Programmable Synaptic Metaplasticity and below Femtojoule Spiking

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Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-based Neuromorphic Memristor Bo Liu, Zhiwei Liu, In-Shiang Chiu, Mengfu Di, YongRen Wu, Jer-Chyi Wang, Tuo-Hung Hou, and Chao-Sung Lai ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b04685 • Publication Date (Web): 06 Jun 2018 Downloaded from http://pubs.acs.org on June 6, 2018

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ACS Applied Materials & Interfaces

Programmable

Synaptic

Metaplasticity

and

below

Femtojoule Spiking Energy Realized in Graphene-based Neuromorphic Memristor Bo Liua,b, Zhiwei Liua, In-Shiang Chiub, MengFu Dic, YongRen Wud, Jer-Chyi Wangb,e,f, Tuo-Hung Houg* Chao-Sung Laib,h,i,j* a

State Key Laboratory of Electronic Thin Films and Integrate Devices, University of Electronic

Science and Technology of China, Chengdu 610054, China b

Department of Electronic Engineering, Chang Gung University, Guishan Dist., 33302, Taoyuan,

Taiwan c

Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521,

USA d e

Integrated Service Technology, Shanghai, China Department of Neurosurgery, Chang Gung Memorial Hospital, Guishan Dist., 33305, Taoyuan,

Taiwan f

Department of Electronic Engineering, Ming Chi University of Technology, Taishan Dist., 24301,

New Taipei City, Taiwan g

Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University,

Hsinchu, 300, Taiwan h

Biosensor Group, Biomedical Engineering Research Center, Chang Gung University, Guishan Dist.,

33302, Taoyuan, Taiwan i

Department of Nephrology, Chang Gung Memorial Hospital, Guishan Dist., 33305, Linkou, Taiwan

j

Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist., 24301,

New Taipei City, Taiwan E-mail address: [email protected] and [email protected]

Keywords: graphene electrode, neuromorphic memristor, artificial synapses, below femtojoule spiking energy, programmable metaplasticity, spike-timing dependent plasticity.

ACS Paragon Plus Environment

ACS Applied Materials & Interfaces 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract: Memristors with rich interior dynamics of ion migration are promising for mimicking various biological synaptic functions in neuromorphic hardware systems. A graphene-based memristor shows an extremely low energy consumption of less than a femtojoule per spike, by taking advantage of weak surface van der Waals interaction of graphene. The device also shows an intriguing programmable metaplasticity property in which the synaptic plasticity depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus. This graphene-based memristor could be a promising building block toward designing highly versatile and extremely energy-efficient neuromorphic computing systems.


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The remarkable progress of AlphaGo has aroused widespread interest in neuromorphic computing1. This software-based neuromorphic paradigm realized powerful artificial intelligence by executing sophisticated machine learning and neural network algorithms on mighty supercomputers, but it also demands several orders of magnitudes higher processing power dissipation than the human brain2,3, which limits its ultimate scale-up capability. This issue could be attributed to the fundamental constraints in the sequential Boolean logic and von Neumann computer architecture. In contrast, for the human brain, various complicated perception and recognition abilities are performed in an exceptionally energy efficient (approximately 1-10 femtojoule per neuron spiking event), adaptive, massively parallel, and fault-tolerant fashion, through modulating the connection strength of 1015 synapses between 1011 neurons4. A schematic of the biological synaptic connection is illustrated in Figure 1(a). The synapse is a 20~40-nm-wide gap between the presynaptic axon and postsynaptic dendrite, where the excitatory or inhibitory spikes fire and subsequently modulate the post-synaptic membrane potential via releasing neurotransmitters and exchanging chemical ions (e.g., Ca2+ or Na+). The modulation of the connection strength (weight) between synapses is referred to as plasticity in neuroscience, which is the basis for learning, adaption, and memory in biological neural systems. Therefore, emulating biological synaptic plasticity in artificial hardware is highly desirable for realizing brain- inspired neuromorphic computing systems. Over the past few years, several essential synaptic plasticity functions, such as neurotransmitter release, excitatory postsynaptic currents, long-term depression or potentiation of postsynaptic currents, and spike-timing dependent plasticity (STDP) have been successfully modeled in various memristive devices by using voltage pulses as neuromorphic stimulations

5–11

. However, the weight change in biological synapses depends not only

on the present but also on the previous neural spiking activities12. This higher-order effect of plasticity is referred to as metaplasticity in neuroscience, and it could be regarded as the “plasticity of plasticity”. Metaplasticity leads to competition and robustness of neuron weights and could facilitate the competitive Hebbian and winner-takes-all learning rules for the applications of neuromorphic computing, such as pattern recognition13. Inspired by the presence of metaplasticity in biological neural systems, several efforts have been attempted to resemble this unique plasticity feature by leveraging the rich dynamics of ion motion in memristive devices recently14–19, in which the resistance could be switched in a binary or analog fashion according to the history of the applied voltage and current20.



However, due to the very nature of metaplasticity, the synaptic plasticity cannot be easily controlled using only the present stimulus, which poses constraints in designing neuromorphic systems and could potentially slow convergence when resetting the cumulative effects of previous stimuli in the system is necessary. Ideally, a programmable metaplasticity, which depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus when necessary, is more favorable in the design of neuromorphic computing systems. Moreover, most memristive devices require energy consumption of at least picojoule per spike21, which is substantially higher than that consumed in biological synapses. Further device and material engineering of memristors is required for realizing programmable and energy-efficient metaplasticity in brain-inspired computing.

A graphene-based neuromorphic memristor with programmable metaplasticity and below femtojoule spiking energy was experimentally demonstrated in this study. As shown in Figure 1(b), the Al top electrode (TE) serves as the presynaptic terminal, and the graphene bottom electrode (BE) serves as the postsynaptic terminal (more details of introducing graphene into memristor in Supporting Information). The TE voltage and BE current emulate the excitatory or inhibitory stimulation and the post-synaptic current, respectively. In response to the DC or AC voltage stimulation, the resistance variation of the graphene-based neuromorphic memristor is attributed to the field-induced reconfiguration of oxygen vacancy (VO) morphology22 in the AlOx resistance switching layer. In the graphene-based memristor presented in this work, programmable metaplasticity through the careful control of compliance current (CC) and extremely low spiking energy consumption of less than a femtojoule were demonstrated experimentally, which could potentially improve the design flexibility and energy efficiency of future neuromorphic systems that are capable of rivaling their biological counterpart.

In the following part of Figure 1, the material analysis was exhibited, including Raman spectrum for graphene lattice and high-resolution transmission electron microscopy (HR-TEM) with energy-dispersive X-ray spectroscopy (EDX) for imaging device structures. Before memristor fabrication, large-area Raman spectrum mapping were conducted to identify graphene lattice properties (details in the Supporting Information), as shown in Figure 1 (c-e). There are three typical Raman


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vibration modes for graphene, D peaks (defect mode), G peaks (vertical vibration mode) and 2D peaks (two-phonon vibration mode). The low ID/IG ratio, high I2D/IG ratio and their associated distribution (where ID/IG and I2D/IG ratios are tightly distributed at 0.15 and 2.3, respectively) indicate low defect density, high crystallinity and good uniformity of the as-transferred graphene sample23 (more details of graphene defects density in Supporting Information) . As shown in Figure 1 (f), it is obvious that the graphene layers with high crystalline quality are located between the SiO2 substrate and 7-nm AlOx resistance switching layer. EDX mapping associated with the TEM image in Figure 1 (g-j) was performed to identify the elementary compositions of graphene and the resistance switching layer. According to the EDX mapping, the stoichiometry of the Al and O elements in this region is approximately 1: 1.5.

The basis resistance switching measurements of Gr-BE memristor and Pt-BE memristor as a comparison were demonstrated Figure S1-S6, including the TEM images and EDX mapping of Pt-BE memristor in Figure S2. It can be found that Gr-BE memristors presents ultra-low operation current, high resistance ratio between high resistance state and low resistance state up to 106 due to its weak surface van der Waals interaction. Between this wide ranges, two distinct LRS’s of Gr-BE memristor with 50 µA CC and 50 nA CC could be seen as two different historical stimulations of the following neuromorphic operations. Figure 2 exhibits their synaptic plasticity characteristics in the DC sweep mode. Note the sweep voltages here are smaller than the SET/RESET voltages in Figure S1 to avoid abrupt resistance switching. As shown in Figure 2 (a-b, d-e), the device shows a pinched hysteresis effect, where the conductance increases or decreases with the positive or negative voltage sweeps, which indicates strengthening or weakening the filament connection through the rearrangement of oxygen defects. Because the device resided at their LRS’s at the respective CC’s, the rearrangement of oxygen defects likely facilitates horizontal expanding or shrinking of the filament diameter (Figure S7)18. These results mimic the potentiation and depression of the post-synaptic current, which is also plotted on a timescale in Figure 2 (c, f). Obviously, the Gr-BE memristors exhibit distinct current levels in the potentiation and depression processes between 50-nA CC and 50-µA CC, even under the identical present stimuli (DC voltage sweep). The discrepancy in synaptic plasticity originates from different filament morphologies induced by the previous SET process with different CC’s24. In the case



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of the 50-nA CC, the nonlinearity in the current-voltage relation becomes notable. Based on the quantum point contact theory, this phenomenon could be interpreted by the parabolic potential barrier at the interface between the graphene BE and the filament with low VO concentration25, and could be accounted for by the following equation26: σ = × × exp(−

( )

),

(1)

= × ( ),

(2)

where σ is the electrical conductivity, is the activation energy, is a constant 1×10-23 S m-1, nVo is the oxygen vacancy concentration, is Boltzmann constant, is 0.4 eV, and f is a piecewise linear and decreasing function. The current study is consistent with the above relations: the I-V curve is linear when the oxygen vacancy concentration is high enough (at 50-µA CC), and becomes nonlinear when the oxygen vacancy concentration is limited (at 50-nA CC). The above phenomenon are exactly in accordance with the concept of metaplasticity in neuroscience. The post-synaptic current can be regulated by prior activity (CC), during which the synaptic weight change is always accompanied with a morphological change and new synaptic connection path (filament) formation27. Additionally, based on the classical Bienenstock, Cooper and Munro (BCM) theory of synaptic plasticity, the threshold of the plasticity slides (the conductance nonlinearity) depends on the neuron cell’s previous history of activity (CC)12.

Subsequently, Figure 3 exhibits the synaptic plasticity characteristics of Gr-BE memristors in the AC pulse mode. 50 nA-CC and 50 µA-CC were also initially set to mimic the historical stimulation activity. Then, 100 consecutive voltage pulses of ±2.3 V with 100-ns width duration were applied to the TE of the device. The voltage pulses, analogous to neuron firing, gradually modulate the device conductance and thus the post-synaptic current. Note that in this AC pulse measurement, the post-synaptic current is simultaneously read using the stimulation pulse without an additional read pulse, which is more bionic and convenient for circuit applications. The magnitude of stimulation pulse below the pulse SET/RESET voltage is chosen to avoid abrupt resistance switching. Unlike the DC sweep mode, AC pulse stimulations were applied to the device HRS after a RESET process, where a ruptured gap is believed to form near the graphene electrode due to the inert nature of graphene. During the voltage pulse stimulation, the oxygen vacancies drift toward or backward to/from the graphene


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electrode in response to the positive and negative voltage pulses, thus adjusting the vertical tunnel distance in the ruptured gap (Figure S7) and post-synaptic current. The potentiation and depression of the post-synaptic current at both 50-nA CC and 50-µA CC mainly operate in the range between 100 pA and 10 nA, as shown in the highlighted regions in Figure 3 (a-b). In other words, the spiking energy of the Gr-BE memristor is approximately in the range of 0.01 to 1 femtojoule for both potentiation and depression10. It should be noted that even under the same or lower energy consumption level compared with biological synapses, Gr-BE memristors requires more than 2 V to activate excitatory or inhibitory response, while biological synapses demand only 0.1 mV. From the viewpoint of future neuromorphic circuit design, a reasonably high voltage threshold for a neuromorphic synapse is meaningful. An extremely low voltage threshold could deteriorate the system immunity to noise induced by thermal fluctuation, cross-coupling or other environmental disturbances. Additionally, the operating current under 50 nA-CC is slightly lower than that under 50-µA CC. According to the in-situ TEM observation from previous studies, the filament residues could still exist across the resistance switching layer even after the RESET process28, which contributes to the HRS current. Via a lower CC with a smaller filament size, less filament residues are preserved in the switching layer, which enables the lower operating current and energy consumption. These synaptic weight change behaviors in AC stimulation mode are also in agreement with the activity-dependent metaplasticity. Additionally, the measured post-synaptic response is well matched with the STDP rule in biological synapses, which is one of the critical weight update protocols for neuron learning and memory (more details of STDP rule in Supporting Information). The derived parameters of synaptic weight change factors /

and time

constants ! /! (µs) are 3.03/1.65 and 0.46/1.07 for 50-µA CC and 7.40/0.68 and 1.07/0.24 for 50 nA-CC. The different STDP characteristics depending on the previous history of activity (CC) is also in agreement with the metaplasticity. Moreover, the Gr-BE memristors operating at a time scale of µs is considerably faster than the biological counterpart operating at a time scale of ms, and thus could be an important building block toward high-speed neuromorphic computation systems with low energy consumption.

In addition to the above experimental demonstrations on metaplasticity, Figure 4 exhibits a proof-of-concept study on programmable metaplasticity. In the current proposed protocol, the historical



stimulation effect configured by SET CC on the post-synaptic current could be refreshed through a RESET operation and reconfigured using a new SET operation with a different CC. Therefore, the metaplasticity in this Gr-BE memristor is interchangeable in different CC modes, as illustrated in Figure 4 (a). Taking a device set with an initial 50-µA CC (Figure 4 (b)) as a demonstration, the 50-µA CC leads to a relatively strong filament with a large diameter and a higher LRS current. After rupturing the filament at RESET, a tunnel gap was formed between the graphene electrode and the residual filament within the AlOx. In the subsequent SET process, the CC was changed to 50 nA (Figure 4 (c)). Then, a new filament was formed in the ruptured gap but with a smaller diameter because of the smaller CC. Finally, the filament morphology could be expanded again when the CC was changed back to 50 µA, as shown in Figure 4 (d). For a device set with an initial 50-nA CC, the different filament morphologies could also be well controlled by CC’s (Figure 4 (e-g)). The precise control on the filament size through CC plays a vital role in the programmable metaplasticity of the Gr-BE memristor, which could be attributed to the high out-of-plane resistance at the graphene/AlOx interface that serves as an effective internal series resistor for compliance29. A brief comparison of synaptic plasticity and spiking energy consumption between the current work and previous reports was shown in Table S1 in Supporting Information.

In summary, integrating unique properties of graphene, such as high out-of-plane resistance and low out-of-plane thermal conductance, into the Gr-BE neuromorphic memristor leads to the extremely low yet precisely controlled operating current and high on-off resistance ratio, which enables below femtojoule spiking energy and programmable metaplasticity. In both the DC- and AC-mode measurements, current compliance in the prior SET process is regarded as historical stimulation, and post-synaptic current shows the properties of activity-dependent metaplasticity. Specifically, at HRS the spiking energy for both post-synaptic potentiation and depression could be as low as 0.01 to 1 femtojoule. Furthermore, the device also demonstrated intriguing programmable metaplasticity in which the historical learning effect could be reconfigured whenever needed. Combining programmable synaptic metaplasticity and below femtojoule spiking energy, the Gr-BE neuromorphic memristor could potentially facilitate the development of future highly flexible and extremely low-power neuromorphic computation systems.


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. Acknowledgement: This research was supported by the Ministry of Science and Technology, Taiwan (106-2221-E-182-059-MY3, 107-2911-I-182-502, 106-2632-E-182-001, and NCRPD2HP011), Chang Gung Memorial Hospital (CMRPD2F0022 and CMRPD2G0101). Bo Liu and Chao-Sung Lai generated the idea and designed the experiment. For the materials analysis, Bo Liu performed Raman spectrum probing, and Zhiwei Liu and YongRen Wu performed the FIB, TEM, and EDX measurements. Bo Liu fabricated the device, In-Shiang Chiu and MengFu Di conducted the basic memristor measurements, and Bo Liu conducted the DC and AC plasticity measurement. Bo Liu wrote the manuscript, and Tuo-Hung Hou, Jer-Chi Wang and Chao-Sung Lai revised it.



Figure 1. Schematic illustration and material analysis of the Gr-BE memristor. (a-b) schematic illustration of the biological synapse and Gr-BE neuromorphic memristor, (c-e) Raman spectrum and large-area mapping distribution of the graphene, (f) TEM image and (g-j) EDX mapping of the Gr-BE neuromorphic memristor.


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Figure 2. Synaptic plasticity of Gr-BE memristors operated in DC-sweep mode. IV curve and post-synaptic current vs. time slot are displayed during the potentiation and depression processes under positive and negative voltage sweeps. The device was previously set by (a-c) 50-µA CC and (d-f) 50-nA CC.



Figure 3. Synaptic plasticity of Gr-BE memristors operated in AC-pulse mode. (a-b) potentiation and depression under continuous positive and negative voltage pulse stimulations and (c-d) STDP characteristics are displayed using the device that was previously set under 50-µA and 50-nA CC.


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Figure 4. Programmable metaplasticity in Gr-BE memristor. (a) Schematic illustration of the concept of programmable metaplasticity. Note that the different colors in the identical device are only for demonstration of the different filament sizes. (b-d) Interchangeable resistance states on the device under 50-µA CC initially, (e-g) Interchangeable resistance states on the device under 50-nA CC initially.



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Programmable Synaptic Metaplasticity and below Femtojoule Spiking

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