Essay Writing Service

1 Star2 Stars3 Stars4 Stars5 Stars (No Ratings Yet)

Emulating Bilingual Synaptic Response Using Junction Based Artificial Synaptic Device

Emulating Bilingual Synaptic Response Using Junction Based Artificial Synaptic Device

Abstract – Excitatory and inhibitory postsynaptic potentials are the two fundamental categories of synaptic responses underlying the diverse functionalities of the mammalian nervous system. Recent advances in neuroscience have revealed the co-release of both glutamate and GABA neurotransmitters from a single axon terminal in neurons at the ventral tegmental area that can result in the reconfiguration of the postsynaptic potentials between excitatory and inhibitory effects. The ability to mimic such features of the biological synapses in semiconductor devices, which is lacking in the conventional field effect transistor-type and memristor-type artificial synaptic device, can enhance the functionalities and versatility of neuromorphic electronic systems in performing tasks such as image recognition, learning and cognition. Here, we demonstrate an artificial synaptic device concept, an ambipolar junction synaptic devices, which utilizes the tunable electronic properties of the heterojunction between two layered semiconductor materials black phosphorus and tin selenide to mimic the different states of the synaptic connection, and hence realize the dynamic re-configurability between excitatory and inhibitory postsynaptic effects. The resulting device relies only on the electrical biases at either the presynaptic or the postsynaptic terminal to facilitate such dynamic re-configurability. It is distinctively different from the conventional hetero-synaptic device in terms of both its operational characteristics and biological equivalence. Key properties of the synapses such as potentiation and depression, and spike-timing-dependent plasticity are mimicked in the device for both the excitatory and inhibitory response modes. The device offers reconfiguration properties with the potential to enable useful functionalities in hardware-based artificial neural network.

Keywords: Artificial synaptic device, two-dimensional heterojunctions, black phosphorus, tin selenide, re-configurability



In neuroscience, an excitatory postsynaptic potential is a temporary depolarization of the postsynaptic membrane that makes the postsynaptic neuron more likely to fire an action potential.1 In contrast, the inhibitory postsynaptic potentials counteracts the excitatory actions and makes the postsynaptic neuron less likely to fire.2 The type of synaptic effects, whether it is excitatory or inhibitory, is determined by the type of ion channels in the postsynaptic neuron activated by the specific neurotransmitter3 released from the pre-synaptic neuron. A recent study showed the co-release of glutamate and gamma-Aminobutyric acid (GABA), excitatory and inhibitory fast neurotransmitters, from a single axon terminal in neurons of the ventral tegmental area that project to the lateral habenula,4 allowing both excitatory and inhibitory postsynaptic potentials to be produced at the same synapse depending on the state of the pre-synaptic and post-synaptic neurons. Other studies have also shown that during mammalian brain development, synaptic activities in certain neurons with GABA neurotransmitters can switch from being excitatory to inhibitory.5-7 In artificial synapses that mimic the operation of their biological counterparts, it is often desirable to re-configure the same synapse between excitatory and inhibitory operations. The ability to re-configure the synaptic effects in a single synaptic unit can offer desirable flexibility and versatility for artificial neural network and neuromorphic system design. However, such re-configurability of synaptic effects has been difficult to realize in a single solid-state device. Traditional methods to build an artificial synapse typically rely on circuit-based approach8-13 that requires 10-20 transistors to realize one synapse. The conventional memristor-type14-20 and transistor-type21-25 artificial synapses can realize synaptic functions in a single semiconductor device, but lacks the ability to dynamically reconfigure between excitatory and inhibitory responses without the addition of a modulating terminal.

In this work, we propose to mimic the bio-synapse that can co-release both excitatory and inhibitory neurotransmitters using a tunable heterojunction formed between black phosphorus (BP) and tin selenide (SnSe) and realize such re-configurability between the excitatory and inhibitory synaptic effects. The synaptic behavior can be dynamically tuned by the electrostatic bias at both the input and output terminals of the device. This device is distinctively different from the previously demonstrated hetero-synaptic devices2627 in terms of both their operational characteristics and biological equivalence. The hetero-synaptic device typically relies on a third active terminal to modulate the synaptic responses and resembles a biological synapse influenced by an external neuromodulator.2627


Figure 1a shows the schematic of the proposed synaptic device structure and the bias conditions, and Figure 1b shows the schematic of a bio-synapse that can co-release both excitatory and inhibitory neurotransmitters. The device consists of the heterojunction between BP and SnSe. Both BP and SnSe are layered semiconductors of orthorhombic crystal structure with bandgaps of 0.3 eV and 0.8 eV, respectively. The pre-synaptic signal is applied at the bottom silicon gate of the device, i.e. the presynaptic input terminal. The postsynaptic current (PSC) flows through the vertical junction between BP and SnSe. Depending on the bias condition on the silicon gate (Vg) and the bias voltage (Vbias) between the electrodes contacting BP and SnSe, the postsynaptic response of the device can be tuned over a wide range of characteristics. Between the BP layer and the SiO2 gate dielectric, a thin layer of phosphorus oxide (POx), the native oxide of BP, is intentionally formed through exposure to oxygen. This POx layer functions as the charge trapping layer25 to enable synaptic behavior in the device. The optical image for one of the devices we have tested is shown in Figure S1 of the supporting information. The Raman spectrum of the BP-SnSe junction is shown in Figure S2 of the supporting information.

The cross-section of the device is characterized by transmission electron microscopy (TEM) and scanning TEM (STEM). The high-resolution TEM image in Figure 1c clearly shows the highly crystalline layered lattice of BP, which has a thickness of ~6 nm. It is also clear that the junction between the SnSe layer and the BP layer is of high crystalline quality. We note that there is an amorphous layer between SiO2 and BP. To identify the nature of this amorphous layer and the material composition of each layer in the device, energy-dispersive X-ray spectroscopy (EDX) mapping was performed on the same region shown in the TEM image. As shown in Figure 1c, the crystalline SnSe layer shows the strong presence of both Sn and Se. The BP region shows the highly crystalline layered structure of the phosphorus (P) element. The presence of the oxygen (O) and silicon (Si) elements was observed in the SiO2 region, which is absent of the phosphorus element. In the interface region between BP and SiO2, both P and O are observed, confirming that the interfacial layer is a phosphorus oxide with a thickness of ~2 nm. Such POx layer can also be confirmed based on our previous BP TEM results.25 The EDX line-scan performed along the cross-sectional region clearly shows the presence of the POand SiO2 layers. The POx layer is a key functional layer in this synaptic device, which traps and de-traps electrons in response to the pulses at the input terminal to enable the postsynaptic current change at the output of the device. The thickness of the SnSe layer is ~100 nm.

The BP-SnSe junction is fundamental to enabling the synaptic characteristics of the device and its re-configurability between the excitatory and inhibitory responses. Device simulations are first developed to elucidate the operation mechanism that results in the tunable electronic characteristics of the BP-SnSe junction as shown in Figure 2a and hence its synaptic behavior (The details of the simulation method can be found in SI.). The simulated band profiles, as shown in Figure 2b and 2c, indicate that this junction-based device can be configured into different operation regions by the bias Vg applied at the input terminal, i.e. the gate. Both a vertical BP-SnSe heterojunction, whose band profiles are shown in Figure 2b, and a lateral homojunction in BP, whose band profiles are shown in Figure 2c, are formed in the current flow path. While the top SnSe layer is p-type2829 and insensitive to the applied bias Vg due to charge screening, the bottom BP layer can be effectively modulated. The relatively moderate 0.3 eV bandgap of BP facilitates the modulation of the BP layer by Vg from p-type to intrinsic and then to n-type in the non-stacking region, as shown in Figure 2c. A lateral homo-junction forms at the boundary of the stacking (BP-SnSe overlap) and non-stacking (only BP) regions because the gate modulation of the bands is more effective in the non-stacking region of the BP layer compared to the BP region under the SnSe layer. At the presence of a negative gate voltage of Vg=-20 V, the BP layer is modulated to p-type, and a p-p vertical heterojunction is formed between BP and SnSe as shown by the simulated band profile in Figure 2b. With a positive gate voltage of Vg=20 V, the BP layer can be modulated to n-type and an n-p vertical heterojunction with a rectifying I-V characteristics is expected. At zero gate bias, the BP layer in the non-stacking region is close to intrinsic and an i-p junction is formed in the current flow path. The intrinsic BP non-stacking region has a low charge density, which results in a large resistance and a low current when the gate is biased near zero in the transfer I-V characteristics. The tunable junction characteristics between the BP and SnSe layers as predicted by the simulation is confirmed by the electrical measurement of the fabricated device. Figure 2d shows the measured reconfigurable rectifying characteristics of the fabricated device. The three regimes of different junction types are clearly observed, i.e. an n-p rectifying junction behavior at Vg= 20 V, an i-p rectifying junction behavior at Vg=0 V, and a p-p non-rectifying junction behavior at Vg=-20 V.

Figure 3a shows the current in this ambipolar junction synaptic device as a function of both Vg and Vbias. The current is plotted as the color map in logarithmic scale using its absolute value. Based on the doping types of the junction between BP and SnSe, the plot can be divided into three regions, i.e. the n-p, i-p and p-p junction regions as predicted by the theoretical simulations. Furthermore, based on the horizontal ridge of the minimum conductivity resulting from the zero bias condition of Vbias and the diagonal ridge joining the points of minimum conductivity as the junction changes from n-p to i-p (both in yellow dashed lines), the current map can be divided into four operation quadrants. When a positive input pulse is applied at the presynaptic input terminal, electrons will be injected and trapped inside the POx layer. This will shift the Fermi level in BP away from the conduction band and closer towards the valence band. For bias conditions in the two left quadrants of the current map in Figure 3a, this Fermi level shift will lead to an increase in the PSC and result in an excitatory postsynaptic potential. For bias conditions in the two right quadrants of the current map, this Fermi level shift will lead to a decrease in the PSC, and hence an inhibitory postsynaptic potential. Figure 3b shows the postsynaptic current measured at the output terminal of the device for four selected bias points 1-4 as indicated on the current map. The PSC is excitatory for bias points 2 (Vg=10 V, Vbias= 2 V) and 3 (Vg=-5 V, Vbias= -5 V) since when the positive input pulse is applied at the presynaptic terminal, the shift in the Fermi level in BP as a result of electron injection into the POx layer leads to an increase in the PSC in both of these two operation quadrants. The response is inhibitory at bias point 4 (Vg=10 V, Vbias= -2 V) since the PSC decreases in this operation quadrant when electrons are injected into the POx layer in response to the positive input pulse. Finally, the response is low at bias point 1 (Vg=-5 V, Vbias= 3 V) due to the relatively constant current as a function of Vg and Vbias in the vicinity of this bias condition. As a result, this synaptic device can reconfigure between generating an excitatory PSC and an inhibitory PSC by either changing the Vbias at the postsynaptic output from positive to negative, or by changing the baseline bias Vg at the presynaptic input from negative to positive, or vice versa. The device, hence, provides versatile re-configurability with control knobs at either its input or output terminals. It also differentiates itself from the conventional hetero-synaptic device2627  since the latter requires a third modulating terminal to adjust the synaptic responses, which resembles a biological synapse influenced by an external neuromodulator.2627 Typically, the positive gate baseline can maintain the synaptic state for a longer time (Figure 3b case 2) while negative gate baseline reduces that time (Figure 3b case 3).

Figure 4 shows the key characteristics of biological synapses – the potentiation, depression and spike-timing-dependent-plasticity (STDP) mimicked in this junction based artificial synaptic device for both the excitatory and inhibitory operation modes. Here, we show the characteristics of the device at both positive and negative Vbias across the BP-SnSe junction, which gives excitatory and inhibitory responses respectively, with the input pulse biased at a baseline of 10 V. As shown in Figure 4a and Figure 4b, twenty positive pulses (V=20 V, W=10 ms) are consecutively applied at the presynaptic input of the device followed by 20 negative pulses (V=-20 V, W=10 ms). For a positive Vbias=2 V (Figure 4a), the device is in the excitatory mode. The PSC increases rapidly in response to the first few positive input pulses before saturating at about 37% weight change, demonstrating potentiation behavior. The subsequent negative input spikes cause the PSC to decrease, resulting in depression behavior. For a negative Vbias=-4 V (Figure 4b), the postsynaptic response of the device becomes inhibitory. The PSC decreases rapidly in response to the first few positive input spikes before saturating at about -46% weight change, resulting in depression behavior. The subsequent negative input spikes cause the PSC to increase, giving rise to the synaptic potentiation.

STDP is a key empirically observed characteristic of biological synapses believed to be fundamental for many functions of the brain from learning to memory.3031 The STDP of the BP-SnSe junction synaptic device operating in the excitatory and inhibitory modes corresponding to the same bias conditions as that in Figure 4a and 4b are shown in Figure 4c and 4d, respectively. As shown in Figure 4c, for the synaptic device operating in the excitatory mode, when the pre-synaptic input pulse arrives before the post-synaptic action, it results in the strengthening of the synaptic connection (potentiation) with positive weight change. Longer positive time interval results in less potentiation. In contrast, it leads to the weakening of the synaptic connection (depression) when the pre-synaptic input pulse arrives after the post-synaptic pulse. Longer negative time interval reduces the negative weight change in the synapse. The STDP behavior is reversed for the same junction synaptic device operating in the inhibitory mode.32 As shown in Figure 4d, for the synaptic device operating in the inhibitory mode, when the pre-synaptic input pulse arrives before the post-synaptic action, it results in the weakening of the synaptic connection (depression) with negative weight change. Longer positive time interval results in less depression. The converse will lead to the strengthening of the synaptic connection (potentiation) when the pre-synaptic spike arrives after the post-synaptic spike. The measured behavior of the BP-SnSe junction synaptic devices agrees well with the STDP in both excitatory and inhibitory biological synapses.3031 An exponential relation can be used to fit the data to extract the correlation time constants for both potentiation and depression responses. Based on a spike-timing dependent synaptic plasticity rule, the amount of synaptic modification arising from a single pair of pre- and postsynaptic pulses separated by a time ∆t is given by:

G(∆t)=A+exp∆tτ+        if ∆t<0-A-exp∆tτ-     if ∆t≥0


The coefficients A+ and A are positive for excitatory synaptic operation and negative for the inhibitory synaptic operation of the device. The ranges of pre-to-postsynaptic inter-spike intervals over which the strengthening and weakening of synaptic connections occur are given by τ+ and τ. They are both in the range of tens of milliseconds, which match well with the millisecond-scale response in typical biological systems.30

The versatile tunability of the device synaptic characteristics with control at both the presynaptic terminal and the postsynaptic terminal are shown in Figure 5. For applications such as pattern recognition by offline training via software, more continuous tuning of the response is desirable. The versatility of the device to tune the response at both input and output terminals provides additional freedom to set the synaptic weight to a precise value. Figure 5a summarizes the synaptic weight change of the device in response to a 20 V input pulse with different biases at the presynaptic and postsynaptic terminals. The weight change can be continuously tuned by either Vg and Vbias applied at the presynaptic and postsynaptic terminals, respectively, and each of the terminal has the capability to reconfigure the synaptic device between excitatory and inhibitory responses. The detailed PSC change at the different bias voltages are shown in Figure 5b. For positive bias (Vbias=2 V) at the output terminal and input baseline voltage equal to or lower than 0 V, the weight change is around or below 1 %, which resembles two isolated neurons without significant synaptic connection. When the input baseline increases to 5 V, 10 V and 15 V, the same device can be reconfigured to be a weakly excitatory, strongly excitatory and weakly inhibitory synapse, respectively. In this way, both the inhibitory and excitatory synapses can be realized in the same device by simply changing the input baseline voltage. Moreover, by changing the bias voltage to negative (Vbias=-4V), the weight profile under the different input baseline can be reconfigured. When the input baseline increase beyond -5 V, the characteristics of the device can be reconfigured from being a weakly excitatory synapse at Vg=-15 V, -10 V and -5 V to a strongly excitatory synapse at 0 V. The response becomes strongly inhibitory at the vicinity of Vg=10 V and weakly inhibitory at Vg= 15 V. Hence, this junction based synaptic device allows bilingual (both excitatory and inhibitory) responses with tunable strengths in the same artificial synapse that can be reconfigured by either the input or the output terminals of the device without the need for a third modulating terminal.



In summary, a junction based artificial synaptic device concept is proposed and experimentally demonstrated utilizing the BP-SnSe heterostructure for mimicking a biological synapse at which a single presynaptic neuron can release both excitatory and inhibitory neurotransmitters. The junction between the moderate bandgap material BP and SnSe gives rise to tunable rectifying electrical characteristics. Furthermore, the charge transfer between the native oxide of BP and the BP channel is utilized to achieve the synaptic behavior. The resulting device offers the useful synaptic characteristics that is reconfigurable between the excitatory and inhibitory responses, resembling biological synaptic activities of a single axon-dendritic synaptic junction that can co-release glutamate (excitatory) and GABA (inhibitory) neurotransmitters. With highly tunable and reconfigurable synaptic characteristics enabled at the single device level, this re-configurable artificial synaptic device may simplify the design and enable useful functionalities in emerging neuromorphic computing systems.


Device Fabrication: The fabrication of the devices started with the exfoliation of BP thin films from bulk crystals onto 90 nm SiO2 on a silicon substrate in an argon-filled glovebox with both oxygen and water concentrations well below zero point one part per million (0.1 ppm). A single-crystalline SnSe flake is then exfoliated and transferred onto the BP flake to form BP-SnSe heterojunctions in the glovebox. Subsequently, the sample was coated with a poly (methyl methacrylate) (PMMA) resist layer and patterned for metallization using a Vistec EBPG 5000+ 100 kV electron-beam lithography system. Cr/Au (3/30 nm) were then evaporated by thermal evaporation using a Kurt J Lesker Nano 36 system to form the source, drain and voltage probe contacts on BP flakes through a liftoff process. A thin layer of POx is formed between BP and SiO2 due to the oxidation of phosphorus as confirmed by the EDX mapping of the device cross-section in Figure 1c.

AFM Measurements: The AFM images were captured using a Bruker Dimension-Icon system.

Transmission Electron Microscopy: The cross-section of the BP-SnSe junction was prepared by the focused ion beam (FEI Helios G3) and characterized structurally using a FEI Tecnai Osiris 200 keV transmission electron microscopy.

Energy-dispersive X-ray spectroscopy: The elemental mapping shown in Figure 1c was acquired using a built-in EDX measurement module in the TEM system.

Raman Spectroscopy: The Raman spectra were acquired using a Horiba LabRAM HR Evolution system.

Electrical Measurements: All the electrical characterizations were carried out using Keysight B1500A parameter analyzer in a Lakeshore probestation. The electrical pulses with 10 ms duration were applied to the gate as pre-synapse input and the behaviors of the drain current were recorded as the post-synapse signals. All measurements were performed in vacuum (<1×10-4 Torr) at room temperature.


1. Thomson, A.; Deuchars, J.; West, D. Single Axon Excitatory Postsynaptic Potentials in Neocortical Interneurons Exhibit Pronounced Paired Pulse Facilitation. Neurosci. 1993, 54, 347-360.

2. Buhl, E. H.; Halasy, K.; Somogyi, P. Diverse Sources of Hippocampal Unitary Inhibitory Postsynaptic Potentials and the Number of Synaptic Release Sites. Nature 1994, 368, 823-828.

3. Mattson, M. P.; Kater, S. Excitatory and Inhibitory Neurotransmitters in the Generation and Degeneration of Hippocampal Neuroarchitecture. Brain Res1989, 478, 337-348.

4. Root, D. H.; Mejias-Aponte, C. A.; Zhang, S.; Wang, H.-L.; Hoffman, A. F.; Lupica, C. R.; Morales, M. Single Rodent Mesohabenular Axons Release Glutamate and GABA. Nat. Neurosci. 2014, 17, 1543-1551.

5. Ganguly, K.; Schinder, A. F.; Wong, S. T.; Poo, M.-m. GABA Itself Promotes the Developmental Switch of Neuronal Gabaergic Responses from Excitation to Inhibition. Cell 2001, 105, 521-532.

6. Kazemi, H.; Hoop, B. Glutamic Acid and Gamma-Aminobutyric Acid Neurotransmitters in Central Control of Breathing. J. Appl. Physiol. 1991, 70, 1-7.

7. Ben-Ari, Y. Excitatory Actions of Gaba During Development: the Nature of the Nurture. Nat. Rev. Neurosci.2002, 3, 728-739.

8. Arthur, J.; Boahen, K. Learning in Silicon: Timing is Everything. Adv. Neural. Inf. Process Syst. 2006, 18, 75.

9. 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. 2006, 17, 211-221.

10. Seo, J.-s.; Brezzo, B.; Liu, Y.; Parker, B. D.; Esser, S. K.; Montoye, R. K.; Rajendran, B.; Tierno, J. A.; Chang, L.; Modha, D. S. In A 45nm CMOS Neuromorphic Chip with a Scalable Architecture for Learning in Networks of Spiking Neurons, IEEE Custom Integr. Circuits Conf. (CICC), IEEE: 2011; pp 1-4.

11. Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.; Cassidy, A. S.; Sawada, J.; Akopyan, F.; Jackson, B. L.; Imam, N.; Guo, C.; Nakamura, Y. A Million Spiking-Neuron Integrated Circuit with a Scalable Communication Network and Interface. Science 2014, 345, 668-673.

12. Qiao, N.; Mostafa, H.; Corradi, F.; Osswald, M.; Stefanini, F.; Sumislawska, D.; Indiveri, G. A Reconfigurable on-Line Learning Spiking Neuromorphic Processor Comprising 256 Neurons and 128K Synapses. Front. Neurosci. 2015, 9, 141.

13. Kim, S.; Ishii, M.; Lewis, S.; Perri, T.; BrightSky, M.; Kim, W.; Jordan, R.; Burr, G.; Sosa, N.; Ray, A. In NVM Neuromorphic Core with 64k-Cell (256-By-256) Phase Change Memory Synaptic Array with on-Chip Neuron Circuits for Continuous In-Situ Learning, IEEE Int. Electron Devices Meet. (IEDM), IEEE: 2015; pp 17.1. 1-17.1. 4.

14. Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett. 2010, 10, 1297-1301.

15. Chang, T.; Jo, S.-H.; Lu, W. Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor. ACS Nano 2011, 5, 7669-7676.

16. Ohno, T.; Hasegawa, T.; Tsuruoka, T.; Terabe, K.; Gimzewski, J. K.; Aono, M. Short-Term Plasticity and Long-Term Potentiation Mimicked in Single Inorganic Synapses. Nat. Mater. 2011, 10, 591-595.

17. Sangwan, V. K.; Jariwala, D.; Kim, I. S.; Chen, K.-S.; Marks, T. J.; Lauhon, L. J.; Hersam, M. C. Gate-Tunable Memristive Phenomena Mediated by Grain Boundaries in Single-Layer MoS2Nat. Nanotechnol. 2015, 10, 403-406.

18. Wang, Z. Q.; Xu, H. Y.; Li, X. H.; Yu, H.; Liu, Y. C.; Zhu, X. J. Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous Ingazno Memristor. Adv. Funct. Mater. 2012, 22, 2759-2765.

19. Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B.; Adam, G.; Likharev, K. K.; Strukov, D. B. Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors. Nature 2015, 521, 61-64.

20. Wang, Z.; Joshi, S.; Savel’ev, S. E.; Jiang, H.; Midya, R.; Lin, P.; Hu, M.; Ge, N.; Strachan, J. P.; Li, Z. Memristors with Diffusive Dynamics as Synaptic Emulators for Neuromorphic Computing. Nat. Mater. 2017, 16, 101-108.

21. Lai, Q.; Zhang, L.; Li, Z.; Stickle, W. F.; Williams, R. S.; Chen, Y. Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions. Adv. Mater.2010, 22, 2448-2453.

22. Kim, K.; Chen, C. L.; Truong, Q.; Shen, A. M.; Chen, Y. A Carbon Nanotube Synapse with Dynamic Logic and Learning. Adv. Mater.2013, 25, 1693-1698.

23. Shi, J.; Ha, S. D.; Zhou, Y.; Schoofs, F.; Ramanathan, S. A Correlated Nickelate Synaptic Transistor. Nat. Commun. 2013, 4, 2676.

24. Zhu, L. Q.; Wan, C. J.; Guo, L. Q.; Shi, Y.; Wan, Q. Artificial Synapse Network on Inorganic Proton Conductor for Neuromorphic Systems. Nat. Commun. 2014, 5, 3158.

25. Tian, H.; Guo, Q.; Xie, Y.; Zhao, H.; Li, C.; Cha, J. J.; Xia, F.; Wang, H. Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications. Adv. Mater.2016, 28, 4991.

26. Tian, H.; Mi, W.; Wang, X.-F.; Zhao, H.; Xie, Q.-Y.; Li, C.; Li, Y.-X.; Yang, Y.; Ren, T.-L. Graphene Dynamic Synapse with Modulatable Plasticity. Nano Lett.2015, 15, 8013-8019.

27. Yang, Y.; Chen, B.; Lu, W. D. Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity. Adv. Mater. 2015, 27, 7720-7727.

28. Li, L.; Chen, Z.; Hu, Y.; Wang, X.; Zhang, T.; Chen, W.; Wang, Q. Single-Layer Single-Crystalline SnSe Nanosheets. J. Am. Chem. Soc. 2013, 135, 1213-1216.

29. Zhao, S.; Wang, H.; Zhou, Y.; Liao, L.; Jiang, Y.; Yang, X.; Chen, G.; Lin, M.; Wang, Y.; Peng, H. Controlled Synthesis of Single-Crystal SnSe Nanoplates. Nano Res. 2015, 8, 288-295.

30. Bi, G.-q.; Poo, M.-m. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. J. Neurosci. 1998, 18, 10464-10472.

31. Song, S.; Miller, K. D.; Abbott, L. F. Competitive Hebbian Learning through Spike-Timing-Dependent Synaptic Plasticity. Nat. Neurosci.2000, 3, 919-926.

32.  Sjöström, P. J.; Rancz, E. A.; Roth, A.; Häusser, M. Dendritic Excitability and Synaptic Plasticity. Physiol. Rev. 2008, 88, 769-840.

Figure Captions



Figure 1. The BP-SnSe junction synaptic device. (a) Schematic of the BP-SnSe heterojunction synaptic device. The presynaptic input is applied at the silicon bottom gate terminal. The electrode contacting SnSe is grounded. The postsynaptic output is measured at the electrode contacting BP. Vbias is applied between BP and SnSe and the voltage Vg is applied between the input terminal and SnSe. (b) The schematic of a biological synapse that can co-release excitatory and inhibitory neurotransmitters. (c) The STEM image, EDX line profile and EDX mapping of the BP-SnSe junction device cross-section.  

Figure 2. Tunable characteristics of the BP-SnSe heterojunction. (a) The schematic of the BP-SnSe junction. (b) The simulated band profiles at the junction between the BP and SnSe layers along the vertical direction indicated by the letter B for Vg=-20 V, 0 V and 20 V, respectively. (c) The simulated band profile at the junction between the BP under the BP-SnSe junction and that outside the junction along the lateral direction indicated by the letter A for Vg= -20 V, 0 V and 20 V, respectively. (d) The Id-Vbias characteristics of the device at different Vg, showing the rectifying characteristics of the BP-SnSe heterojunction that is reconfigurable between p-p, i-p and n-p junction types depending on the bias condition.


Figure 3. Excitatory and inhibitory responses reconfigurable at both the presynaptic and postsynaptic terminals. (a) The magnitude of the current measured at the electrode contacting BP for different Vbias and Vg, plotted in logarithmic scale. Positive pulses applied at the input terminal will lead to the injection of electrons into the phosphorus oxide layer. For regions with the magnitude of the current increasing with Vg, the synaptic response will be excitatory. For regions with the magnitude of the current decreasing with Vg, the synaptic response will be inhibitory. The Id-Vg characteristics can be classified into three regimes based on the different junction types, i.e. p-p, i-p and n-p. The current map can also be divided into four operation quadrants based on the horizontal ridge of zero Vbias and the diagonal ridge joining the points of minimum conductivity, both marked with the yellow dashed lines. (b) The PSC in response to a 20 V input pulse at the input terminal for four different bias conditions corresponding to the points 1 to 4 in (a).

Figure 4. Potentiation, depression and STDP for both the excitatory and inhibitory synaptic response modes. The weight change of the BP-SnSe synapse under positive (10 ms 20 V pulses spaced at 90 ms apart) and negative (10 ms -20 V pulses spaced at 90 ms apart) input pulse trains for (a) the excitatory response at Vg=10 V and Vbias=2 V and (b) the inhibitory response at Vg=10 V and Vbias=-4 V. The STDP characteristics for (c) the excitatory response mode and (d) the inhibitory response mode at the corresponding bias conditions in (a) and (b), respectively.

Figure 5. Tuning the synaptic responses. (a) The synaptic weight changes in response to a 20 V input pulse at different Vg bias conditions for Vbias=2 V and -4 V. (b) Tunable strengths of the excitatory and inhibitory synaptic responses mimicked by the device with different bias conditions at the presynaptic and postsynaptic terminals.

Table of contents

EssayHub’s Community of Professional Tutors & Editors
Tutoring Service, EssayHub
Professional Essay Writers for Hire
Essay Writing Service, EssayPro
Professional Custom
Professional Custom Essay Writing Services
In need of qualified essay help online or professional assistance with your research paper?
Browsing the web for a reliable custom writing service to give you a hand with college assignment?
Out of time and require quick and moreover effective support with your term paper or dissertation?