Synaptic Computation Enabled by Joule Heating of Single-Layered

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Synaptic Computation Enabled by Joule Heating of Single-layered Semiconductors for Sound Localization LINFENG SUN, Yishu Zhang, Geunwoo Hwang, Jinbao Jiang, Dohyun Kim, Yonas Eshete, Rong Zhao, and Heejun Yang Nano Lett., Just Accepted Manuscript • DOI: 10.1021/acs.nanolett.8b00994 • Publication Date (Web): 18 Apr 2018 Downloaded from http://pubs.acs.org on April 18, 2018

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Synaptic Computation Enabled by Joule Heating of Single-layered Semiconductors for Sound Localization Linfeng Sun1†, Yishu Zhang2†, Geunwoo Hwang1, Jinbao Jiang1,3, Dohyun Kim1, Yonas Assefa Eshete1, Rong Zhao2*, Heejun Yang1* 1

Department of Energy Science, Sungkyunkwan University, Suwon 16419, Korea

2

Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore

3

IBS Center for Integrated Nanostructure Physics (CINAP), Institute for Basic Science,

Sungkyunkwan University, Suwon 16419, Korea †These authors contributed equally to this work. *Correspondence to: [email protected], [email protected] Abstract: Synaptic computation, which is vital for information processing and decision making in neural networks, has remained technically challenging to be demonstrated without using numerous transistors and capacitors, though significant efforts have been made to emulate the biological synaptic transmission such as short-term and long-term plasticity and memory. Here, we report synaptic computation based on Joule heating and versatile doping induced metal-insulator transition in a scalable monolayer-molybdenum disulfide (MoS2) device with a biologically comparable energy consumption (~ 10 fJ). A circuit with our tunable excitatory and inhibitory synaptic devices demonstrates a key function for realizing the most precise temporal computation in the human brain, sound localization: detecting an interaural time difference by suppressing sound intensity- or frequency-dependent synaptic connectivity. This report opens a way to implement synaptic computing in neuromorphic applications, overcoming the limitation of scalability and power consumption in conventional CMOS-based neuromorphic devices. Keywords: synaptic computation, metal insulator transition, doping, two dimensional materials, semiconductor, resistive heating

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Despite the crucial role of short-term plasticity (STP) for neural computing in the brain 1, current semiconductor-based logic and memory devices with numerous transistors and capacitors designed for synaptic operations have shown limited integration of STP conducted by innumerable biological synapses in the brain 2-13. A breakthrough in materials and operation mechanism should be conceived for effective neural computing; massively parallel architecture of numerous (1015) synapses in the human brain demands low dimensional smart materials

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for a practical and energy-efficient

neuromorphic chip. In this study, we report the first artificial synaptic computation based on STP with single-layered MoS2 devices, where tunable synaptic plasticity is realized by Joule heating of the device channel. The new working mechanism of STP resolves a long-standing issue, realizing synaptic computation by a single memristive device like a biological synapse, which could not be operated by previous conventional memristors by the lack of appropriate time-scales, tunability of excitatory and inhibitory functions in a single device or scalability. The resistive heating of a monolayer MoS2 allows a residual temperature increase by a low electrical energy (~10 fJ), which increases (synaptic facilitation) or decreases (synaptic depression) the conductance of the MoS2 with controllable characteristic times from seconds to minutes. The tunable STP (including the interconversion between facilitation and depression) could be realized by flexible doping levels of MoS2, as metal-insulator transition (MIT) in the MoS2 exhibits a crossover in the temperature-dependent conductance with the carrier density 17. Signal transfer from pre-synaptic to post-synaptic neurons through a biological synapse

1, 18

and a

corresponding artificial synapse based on our single-layered MoS2 device are schematically shown in Figure 1a and 1b. The spike signal transfer through the synapse (violet spikes in Figure 1a and 1b) exhibits a higher amplitude of transmitted excitatory post-synaptic current (EPSC) in Figure 1b (yellow EPSC in Figure 1b), which mimics synaptic facilitation with a higher synaptic strength. This is reflected as hysteresis on successive current-voltage (I-V) sweeps in Figure 1c; the hysteresis in the I-V curves exhibits a memory effect. Repeating I-V measurement cycles (over the orange arrow in Figure 1c) produces a higher current (higher conductance) as a synaptic connectivity of a biological synapse is strengthened by successive spike stimulus (Figure S1a). With the removal of stimulation, the device conductance automatically decays back into its initial conductance with a characteristic time constant of several minutes (the red line in Figure 1d), indicating the operation 2


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timescale of STP

1, 19

. Considering the variable conductance as a memory state, our device

demonstrates continuously tunable short-term memory states, which is a basic feature of STP for synaptic computation in biological systems.

Figure 1. Artificial synaptic device by Joule heating. Schematic diagrams of two-terminal devices based on monolayer MoS2 without (a) and with (b) a Joule heating. The violet and yellow spikes shown represent an input spike and a transmitted excitatory post-synaptic current, respectively. (c) Joule heating-driven conductance (G) facilitation with multiple voltage sweeps. (d) Absence of STP at a low temperature of 15 K. The inset shows temperature-dependent conductance changes of the MoS2 device with three different gate voltages. (e) Normalized gate-voltage-dependent STP and time constants. (f) Operation energy of STP and its evolution as a function of the device dimensions.

The increasing conductance during the I-V sweeps in Figure 1c could be understood as a resistive heating effect on the semiconducting MoS2 channel. As semiconductors show a positive correlation between temperature and conductance, the increased channel temperature by resistive heating should induce a higher conductance that can last for a short time with a residual temperature. Two pieces of experimental evidence for resistive heating driven STP are given in Figure 1d. No conductance variation on the same I-V sweeps was observed with a sufficient cooling power at a temperature of 15 K (the corresponding I-V sweeps are shown in Figure S2). Temperature-dependent conductance 3


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over a temperature range from 300 K to 450 K (the inset in Figure 1d) exhibits a similar conductance variation in Figure 1c and S1. The increased conductance by resistive heating corresponds to a stronger synaptic connectivity shown in Figure 1b. Due to the multiple routes of heat dissipation around the MoS2 device in ambient condition, a small voltage bias (< 0.1 V) translates into little temperature change, resulting in a linear I-V curve. Therefore, we used a voltage of 0.1 V to measure the automatically decayed conductance of the channel. A four-point resistivity probe measurement reveals that the main resistance originates from the channel rather than the electrode contact, as shown in Figure S3. The correlation between temperature and conductance varies with the carrier density in the MoS2, which has been identified as MIT in two-dimensional materials

17, 20

. Since metals and

semiconductors have opposite temperature-conductance correlations, doping-dependent STP with controllable time constants and gains is feasible with our artificial synapses. Gate-dependent STP with various synaptic strengths and timescales are demonstrated in Figure 1e and Figure S4. A negative gate voltage (-10 V in Figure 1e) decreases electron density in the n-doped MoS2 device, which generates a lower conductivity and a larger positive correlation between temperature and conductance. Highly n-doped MoS2 by a positive gate bias has been reported to show metallic behavior such as a negative temperature-conductance correlation

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; this turns off the synaptic

facilitation or enhances synaptic depression in our artificial synapse. Accordingly, both the synaptic (positive and negative) gains and time constants could be controlled by the doping level of atomically layered semiconductors (see also Figure S4). Beyond tunable STP, two more critical benefits from resistive heating of single-layered semiconductors for synaptic computation should be noted: the scalable device dimensions and the minimized energy consumption. A MoS2 synaptic device with a channel length of 50 nm (width of 200 nm) (Figure S5) was used to measure the electrical energy for a stimulus that can double the synaptic strength (Figure 1f). The pulse amplitude, duration and current flow were 0.5 V, 20 ms, and 7.23 pA, respectively. We estimated an operation energy of ~0.072 pJ, similar to the energy consumption by a spike in biological synapses for the meaningful synaptic strength change (approximately double) 21. As shown in the inset of Figure 1f, the energy consumption for resistive heating would be further reduced by decreasing the channel length. The ideal energy consumption 4


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for a spike in our synaptic device originates from resistive heating of a minimized two-dimensional volume of MoS2; other three-dimensional semiconductors cannot be adopted for our resistive heating-based STP with such a low energy. While short-term memory characteristics are highlighted as a first form of STP in Figure 1, emulating synaptic computation in the brain requires more critical forms of STP: paired pulse facilitation (PPF by synaptic facilitation) and paired pulse depression (PPD by synaptic depression). In neuroscience, PPF (PPD) arises from increased (decreased) presynaptic calcium ion (Ca2+) levels during repetitive impulses leading to a subsequently increased (decreased) release of neurotransmitter containing synaptic vesicles

22, 23

. The PPF or PPD index, as a characteristic

parameter to evaluate the strength of PPF or PPD, is defined as A2/A1, where A1 and A2 are the absolute amplitudes of the EPSCs or the inhibitory post-synaptic currents (IPSCs) by two successive pre-synaptic impulses (Figure S6). In our synaptic MoS2 device, the interval of the two impulses (∆t) ranges from 10 ms to 10 s with a pre-synaptic voltage amplitude and a width of 25 V and 50 ms, respectively (Figure 2a). It is clear that paired impulses increase more strongly the residual temperature of the MoS2 (corresponding to an increased amount of pre-synaptic Ca2+ for PPF) by a shorter interval of the two impulses, which produces a higher conductance (corresponding to an increased probability of vesicle fusion or more release of neurotransmitter). With a longer time-interval, the increased residual device temperature by the first voltage impulse will more greatly decrease before applying the second voltage impulse. To realize PPD, electric gating-based MIT in MoS2 and the negative temperature-conductance correlation of the metallic MoS2 were employed similarly with mimicking synaptic depression in our device; a heavily n-doped MoS2 showed an increased residual temperature with a lower conductance by paired pulses (Figure S7). We note that the synaptic depression or the role of inhibitory synapses, demonstrated by the heavily n-doped MoS2 in this work, is critical for all kinds of neural computations, considering that a large number of synapses and operations are involved in the process. The characteristic time and PPF (PPD) index are tunable by an electric gating or doping (carrier density) control (Figure 2b and Figure S7). The corresponding PPF index with a frequency domain is 5


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shown in Figure 2b with three different gate voltages. The fitted two time constants, t1 and t2, of the PPF index curve are consistent with those of biological synapses 24. Thus, PPF (PPD) performances, synaptic strengths, and time constants could be controlled by the carrier concentrations of materials. This implies that we can selectively facilitate or depress the information transfer from a series of external stimuli as diverse synapses conduct in biological neural networks.

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Depression 0.1 1 Time Interval (s)

t1= 50 ms, t2= 780 ms t1= 44 ms, t2= 705 ms

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25 50 0 150 300 0 25 50 0 150 300 0 25 50 0 150 300 Pulses (N) Time (s) Pulses (N) Time (s) Pulses (N) Time (s)

Figure 2. Demonstration of tunable synaptic computation. (a) PPF and PPD indexes after two consecutive pulses as a function of the inter-pulse interval. (b) PPF index plotted as a function of the stimulus frequency showing high-pass filtering behavior (excitatory synapse). The on/off ratio and the threshold frequency of PPF can be tuned by the gate voltage. The curves shown in (a) and (b) are fitted by a combined exponential formula (PPF = (A exp(-t/t1) + B exp(-t/t2)) to obtain two characteristic time constants. (c) Continuously increased synaptic strength (50 continuous synaptic strengths) by a series of pulses with an amplitude, width, and time interval of 25 V, 50 ms, and 50 ms, respectively. The auto-reset process was measured with a small voltage of 0.1 V.

A critical temporal signal processing for brain functions, synaptic filtering based on PPF (high-pass filtering) and PPD (low-pass filtering), is also reflected in Figure 2a and 2b. Moreover, other computations, such as sensitization/adaptation and gain control 25, 26, could be emulated with various 6


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impulse parameters

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; in Figure 2c, the synaptic strength is continuously enhanced by multiple

stimulations (sensitization/adaptation), and a saturation of the synaptic facilitation that exists in the brain is obtained by an equilibrium heating state. The three cycles of stimulation and STP in Figure 2c represent a stable operation of our synaptic device over multiple series of spikes.

Figure 3. Synaptic computation for sound localization. (a) Schematic picture for sound localization with both ITD and ILD. Both excitatory and inhibitory synapses are used. CN: cochlea nuclei; AN: auditory nerve; MSO: medial superior olive; MNTB: medial nucleus of the trapezoid body; LNTB: lateral nucleus of the trapezoid body; (b) Schematic picture of the working mechanism of synaptic computation for ITD-based sound localization. “CA” means cochlea shown in Figure. 3(a). The blue circles represent neurons. The horizontal dashed lines represent the potential threshold for neuronal firing. Frequency and stimulus number-dependent outputs at MSO are shown in (c) without and (d) with synaptic computation included in the sound localization. The brighter area (right top region) in ‘(c)’ shows a higher amplification for input synaptic stimulus with higher frequencies, which confounds the MSO by ILD.

The tunable STP in our synaptic device enables the most precise temporal neuronal computation in mammals, sound localization (SL)

28-30

. Two major working mechanisms for SL have been 7


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discovered (Figure 3a): coincidence detection by ‘interaural time difference (ITD)’ and by ‘interaural level difference (ILD)’31. While coincidence detection requires two signals from both cochlea nuclei (CN) as shown in Figure 3a, the louder sound to the ipsilateral cochlea nucleus produces not only a time difference (ITD) but also more frequent spikes (ILD) compared to the synaptic signal from contralateral cochlea nucleus (Figure 3a). Then, the more frequent spikes confound the coincidence detection by altering the medial superior olive (MSO) before the signal from contralateral cochlea nucleus arrives

31, 32

; therefore, the coincidence detection in biological systems is conducted in a

complicated way. We demonstrate experimentally how artificial synaptic computation can suppress the interference with ILD and encode only ITD information over a wide range of sound intensity and frequency in SL. Figure 3b illustrates a sound source closer to the top cochlea with an angle of θ. The contralateral cochlea and CN transfer the sound signal to low-frequency input spikes (compared to the ipsilateral cochlea and CN (red spikes in Figure 3b)) to the excitatory synapse (blue spikes in Figure 3b). The EPSC from the low-frequency input spikes (the blue EPSC in Figure 3b) produces a weaker PPF than the PPF from high-frequency input spikes (the red EPSC from the ipsilateral CN in Figure 3b); however, the weaker PPF in the bottom of Figure 3b produces a smaller reduction in the blue IPSC than the red IPSC from high-frequency input spikes through ipsilateral CN. Therefore, the artificial synaptic computation balances the frequency difference between input spikes originating from contrasting sound levels. We further note that the balance described in Figure 3b is critical to stabilize synaptic signals in all types of synaptic computation involving multiple signal transfers. The confounding effect from the cue of ILD without our synaptic computation or inhibitory synaptic device is shown in Figure 3c; the frequency (ILD), rather than the time difference (ITD), dominates the synaptic connectivity. At the upper-right corner in Figure 3c, the bright area indicates a larger EPSC than the bottom-left area. The steady-state ratio of synaptic devices involving only a cue of ITD (Figure 3d) is summarized in Figure S8; frequency-independent output signal amplitude to encode ITD information is realized by our artificial synaptic computation. The final current after signal processing through excitatory and inhibitory synapses shows a same current level as shown in Figure 3d. Thus, the circuit described in Figure 3b conveys only ITD information to the MSO for SL.

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We conducted a simulation study to demonstrate how the ITD can be used for SL. As shown in Figure S9, we set three θ of 0°, 90°, and 180° (following the geometry of Figure 3b) to represent three different locations of the sound source. Obviously, when the θ is 90° (P2 in Figure S9), there is no intensity and time difference; the signals from left and right CN enter the MSO at the same time, firing a specific neuron in MSO. While for other two cases: 0° and 180° (P1 and P3, respectively, in Figure S9), a time delay exists between the signals from left and right CN (ipsilateral and contralateral CN), and other corresponding neurons in MSO are activated. The detailed description for the realization of SL is in the Methods in supporting information. Besides the SL, the circuits in Figure 3b could be used for a coincidence detection for other equivalent neuronal computing. Synaptic computation demonstrated by the gate-tuning of STP described in Figure 1 and 2 requires a three-terminal device geometry unlike biological synapses; the diverse synaptic strengths and timescales of numerous (1015) synapses are fixed in the brain under their given roles in neural networks. We demonstrated that the conceptual operation of diverse STP for synaptic computation is feasible in a two terminal device geometry by controlling the initial doping level of each MoS2 without any gate voltage. As different carrier densities of MoS2 have been reported by various physical and chemical treatments such as post-annealing and chemical doping, we developed a synthesis technique for less n-doped MoS2 by introducing locally-distributed bilayer regions. The photoluminescence (PL) and Raman features of the less n-doped MoS2 having partial bilayer areas are shown in Figure 4a, 4b and S11 (See Methods). The topography of the bilayer regions was characterized in Figure 4d by atomic force microscopy (AFM), which was combined with the results of confocal PL, and Raman spectroscopy (Figure 4); the weaker PL in Figure 4a and Figure S11a,33, 34 the height difference in Figure S4f,35 and the stronger Raman intensity (Figure 4b and Figure S11b)35, 36 are consistent with the characteristics of previously reported bilayer MoS2. The transfer curves in Figure 4c clearly exhibits less n-doped behavior from MoS2 having partial bilayer regions (black curve), compared to that from a conventional MoS2 (red curve). The MoS2 in Figure 4c (group 2, red curve) had no bilayer region, suggesting that the decreased n-type doping characteristics originates from the formation of partial bilayer regions in MoS2. This was further 9


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supported by Kelvin probe force microscopy (KPFM) with a corresponding optical image in Figure 4e. In the height and surface potential profiles, a correlation between the topographic height and the surface potential is observed with a potential difference of 28 meV. The lower surface potential (darker regions in Figure 4f) manifests a higher work function37 at the bilayer region than that of the surrounding monolayer region as described by the band diagram in Figure S13; this reduces the overall carrier density of the MoS2 flake having local bilayer regions,38 which clarifies the origin of the less n-doped behaviors in Figure 4c (group 1) generating the synaptic plasticity by Joule heating of the 2D semiconducting MoS2 in Figure 1 and 2. Instead, the metallic samples with high n-doped do not show conductance plasticity (Figure S14).

Figure 4. Synthesis control for diverse STP. Total intensity mapping of the (a) PL and (b) Raman of the MoS2 used in Figure. 1. The bilayer regions produce a weaker photoemission and a stronger Raman scattering feature compared to those from monolayer regions. (c) Transfer curves from the devices used in Figure. 1 (black curve) and from a typical MoS2 device fabricated for comparison (red curve). (d) The corresponding AFM image. (e) A KPFM image of the device containing a bilayer region, which is also presented in ‘Figure. S12’ with a tip bias of 5 V. (f) The height profile of AFM and the potential profile of KPFM. 10


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In conclusion, a synaptic device based on Joule heating of single-layered semiconductors was developed for diverse STP with controllably tunable synaptic connection and timescales required for synaptic computation and neuromorphic computing. The unique operation of synaptic facilitation and depression with a low operation energy (~ 10 fJ) originates from MIT with a control parameter of the carrier density in MoS2. Our synaptic devices were adopted for realizing sound localization, which shows a breakthrough for complete neuromorphic computing, mimicking the complicated and accurate synaptic information processing in the human brain. Supporting Information The Supporting Information is available free of charge on the ACS Publications website. The description of materials synthesis, device fabrication, and working mechanism of sound localization; Details of KPFM measurement, optical image, Raman and PL spectra and images; Conductance plasticity with the stimulus of negative bias; Temperature dependent Id-Vd curves; Four-probe resistance measurement; Gate-tunable time constants of STP; Operation energy of synaptic device based monolayer MoS2 nanoribbon; Measurement of short-term depression; Comparison of steady-state EPSC amplitude ratio with and without considering synaptic computation; Simulation results of sound localization; Id-Vg curves for a highly n-doped monolayer MoS2. Acknowledgments This work is supported by the Samsung Research Funding & Incubation Center of Samsung Electronics under project no. SRFC-MA1701-01 (H. Yang), and Singapore Ministry of Education Academic Research Fund Tier 2 grant with no. MOE2016-T2-2-141 (R. Zhao). L. F. Sun. acknowledges support from the Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant no. NRF-2017H1D3A1A01013759. Notes The authors declare no competing financial interest.

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Synaptic Computation Enabled by Joule Heating of Single-Layered

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