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Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse

Jiajuan Shi, Ya Lin, Tao Zeng, Zhongqiang Wang, Xiaoning Zhao, Haiyang Xu and Yichun Liu

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 Corresponding author: Ya Lin, Email: liny474@nenu.edu.cn; Zhongqiang Wang, wangzq752@nenu.edu.cn

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Abstract:

The development of electronic devices that possess the functionality of biological synapses is a crucial step towards neuromorphic computing. In this work, we present a WOx-based memristive device that can emulate voltage-dependent synaptic plasticity. By adjusting the amplitude of the applied voltage, we were able to reproduce short-term plasticity (STP) and the transition from STP to long-term potentiation. The stimulation with high intensity induced long-term enhancement of conductance without any decay process, thus representing a permanent memory behavior. Moreover, the image Boolean operations (including intersection, subtraction, and union) were also demonstrated in the memristive synapse array based on the above voltage-dependent plasticity. The experimental achievements of this study provide a new insight into the successful mimicry of essential characteristics of synaptic behaviors.

Key words: memristorartificial synapseshort-term plasticitylong-term potentiationimage Boolean operations



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Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577(7792), 641 doi: 10.1038/s41586-020-1942-4
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Wang Z, Joshi S, Savel’ev S, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16(1), 101 doi: 10.1038/nmat4756
[4]
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Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10(4), 1297 doi: 10.1021/nl904092h
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Serrano-Gotarredona T, Masquelier T, Prodromakis T, et al. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci, 2013, 7(2), 2 doi: 10.3389/fnins.2013.00002
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Zucker R S, Regehr W G. Short-term synaptic plasticity. Annu Rev Physiol, 2002, 64(1), 355 doi: 10.1146/annurev.physiol.64.092501.114547
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Kim M K, Lee J S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano, 2018, 12(2), 1680 doi: 10.1021/acsnano.7b08331
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Wang Z, Zeng T, Ren Y et al. Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices. Nat Commun, 2020, 11(1), 1510 doi: 10.1038/s41467-020-15158-3
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Yang J T, Ge C, Du J Y, et al. Artificial synapses emulated by an electrolyte-gated tungsten-oxide transistor. Adv Mater, 2018, 30(34), 1801548 doi: 10.1002/adma.201801548
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Lin Y, Wang C, Ren Y, et al. Analog –digital hybrid memristive devices for image pattern recognition with tunable learning accuracy and speed. Small Methods, 2019, 3(10), 1900160 doi: 10.1002/smtd.201900160
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Kamiya R, Zucher R S. Residual Ca2 + and short-term synaptic plasticity. Nature, 1994, 371, 603 doi: 10.1038/371603a0
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Wang T Y, Meng J L, He Z Y, et al. Room-temperature developed flexible biomemristor with ultralow switching voltage for array learning. Nanoscale, 2020, 12(16), 9116 doi: 10.1039/D0NR00919A
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Waser R, Aono M. Nanoionics-based resistive switching memories. Nanosci Technol, 2009, 158 doi: 10.1142/9789814287005_0016
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Wang W, Xu J, Ma H, et al. Insertion of nanoscale AgInSbTe Layer between the Ag electrode and the CH3NH3PbI3 electrolyte layer enabling enhanced multilevel memory. ACS Appl Nano Mater, 2019, 2(1), 307 doi: 10.1021/acsanm.8b01928
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Atkinson R C, Shiffrin R M. Human memory: A proposed system and its control processes. Psychol Learn Motiv, 1968, 2, 89 doi: 10.1016/S0079-7421(08)60422-3
[26]
Harary F, Wilcox G W. Boolean operations on graphs. Math Scand, 1967, 20(1), 41 doi: 10.7146/math.scand.a-10817
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Satoh T, Chiyokura H. Boolean operations on sets using surface data. Proceedings of the First ACM Symposium on Solid Modeling Foundations and CAD/CAM Applications, 1991, 119
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Mäntylä M. Boolean operations of 2-manifolds through vertex neighborhood classification. ACM Trans Graph, 1986, 5(1), 1 doi: 10.1145/7529.7530
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Gardan Y, Perrin E. An algorithm reducing 3D Boolean operations to a 2D problem: concepts and results. Comput Aid Des, 1996, 28(4), 277 doi: 10.1016/0010-4485(95)00050-X
Fig. 1.  (Color online) The structure and characterization of the Au/WOx/Ti memristive device. (a) Schematic illustration of the biological synapse connecting pre-synaptic and post-synaptic neurons. (b) Schematic illustration of the device, including Au top electrodes, WOx films and Ti bottom electrodes. (c) An overhead view of the device obtained by an optical microscope. (d) A cross-sectional SEM image of the device.

Fig. 2.  (Color online) Demonstration of spike-intensity-dependent synaptic plasticity in a Au/WOx/Ti memristive device. (a, b) IV characteristics of the device obtained under positive/negative bias; the voltage sweep range was 0 to 2 V (−2 V) then back to 0 V. (c) Schematic diagram of PPF measurement. (d) The variation of PPF according to relative spike timing.

Fig. 3.  (Color online) The transition from STP to LTP by adjusting spike intensity. (a) The device received input stimuli with different features, including a spike train with an amplitude of 0.2 V, weak stimuli with an amplitude of 1.5 V, and strong stimuli with an amplitude of 3 V. (b) Memorization of the image “T” to demonstrate the transition from STM to LTM. Case 1: the conductance of the memristive array before stimulation, after 1.5-V stimulation, and after stimulation for 60 s; Case 2: the conductance of the memristive array before stimulation, after 3-V stimulation, and after stimulation for 60 s. The different color levels represent different magnitude conductance values.

Fig. 4.  (Color online) Demonstration of image Boolean intersection operation in the memristive synapse array. (a) The stimulation condition for inputting the images “X” and “Y”. (b) The conductance states of the devices under different inputting conditions.

Fig. 5.  (Color online) Demonstration of the image. (a) Boolean subtraction and (b) Boolean union operations in the memristive synapse array.

[1]
Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1(1), 22 doi: 10.1038/s41928-017-0006-8
[2]
Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577(7792), 641 doi: 10.1038/s41586-020-1942-4
[3]
Wang Z, Joshi S, Savel’ev S, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16(1), 101 doi: 10.1038/nmat4756
[4]
Wang Z Q, Xu H Y, Li X H, et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv Funct Mater, 2012, 22(13), 2759 doi: 10.1002/adfm.201103148
[5]
Yan X B, Zhao J H, Liu S, et al. Memristor with Ag-cluster-doped TiO2 films as artificial synapse for neuroinspired computing. Adv Funct Mater, 2018, 28(1), 1705320 doi: 10.1002/adfm.201705320
[6]
Wang J R, Zhuge F. Memristive synapses for brain-inspired computing. Adv Mater Technol, 2019, 4(3), 1800544 doi: 10.1002/admt.201800544
[7]
Ohno T, Hasegawa T, Tsuruoka T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10(8), 591 doi: 10.1038/nmat3054
[8]
Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15(3), 2203 doi: 10.1021/acs.nanolett.5b00697
[9]
Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10(4), 1297 doi: 10.1021/nl904092h
[10]
Li Y, Zhong Y, Zhang J, et al. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci Rep, 2015, 4(1), 4906 doi: 10.1038/srep04906
[11]
Serrano-Gotarredona T, Masquelier T, Prodromakis T, et al. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci, 2013, 7(2), 2 doi: 10.3389/fnins.2013.00002
[12]
Zucker R S, Regehr W G. Short-term synaptic plasticity. Annu Rev Physiol, 2002, 64(1), 355 doi: 10.1146/annurev.physiol.64.092501.114547
[13]
Debanne D, Gähwiler, B H, Thompson S M. Heterogeneity of synaptic plasticity at unitary CA3–CA1 and CA3–CA3 connections in rat hippocampal slice cultures. J Neurosci, 1999, 19(24), 10664 doi: 10.1523/JNEUROSCI.19-24-10664.1999
[14]
Kim M K, Lee J S. Short-term plasticity and long-term potentiation in artificial biosynapses with diffusive dynamics. ACS Nano, 2018, 12(2), 1680 doi: 10.1021/acsnano.7b08331
[15]
Wang Z, Zeng T, Ren Y et al. Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices. Nat Commun, 2020, 11(1), 1510 doi: 10.1038/s41467-020-15158-3
[16]
Abraham W C, Gustafsson B, Wigström H. Single high strength afferent volleys can produce long-term potentiation in the hippocampus in vitro. Neurosci Lett, 1986, 70(2), 217 doi: 10.1016/0304-3940(86)90466-0
[17]
Yang J T, Ge C, Du J Y, et al. Artificial synapses emulated by an electrolyte-gated tungsten-oxide transistor. Adv Mater, 2018, 30(34), 1801548 doi: 10.1002/adma.201801548
[18]
Du J Y, Ge C, Riahi H, et al. Dual-gated MoS2 transistors for synaptic and programmable logic functions. Adv Electron Mater, 2020, 6(5), 1901408 doi: 10.1002/aelm.201901408
[19]
Lin Y, Zeng T, Xu H Y, et al. Transferable and flexible artificial memristive synapse based on WOx Schottky junction on arbitrary substrates. Adv Electron Mater, 2018, 4(12), 1800373 doi: 10.1002/aelm.201800373
[20]
Lin Y, Wang C, Ren Y, et al. Analog –digital hybrid memristive devices for image pattern recognition with tunable learning accuracy and speed. Small Methods, 2019, 3(10), 1900160 doi: 10.1002/smtd.201900160
[21]
Kamiya R, Zucher R S. Residual Ca2 + and short-term synaptic plasticity. Nature, 1994, 371, 603 doi: 10.1038/371603a0
[22]
Wang T Y, Meng J L, He Z Y, et al. Room-temperature developed flexible biomemristor with ultralow switching voltage for array learning. Nanoscale, 2020, 12(16), 9116 doi: 10.1039/D0NR00919A
[23]
Waser R, Aono M. Nanoionics-based resistive switching memories. Nanosci Technol, 2009, 158 doi: 10.1142/9789814287005_0016
[24]
Wang W, Xu J, Ma H, et al. Insertion of nanoscale AgInSbTe Layer between the Ag electrode and the CH3NH3PbI3 electrolyte layer enabling enhanced multilevel memory. ACS Appl Nano Mater, 2019, 2(1), 307 doi: 10.1021/acsanm.8b01928
[25]
Atkinson R C, Shiffrin R M. Human memory: A proposed system and its control processes. Psychol Learn Motiv, 1968, 2, 89 doi: 10.1016/S0079-7421(08)60422-3
[26]
Harary F, Wilcox G W. Boolean operations on graphs. Math Scand, 1967, 20(1), 41 doi: 10.7146/math.scand.a-10817
[27]
Satoh T, Chiyokura H. Boolean operations on sets using surface data. Proceedings of the First ACM Symposium on Solid Modeling Foundations and CAD/CAM Applications, 1991, 119
[28]
Mäntylä M. Boolean operations of 2-manifolds through vertex neighborhood classification. ACM Trans Graph, 1986, 5(1), 1 doi: 10.1145/7529.7530
[29]
Gardan Y, Perrin E. An algorithm reducing 3D Boolean operations to a 2D problem: concepts and results. Comput Aid Des, 1996, 28(4), 277 doi: 10.1016/0010-4485(95)00050-X
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    Received: 21 May 2020 Revised: 06 June 2020 Online: Accepted Manuscript: 04 September 2020Uncorrected proof: 11 September 2020Published: 09 January 2021

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      Jiajuan Shi, Ya Lin, Tao Zeng, Zhongqiang Wang, Xiaoning Zhao, Haiyang Xu, Yichun Liu. Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse[J]. Journal of Semiconductors, 2021, 42(1): 014102. doi: 10.1088/1674-4926/42/1/014102 J J Shi, Y Lin, T Zeng, Z Q Wang, X N Zhao, H Y Xu, Y C Liu, Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse[J]. J. Semicond., 2021, 42(1): 014102. doi: 10.1088/1674-4926/42/1/014102.Export: BibTex EndNote
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      Jiajuan Shi, Ya Lin, Tao Zeng, Zhongqiang Wang, Xiaoning Zhao, Haiyang Xu, Yichun Liu. Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse[J]. Journal of Semiconductors, 2021, 42(1): 014102. doi: 10.1088/1674-4926/42/1/014102

      J J Shi, Y Lin, T Zeng, Z Q Wang, X N Zhao, H Y Xu, Y C Liu, Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse[J]. J. Semicond., 2021, 42(1): 014102. doi: 10.1088/1674-4926/42/1/014102.
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      Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse

      doi: 10.1088/1674-4926/42/1/014102
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      • Jiajuan Shi:got her B.S. degree at Liaoning Normal University in 2018, Dalian, China. Now she is a Ph.D. student at the key Laboratory for UV Light-Emitting Materials and Technology, Northeast Normal University, China. Her current research focuses on fabrication and electrical characterization of memristive devices
      • Ya Lin:received his B.S. degree at Dalian University of Technology in 2012, Dalian, China, and Ph.D. degrees at Northeast Normal University in 2018, Changchun, China. He is currently a postdoctoral fellow at Northeast Normal University, China. His research interest includes the design and fabrication of memristive devices and their applications for synaptic emulations
      • Zhongqiang Wang:received the B.S. and Ph.D. degrees in 2008 and 2013 at Northeast Normal University, Changchun, China. During 2014–2016, he worked as a postdoctoral fellow in Polytechnic University of Milan, Italy. Currently, he is a professor at Northeast Normal University. His current research interests include device fabrication, electrical characterization, and neuromorphic applications of memristor
      • Corresponding author: Email: liny474@nenu.edu.cnwangzq752@nenu.edu.cn
      • Received Date: 2020-05-21
      • Revised Date: 2020-06-06
      • Published Date: 2021-01-10

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