear threshold selector being vertically stacked on a memristor-synapse would be essentially necessary for a high accurate AI application. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2016M3A7B4910249). This material is based upon work supported by the Ministry of Trade, Industry & Energy(MOTIE, Korea) under Industrial Technology Innovation Program (10068055). Reference [1] Merolla, Paul A., et al. "A million spiking-neuron integrated circuit with a scalable communication network and interface." Science 345.6197 (2014): 668-673. [2] Wu, Chaoxing, et al. "Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability." Nature communications 8.1 (2017): 1-9. [3] Moon, K., et al. "RRAM-based synapse devices for neuromorphic systems." Faraday discussions 213 (2019): 421-451. Figure 1
    Type of Medium: Online Resource
    ISSN: 2151-2043
    Language: Unknown
    Publisher: The Electrochemical Society
    Publication Date: 2020
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  • 6
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2041-2041
    Abstract: The application fields of artificial intelligence (AI) has been widely expanded; security, finance, health care, etc. A conventional neuromorphic chips for AI has been fabricated with synapse array and neuron integration using C-MOSFET technology, having a scaling-down limit of neuromorphic chips because of a large neuron size and producing a high power consumption. In particular, as an alternative solution for scaling-down neuron, 2-terminal perpendicular-spin-transfer-torque (p-STT) neuron has been proposed [1], [2] . In this neuron, the face –centered-cubic (f.c.c) crystallinity of a MgO tunneling barrier is an extremely critical parameter to determine the integrate characteristic of a neuron. In our study, we investigated how the f.c.c crystallinity of MgO tunneling barrier influenced the resistance sensing margin for integrate characteristic in 2-terminal p-STT neuron. The f.c.c crystallinity of MgO tunneling barrier strongly depended on the RF sputtering power to sputter 1.15-nm-thick MgO tunneling barrier, as shown in Fig.1(a). The resistance difference between anti-parallel (AP) state and parallel (P) state increased with the RF sputtering power as shown in Fig.1(b). The better sequence of the f.c.c crystallinity of the MgO tunneling barrier was followed by 320, 350, 290, and 260 Watt, as shown in Fig. 1(c). The dependency of the f.c.c crystalliny of the MgO tunneling barrier RF sputtering power will be explained by crystalline defect distribution using spectroscopy ellipsometer. In addition, we examined the f.c.c crystallnitiy of the MgO tunneling barrier influence on the resistance sensing margin for integrate characteristic of neuron, as shown in Figs.1(d)-(g). Note that, in our experiment, the integrate characteristics of p-STT neuron was measured after a partially reset. The resistance sensing margin clearly increased with the f.c.c crystallinity of the MgO tunneling barrier in p-STT neuron and evidently increased with the input spike amplitude. In particular, an assure clarification of a resistance margin depending on the input spike amplitude could be achieved above the f.c.c crystallinity of the MgO tunneling barrier sputtered at 320 and 350 Watt. In out presentation, we will demonstrate the mechanism why the integrate characteristic of the neuron mainly depended on the f.c.c crystallinity of the MgO tunneling barrier by reviewing p-STT switching at a MgO tunneling barrier having a nanoscale-diameter MgO grain which affects the activation energy barrier for p-STT switching. Reference [1] Kondo, K., Choi, J., Baek, J., and Jun, H. (2018). A two-terminal perpendicular spin-transfer torque based artificial neuron. J. Phys. D Appl. Phys. 51:504002. [2] Dong Won Kim, Woo Seok Yi, Jun Young Choi, Kei Ashiba, Jong Ung Baek, Han Sol Jun, Jae Joon Kim and Jea Gun Park (2020) Acknowledgement This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2016M3A7B4910249)and the Brain Korea 21 PLUS Program in 2014. . Figure 1
    Type of Medium: Online Resource
    ISSN: 2151-2043
    Language: Unknown
    Publisher: The Electrochemical Society
    Publication Date: 2020
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  • 7
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    Online Resource
    The Electrochemical Society ; 2020
    In:  ECS Meeting Abstracts Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2042-2042
    In: ECS Meeting Abstracts, The Electrochemical Society, Vol. MA2020-02, No. 31 ( 2020-11-23), p. 2042-2042
    Abstract: Achieving a higher integration density and a lower power consumption for neuromorphic devices, recently, spin neuron has been intensively researched. [ref . 1-3] In particular, 2-terminal perpendicular spin-transfer-torque (p-STT) neuron has been reported as a candidate of spin neuron. [ref 4-5] This neuron showed both stochastic nature as well as integrate nature, which is determined by reset voltage and spike amplitude (i.e. set voltage). However, the mechanisms between two different neuron nature has not been clearly proved. Here, two different mechanisms were reviewed by observing the dependency of neuron nature on reset voltage and spike amplitude. 2-terminal p-STT neuron, as shown in Fig. 1(a), was operated by two different reset voltage and spike amplitude (i.e. fully reset or partially reset) dislike p-STT MRAM, as shown in Fig. 1(b). Two different reset voltage and spike amplitude would have a different energy distribution from parallel state to anti-parallel state for integrate or stochastic nature; i.e. the energy level of parallel state for partially reset was higher than that for fully reset and the activation energy (i.e. parallel to anti-parallel state switching) for partially reset was lower than that for fully reset, as shown in Fig. 1(c). For fully reset case, the probability of switching from parallel to anti-parallel increased with the spike amplitude (i.e. negative bias magnitude) being able to produce a sigmoid function for stochastic neuron, as shown in Figs. 1(d)-(h). Otherwise, for partially reset case, the resistance margin between parallel and anti-parallel state was enhanced with the spike amplitude, being able to reduce a read error rate in fire circuit, as shown in Figs. 1(i)-(m). In addition, in our presentation, we will review in detail to different mechanism between integrate and stochastic nature by the grain size distribution of the MgO tunneling barrier in p-STT neurons. Particularly, it was found that a criteria between integrate and stochastic nature in p-STT neurons could be decided by the reset voltage amplitude; i.e. the ratio of stochastic (i.e. fully reset) to integrate (i.e. partially reset) decreased with increasing the reset voltage amplitude. ACKNOWLEDGEMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2016M3A7B4910249)and the Brain Korea 21 PLUS Program in 2014. REFERENCE [1] Sengupta, Abhronil, and Kaushik Roy. "Spin-transfer torque magnetic neuron for low power neuromorphic computing." 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. [2] Krzysteczko, Patryk, et al. "The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System." Advanced Materials 24.6 (2012): 762-766. [3] Borders, William A., et al. "Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation." Applied physics express 10.1 (2016): 013007. [4] Kondo, Kei, et al. "A two-terminal perpendicular spin-transfer torque based artificial neuron." Journal of Physics D: Applied Physics 51.50 (2018): 504002. [5] Kim, Dong Won, et al. "Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron." Frontiers in Neuroscience 14 (2020). Figure 1
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    ISSN: 2151-2043
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  • 8
    In: Nano Energy, Elsevier BV, Vol. 12 ( 2015-03), p. 410-418
    Type of Medium: Online Resource
    ISSN: 2211-2855
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 2648700-7
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  • 9
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    Springer Science and Business Media LLC ; 2008
    In:  MRS Proceedings Vol. 1071 ( 2008)
    In: MRS Proceedings, Springer Science and Business Media LLC, Vol. 1071 ( 2008)
    Abstract: Recently, organic nonvolatile memory has attracted much interest as a candidate device for next generation nonvolatile memory because of its simple process, small device area, and high speed. To investigate electrical characteristics of small molecular organic nonvolatile memory with Ni as a middle metal layer, we developed a small molecular organic nonvolatile memory with the device structure of Aluminum tris (8-hydroxyquinolate) (Al/Alq 3 ), Ni nanocrystals, and Alq 3 /Al. A high vacuum thermal deposition method was used for the device fabrication. It is critical that the fabrication process condition for Ni nanocrystals be optimized, including ∼100 Å thickness, 0.1 Å/sec-evaporation rate, and in-situ plasma oxidation for effective oxidation. The reasons we chose Ni for the middle metal layer are that Ni has a smaller grain boundary, which is beneficial for scaling down and has a larger work function (∼5.15 eV) that can make a deep quantum well in an energy band diagram, compared with that of Al. Our device showed an electrical nonvolatile memory behavior including V th of ∼2 V, V w (write) of ∼3.5 V, negative differential region (NDR) of 3.5∼7 V, V e (erase) of 8 V, and symmetrical electrical behavior at reverse bias. In addition, an interesting behavior of electrical properties was that, although retention and endurance characteristics were similar to the Al device, the I on /I off ratio was greater than 10 4 at V r (read) of 1 V. This value of the Ni device was higher than 10 2 compared to that of the Al device. Also, small molecular organic nonvolatile memory with a Ni middle layer with α-NPD at same fabrication condition showed more unstable characteristics than Alq 3 . We can speculate that there is a relationship in fabrication condition between the middle metal material and the organic material. Finally, we conclude that our device with a Ni nanocrystals middle layer is more reliable and useful for small molecular organic nonvolatile memory.
    Type of Medium: Online Resource
    ISSN: 0272-9172 , 1946-4274
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2008
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  • 10
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    Wiley ; 2011
    In:  Advanced Materials Vol. 23, No. 36 ( 2011-09-22), p. 4183-4187
    In: Advanced Materials, Wiley, Vol. 23, No. 36 ( 2011-09-22), p. 4183-4187
    Type of Medium: Online Resource
    ISSN: 0935-9648
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2011
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