Flexible Neuromorphic Architectures Based on Self-Supported Multi

Subscriber access provided by University of South Dakota

Organic Electronic Devices

Flexible Neuromorphic Architectures Based on Self-Supported Multi-Terminal Organic Transistors Ying Fu, Ling-An Kong, Yang Chen, Juxiang Wang, Chuan Qian, Yongbo Yuan, Jia Sun, Yongli Gao, and Qing Wan ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b07443 • Publication Date (Web): 16 Jul 2018 Downloaded from http://pubs.acs.org on July 16, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 22 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

ACS Applied Materials & Interfaces

Flexible Neuromorphic Architectures Based on Self-Supported Multi-Terminal Organic Transistors Ying Fu,1 Ling-an Kong,1 Yang Chen,1 Juxiang Wang,1 Chuan Qian,1 Yongbo Yuan,1 Jia Sun,* 1 Yongli Gao, 1,3 and Qing Wan2* 1

Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, P. R. China 2

School of Electronic Science & Engineering, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China. 3

Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627, USA

Corresponding Author: J. Sun (*E-mail: [email protected])；；Q. Wan (*E-mail: [email protected]);

ABSTRACT: Because of the fast expansion of artificial intelligence, development and applications of neuromorphic systems attract extensive interest. In this paper, highly interconnected neuromorphic architecture (HINA) based on flexible self-supported multi-terminal organic transistors is proposed. Au electrodes, poly(3-hexylthiophene) (P3HT) active channels and ion-conducting membranes were combined to fabricate the organic neumorphic devices. Especially, free-standing ion-conducting membranes were used as gate dielectric as well as support substrates. Basic neuromorphic behavior and four forms of spike-timing-dependence-plasticity (STDP) were emulated. Fabricated neuromorphic device showed excellent electrical stability and mechanical flexibility after 1000 bends. Most importantly, the device structure is interconnected in a way similar to the neural architecture of the human brain, and realizes not only the structure of the multi-gate but also characteristics of the global gate. Dynamic processes of memorizing and forgetting were incorporated into the global gate matrix simulation. Pavlov's learning rule was also simulated by taking advantage of the multi-gate array. Realization of HINAs would open a new path for flexible and sophisticated neural networks. KEYWORDS: Flexible neuromorphic devices, self-supported multi-terminal organic transistors, highly interconnected architectures, STDP, memorizing and forgetting, Pavlov's learning rule.

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

Page 2 of 22

INTRODUCTION Because of their flexibility, ductility, high efficiency and low-cost manufacturing processes, flexible electronic devices are considered as emerging electronic technologies

for

information,

bio-medicine,

defense

and

other

fields.1-18

Developments of flexible electronic skin,4, 7-9 human-machine interface7, 19 and soft robots6, 8, 20 also rely on flexible electronic technology. Mass production of flexible devices might soon revolutionize electronic and information technologies, therefore they attract widespread attention all over the world and develop rapidly. Flexible intelligent systems can be integrated into variety of sensors5, 7, 9 and electronic devices, and they can also convert external stimuli into processible electrical signals. However, processing a large amount of input signals in large-scale-sensor-integrated intelligent systems is still a big challenge. Hardware implemented neuromorphic processing along with neural network architectures can solve this drawback.21, 22 Inspired by biological neural systems, neuromorphic devices provide a feasible solution for the rapid development of artificial intelligence.23 Human brain had large number (1011) of highly interconnected neurons (HIN), among which the vast majority of neurons are interconnected with at least 103 or 104 of other neurons.24 HIN architecture (HINA) provides our brains with complex functions such as memory, learning, and consciousness.21, 25 The learning process of the biological brain adapts to changes in the intensity of connection between HIN and external stimulus signals. Human memory and learning skills are not affected by the death of some neural cells because brain stores information in a number of processing units as well as in their interconnections.26, 27 Some transistor devices have been used to simulate artificial neurons. However, these devices generally showed multi-inputs to one-output28-31 or one-input to multi-outputs32-35 structures, which are different from actual neurons. In addition, the main functions of the brain are learning and memory,18, 36-38 both of which are significant. There are many devices that simulate basic tasks of synapses such as synaptic plasticity,30, 36, 39-45 but learning and memory functions have never been achieved in HINAs.


Page 3 of 22 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


Because of the irreversible electrochemical doping during operation,46 organic electrochemical transistors (OECTs) are considered to be a good class of non-volatile memories.37,47 In this paper, we successfully fabricated flexible and multi-terminal OECTs by the ion-gel membrane used as both electrolyte and substrate. The fabricated devices showed HINA with interrelated multi-inputs and multi-outputs. Four forms of spike-timing-dependence-plasticity (STDP) were emulated, which are believed to be the key of the brain learning and storing functions. By taking advantage of non-volatile behavior, conversion from short-term to long-term memory was implemented in a global gate matrix simulation. Pavlov’s learning rule was also simulated in multi-gate arrays. Our results indicated that the HINA is very promising to further develop neuromorphic system for complex signal processing.

RESULTS AND DISCUSSION

Figure 1. HINA based on self-supported multi-terminal OECTs. (a) Schematic of a biological neural structure. The insert is a schematic of a biological synapse. (b) Schematic of highly interconnected neural devices with ion-gel membrane as both electrolyte and a substrate. Interconnected multi-inputs to multi-outputs architectures are shown with yellow rectangular prisms. (c) Photograph of the flexed device. (d) Schematics of an artificial neural network. (e) Detail structure of a single self-supported OECT (schematically shown in Figure 1b). (f)



Page 4 of 22

Experimental data example showing P3HT absorption peak at ~550 nm at -3V gate voltage for applied for 40 s.

Figure 1a shows a schematic of a synaptic network connecting two neurons. Pre-synaptic neurons collect a large number of local stimulation signals through the dendrites and then transmit them from pre-neurons to post-neurons.30 3D cartoon of artificial neural arrays architecture based on P3HT OECTs array is shown in Figure 1b. Source/drain (S/D) and gate (G) electrodes were deposited on the P3HT and the ion-gel

membrane,

respectively. Crystallinity

and

surface

morphology

of

P3HT/ion-gel and ion-gel film were also checked (see Figures S1 and S2, respectively). These films are generally smooth with just some roughness, which is useful for device operation.48-52 Optical microscope images of neural device with global gate and multi-gate are displayed in the Figure S2a. The P3HT with S/D electrodes is considered as post-neuron arrays (Snm) and G electrodes (Ixy) as dendrites of pre-neuron arrays, used for collecting stimulus signal. This device simulates the HINs in human brain and can receive signals from multiple directions and transmit signals to other neurons. The difference between the channel current before the first pulse (E0) and after the n-th pulse (En) is a change of synaptic weight (∆W = En - E0). Photograph of the flexed device is shown in Figure 1c. Figure 1d shows schematics of the artificial neural-network constructed with HINA. Ixy values are the input signals, Snm represent the post-neurons and Wn indicates the ∆W of the corresponding neuron after receiving the signal. This artificial neural-network structure based on the proposed flexible HINA is a reasonable strategy for the neuromorphic engineering. All in-plane input terminals can be coupled with the postsynaptic output terminals because of the ion migration. One neuron can receive many input signals and each input signal can also be transmitted to multiple neurons. If there is only one input signal sent to the neuron, the output current generated by the neuron and the corresponding ∆W will be relatively small. However, if there are multiple input signals sent to the same neuron, the output current and the corresponding ∆W will be higher. If an input signal is sent to neuron S1 as well as


Page 5 of 22 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


neuron S2, both will generate the corresponding output and storing retention currents in the neurons. Even if the output current of one neuron disappears, the weight of memory triggered by this signal will not be completely erased from the neural system, which reflects the brain capacity for fault tolerance.35 This phenomenon is very similar to the learning and memory functions of the brain. This flexible neuromorphic devices exhibit novel configuration for constructing highly interconnected neural networks. Figure 1e shows single OECT schematically enlarged from Figure 1b. Because of the electrochemical doping between P3HT and ion-gel, a non-volatile current response is obtained after removing the gate voltage (Vg). In-situ absorbance spectrum of the OECT is presented in Figure S3. Figure 1f shows absorption peak at 550 nm as a function of time. By applying Vg, absorbance intensity decreases rapidly and remained at the same level at constant Vg (-3V as shown in Figure 1f). When the voltage is no longer applied, the absorbance slowly increases but is not able to return to its initial value. This is a strong evidence of the non-volatile features.



Figure 2. Synaptic plasticity and STDP implementation. (a) Vds dependent current. (b) PPF ratio plotted as a function of time difference (∆t) between two consecutives pre-synaptic spikes. Circles and squares indicate data at Vds equal to -1.2 V and -0.5 V, respectively. (c) Asymmetric STDP learning rule. ∆W is plotted as a function of time difference (∆T) between the presynaptic and postsynaptic spikes. (d-f) Three other STDP forms observed in our device. Red lines show fitting of the experimental data.


Page 6 of 22

Page 7 of 22 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


Transfer and output characteristic curves of the device are shown in Figures S4a and S4b, respectively. When the presynaptic spike (Vpre) is equal to -2 V, the largest hysteresis is observed. Therefore, in order to demonstrate the non-volatile effect better, the initial Vpre of the subsequent test is set to -2 V. Figure 2a shows Vds-dependent current response with constant spike (at Vpre=-5 V and Tp = 50 ms) in the same device. A large Vg (-5 V) is applied to the gate electrode, resulting in irreversible electrochemical doping. After spiking, the current does not dissipate for over 60 s. When the Vds changes from -0.5 V to -1.2 V, the current peak values increase from -23.6 to -52.9 µA. In addition, the ∆W values also increase from -0.5 to -18.8 µA. As the Vds increases, more carriers are injected the channel, resulting in ∆W increase. It indicates that our neural devices can realize non-volatile synaptic behavior and the synaptic weight can be changed by local input of Vds. Figure 2b shows the ∆t dependent decay of paired-pulse facilitation (PPF) ratio (100%×A2/A1). A typical paired-pulse synaptic response is shown in Figure S5. Obviously, when the Vds is equal to -1.2 V, the highest PPF ratio reaches 154.5% at ∆t = 200 ms. As the ∆t increases to 2000 ms, the PPF index drops to 117%. Thus, the PPF obtained at Vds = -1.2 V is larger than that obtained at Vds = -0.5 V. The channel area of the device can affect the energy consumption of synaptic device, as shown in Figure S6b. When the device channel area is 0.026 mm2, the lowest energy consumption of the device is 510 pJ, which is close to that of traditional CMOS circuit (equal to 900 pJ). It is known that the energy consumption of actual biological synapses is at fJ level. As shown Table S1, energy consumption of other organic synaptic devices is comparable with the actual biological synapses. Thus, further investigation and optimization is needed to study energy consumption decrease for our organic neuromorphic devices. STDP represents an important synaptic modification rule, which has the potential to transform the time information in neural networks into memory storage. STDP can be effectively implemented by adjusting the timing of input and output,39-43,

45, 53

which can be divided into two types - asymmetric and symmetric learning. The asymmetric one is when the synapse weight is determined by the time difference (∆T)



between the post- and pre-synaptic spike and timing order of stimulation of the two spikes. The symmetric one is when the synaptic weight only relates to ∆T and not the timing order. Figures 2(c-f) show ∆W plotted as a function of the ∆T between the V before the spike, Vpre (Vg pulse), and V after the spike, Vpost (local input Vds pulse). The Vpre and Vpost are used as input and the control bias. Images of spike setting for the four forms of STDP measurement are shown in Figures S7(a-d). During the measurement of asymmetric STDP functions (shown in Figure 2c), Vpost consists of two right-angled triangular spikes: the left spike changes from 0.2 V to 2 V and then the right one from -2 V to -0.2 V (Figure S7a). The voltage step is 0.2 V. Vpre is a rectangular wave spike with a Tp of 50 ms and amplitude of -5 V. When the presynaptic peak is triggered before the postsynaptic peak of 2 V, the ∆T is positive and ∆W gradually decreases with the increasing of ∆T. When the presynaptic peak is triggered after the postsynaptic peak at -2 V, the ∆T is negative and the direction of ∆W is reverse modulated. In both cases, the greatest change in ∆W occurs when ∆T is small. As ∆T becomes close to zero, dramatic changes in ∆W can be seen. According to this algorithm, ∆T is set to the size of control bias for STDP modulation. Similarly, the other three forms of STDP can be also realized by using different waveforms (Figure S7b-d). Successful implementation of STDP in HINAs has provided a novel working model for the development of neuromorphic systems.


Page 8 of 22

Page 9 of 22 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


Figure 3. Flexibility and stability of the devices. (a) 3D-scatter plot describing the average current (at Vds = -0.5 V, Vpre = -5 V, Tp = 50 ms) as a function of bending radius and gate-to-channel distance. (b) 3D-scatter plot describing the average current as a function of radius and Tp. (c) Average current plotted as a function of bends. (d) Potentiation (Vds = -0.5 V, Vpre = -3 V, Tp = 50 ms, ∆t=2.5 s) and depression (Vds = -0.5 V, Vpre = 2.5 V, Tp = 50 ms, ∆t = 2.5 s) in the flexible device without bending. Each point is the current value at every 2.5 s. The inserts present enlarged views of each current curve. (e) A contrast diagram of the potentiation/depression between currents at 0 bends and after 1000 bends.

Flexibility and stability are very important for flexible devices.3 Thus, we tested the bend deformation as function of the current in our devices. Figure 3a shows a 3D scatter plot that describes the average current (Vds = -0.5 V, Vpre = -5 V, Tp = 50 ms) as a function of bending radius and gate-to-channel distance. Each point represents an average of five measurements. When the device is in a different bend deformation state, the closer the gate to the channel, the larger the current is. As shown in Figure



Page 10 of 22

S8(a), capacitance response shows similar behavior. However, there is almost no current change with different bending radii at the same distance. Figure 3b shows a 3D scatter plot that describes the average current as function of the bend radius and Tp. It is clearly observed that the current is increased with the Tp, however, current of the device is changed only slightly at the same Tp at small radii. As shown in Figure 3c, the average current is almost unchanged even after 1000 bends with 3 cm radius. The green error bars and pink error bars indicate the fluctuations. The data points represent an average value of the current. These results clearly demonstrate that the synaptic response is unaffected by the bending deformation and the device shows excellent flexibility and stability. Potentiation and depression behavior is realized in the device without bending, as demonstrated in Figure 3d. Each point is the current value (I) at 2.5 s pulse duration. Initially, I is relatively low (-5.8 µA). After 40 consecutive pulses (at Vds = -0.5 V, Vpre = -3 V, Tp = 50 ms, ∆t=2.5 s), I gradually increases because of the electrochemical doping, which could be used to mimic the potentiation of the synaptic strength. Afterward, under the negative pulses (Vds = -0.5 V, Vpre = 2.5 V, Tp = 50 ms, ∆t=2.5 s), negative ion is slowly restored to its equilibrium position, decreasing I gradually to its initial value as the pulse number increases. This corresponds to the depression of the synaptic strength. Figure 3e is a contrast diagram of the potentiation/depression

of

the

device

at

0

and

1000

bends.

The

potentiation/depression obtained by applying a series of consecutive pulses to the device is not affected by the bending. The purpose of three repeated tests was to demonstrate that our devices have good repeatability and stability. Therefore, synapses devices based on P3HT OECTs have good cycle tolerance and stability characteristics.


Page 11 of 22 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


Figure 4. Highly interconnected neuromorphic architectures. (a) Schematic of neural device with 3×3 presynaptic array with multi-inputs to one-output. (b) 3D current mapping when the Vpre spikes are applied at 9 different gate positions and (c) its corresponding ∆W mapping. (d) Schematic of the neural structure consisting of 3×3 postsynaptic array with one-input to multi-outputs. Au electrode on the ion-gel film is used as a global input gate. (e-f) 3D current and ∆W mapping, obtained from the output signal at 9 different post neuron positions.

Information processing in the brain takes place in the neuron network interconnected by a large number of synapses and neurons.32, 54 A neuron can receive synaptic information from different directions and the global gate can regulate the entire network. Both of these aspects are very important to the overall function. A local input of Vds (= -0.5 V) is applied and the resulting current value is registered. Figure 4a shows a diagram of a neural device consisting of 3×3 presynaptic array with multi-inputs to one-output. In this array four successive Vpre spikes (at Vpre = -5 V, Tp = 50 ms, ∆t = 50 ms) are applied to each gate electrode. Prior to the recording a data point, the device memory is erased to the initial state. Corresponding current peak values and ∆W are plotted as shown in Figures 4b and 4c. Current peak and ∆W values are strongly related to the gate position. This behavior is similar to the synaptic response of real biological neurons. Figure 4d shows a diagram of neural device consisting of 3×3 postneuron array



Page 12 of 22

with one-input to multi-outputs. All P3HT channels are in contact with a common ion-gel membrane and Au gate electrode is used as a global input. In the 3 × 3 postsynaptic array, four successive Vpre spikes (at Vpre = -5 V, Tp = 50 ms, ∆t =50 ms) are applied to the global gate electrode. Figures 4(e-f) show 3D current and ∆W mapping, demonstrating that Vpre spikes applied on the global gate could trigger the current at different positions simultaneously. The amplitude is related to gate-to-channel distance. These results demonstrate that the HINA provides a great advantage for the realization of the complex neural network.

Figure 5. (a-d) Pavlov's Learning: (a) Results of Pavlov’s learning. (b) Time difference between training spike applied at G1 and G2 (∆T) as function of ∆Wpeak. (c) ∆Wpeak at different modes of training pulse signals at G1 and G2 electrodes. Meaning of the modes: O-R: only ringing; O-F: only food; R-F: ringing-food). (d-e) Dynamic processes of memorizing and forgetting: (d) Dynamic process of memory and forgetfulness of the two images displayed, controlled by the local input. It is simulated by a 3×3 postsynaptic array with a global gate input. The diagram shows that the capital letter “H” consists of a capital letter “I” and a rotated uppercase “T”. The red pattern of “I” is obtained by applying Vds = -0.5 V. The green pattern of “T” is obtained by applying Vds = -1.2 V at the corresponding postsynaptic sites. (e) Memorizing and forgetting evolution processes from capital “H” to rotated capital “T”.

By taking advantage of HINAs, we demonstrated implementation of classical conditioning according to the Pavlov's learning rule.18,

55

Very recently, Pavlov's

learning behavior was demonstrated in memristor and transistors. Wu et al.


Page 13 of 22 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


demonstrated that the classical conditioning can be emulated using a single memristor.18 Sufficient pairing stimulation with positive and negative bias voltages, acting as unconditioned and conditioned stimuli, were used to trigger a conditioned response. John et al. also emulated classical conditioning by simultaneously using paired stimulation of unconditioned optical and conditioned voltage pulses, and proposed a mechanism of the associative learning behavior between optical and electrical spikes.55 By taking the advantage of high interconnected multi-terminal devices, classical conditioning in our work is simulated in multi-gate devices. For the simulation of Pavlov's learning, four -3 V pulse signals simulate the “bell ringing” while four -5V pulse signals simulate the “sight of food”. A simple diagram is demonstrated in Figure S9. In the first stage, the input signal G2 (corresponding to the unconditioned stimulus of the bell ringing) lead to a slight increase in ∆Wpeak from output neurons, but the input signal G1 (corresponding to the conditioned stimulus of the sight of food) resulted in significant increase of ∆Wpeak for output neurons (which corresponds to salivation). In the training stage, input signals were simultaneously applied to the G1 and G2 electrodes to cause large changes in ∆Wpeak of the device. After the training, the ∆Wpeak of the device could also be significantly increased when the input signal only is applied to the G2 electrode. This is analogous to the puppy salivating when the bell rings. The result of our simulated learning is shown in Figure 5a. The blue line indicates ∆W for each output. The third graph (blue-filled part) is the training process. In order to distinguish whether our multi-terminal synaptic device learned to associate the food signal with the ringing signal, a threshold of 45 µA was defined (expressed with the green dotted line in Figure 5a). First, four -3 V pulse signals (at Tp = 50 ms and ∆t = 50 ms) were applied to the G2 electrode to simulate the “bell ringing”. Smaller value of ∆Wpeak comparing to the defined threshold (which correspond to absence of salivary responses) was produced. When four -5V pulse signals (at Tp = 50 ms and ∆t = 50 ms) were applied to the G1 electrode to simulate the “sight of food”, corresponding ∆Wpeak was larger than the defined threshold (which corresponds to



Page 14 of 22

salivary responses). The spontaneous attenuation of ∆W occurs which drops quickly in the initial phase and then decreases gradually. Four -3 V and -5 V pulse signals (Tp = 50 ms and ∆t = 50 ms) were applied simultaneously to the G1 and G2 electrodes to simulate training. After training, ∆Wpeak larger than the defined threshold (salivary response) is clearly produced even with four -3 V pulse signal inputs (bell ringing). Therefore, an effective link between the input signals applied to the G1 and G2 electrodes

was

established,

demonstrating

that

our

highly

interconnected

multi-terminal neural devices can learn to associate four -5V pulse signals with four -3 V pulse signals. Figure 5b shows the effect of the time difference (∆T) between food spike signals (G1) and ringing spike signals (G2) on learning outcomes during training. The ∆Wpeak is obtained when the bell ringing signal is only heard after training. According

to the threshold value defined above, it can be deduced that the learning ability of the device is realized when the ∆T between a food signal and a ringing signal is less than 4 s, as shown in green-shaded region in Figure 5b. To clearly demonstrate the effect of "sight of food" on the learning outcome of "bell ringing", we compared ∆Wpeak in different training modes: only ringing (O-R), only food (O-F) and ringing- food (R-F). It can be seen from Figure 5c that ∆Wpeak can be above the defined thresholds only in the R-F mode, enabling the device to associate food signals with the ringing signals. It indicates that stronger simulations of “sight of food” and “bell ringing” are simultaneously needed for the association learning. Based on the transition of synaptic behaviors controlled by the local inputs and the global gate modulation ability, the dynamic process of memory and forgetting are simulated by a 3×3 postsynaptic array with a global gate input (Figure 4d). The diagram in Figure 5d shows that the capital letter “H” consists of a capital letter “I” and a rotated uppercase “T”. The red pattern of “I” is obtained by applying a local input Vds of -0.5 V and a green pattern of “T” by applying a local input Vds of -1.2 V at the corresponding postsynaptic sites. The responses are different when the two ACS Paragon Plus Environment

Page 15 of 22 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


types of local input Vds are applied to a single device array. By applying Vpre spike of -5 V (Tp = 50 ms) on the global gate, the pattern of “I” and inverted “T” showed short-term and long-term memory behavior, respectively. The corresponding result is shown in Figure 5e. The memory information of different stages is represented by the current value of the corresponding device. At first, both the letter “I” and the rotated letter “T” have a high conductance, which can form the pattern of capital letter “H”. However, with time the inverted letter “T” gradually becomes opaquer, indicating that “T” was stored in long-term memory and “I” was stored in short-term memory. For the mapping of memory and forgetting in Figures 5d and 5e, the input is given sequentially, and each output is recorded by constructing the pixels. As indicated in Figure S10, after we changed different local input for the measurement, higher Vds make the pattern “I” clearer. However, the pattern “I” is less clear than the rotated letter “T” even at the same local input of Vds, which is related to the gate-to-channel-distance dependent synaptic response. The 3×3 postsynaptic array with a global gate spike successfully simulated that the uppercase letter “H” evolves into a rotated uppercase letter “T”, which effectively proved that the neuron device array can be used to realize relatively complex neural network functions.

CONCLUSION We proposed a HINA with flexible self-supported multi-terminal OECT, which is very similar to the neural network of human brain. The fabricated devices exhibited excellent flexibility and electrical stability after 1000 bends. Important synaptic functions of STDP and potentiation/depression with good repeatability were achieved. By taking advantage of HINA, conversion of memory and forgetting was implemented in a global gate matrix simulation, and the Pavlov’s learning rule was simulated in multi-gate devices. Our results indicated that the realization of HINAs would open a new path for flexible neumorphic systems, such as, electronic skin, human-machine interface and soft robots.



Page 16 of 22

ASSOCIATED CONTENT Supporting Information XRD spectra, Optical microscope image, AFM and SEM images, Vis/NIR absorption spectrum, PPF characteristics, transfer characteristic curve and output characteristic curves of P3HT-based OECT, the low power consumption versus device size, waveforms of Vpre (pre-neuron spike) and Vpost (post-neuron spike) for achieving asymmetric and symmetric STDP functions, capacitance versus frequency, schematic of Pavlov’s learning rule, matrix simulations of the globe gate in a 3 × 3 neural array.

ACKNOWLEDGEMENTS This work was supported by the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61425020), the National Natural Science Foundation of China (Grants No. 61306085, 11334014) and the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3679). Y. F. acknowledges support by the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2018zzts358). Y. G. acknowledges support by National Science Foundation (Grant No. CBET-1437656).

EXPERIMENTAL SECTION Materials.

Poly(3-hexylthiophene)

(P3HT)

and

Poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-co-HFP) with MW = 130 000 g/mol were purchased from Sigma-Aldrich. 1-Ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [EMI][TFSA] was purchased from TCI Chemicals. Solution preparation. P3HT was dissolved into dichlorobenzene to obtain 15 mg/mL solution. By dissolving of PVDF-co-HFP and [EMI][TFSA] into acetone at


Page 17 of 22 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


[EMI][TFSA]:acetone = 1:4:7 (by weight) ratio, the ion gel solution was obtained. Then, the ion gel solution was placed on a magnetic heating agitator at 60 °C and a 500 rpm stirring speed for 6 hours. Device fabrication. In order to eliminate the organic matter and improve the wettability, the glass substrate was first wiped by a cotton swab with alcohol. The substrates were continuously cleaned by acetone, de-ionized water and isopropyl alcohol for 20 minutes, and dried by N2 gas stream. Then, the substrate was treated by UV-Ozone for 20 minutes. The free-standing ion-gel membrane was prepared on glass substrate by spin-coating at 500 rpm for 3 s. To remove the residual solvent, ion-gel membranes were dried on a hot plane at 70°C for 2 hours. The P3HT channel layer was spin-coated onto the ion-gel membrane at 500 rpm for 9 s and then at 2000 rpm for 30s. After spin-coating, the P3HT/ion-gel membranes were cured at 50 °C for 2 hours. The source-drain electrodes and in-plane-gate electrodes were fabricated by thermally depositing Au electrodes through a shadow mask. The electrodes on the ion-gel membrane are gate electrodes, and the electrodes on the organic semiconductor serve as source-drain electrodes. Characterization. Electrical properties and neuromorphic functions of the flexible devices were performed by a semiconductor parameter analyzer (Keithley 4200-SCS) connected with a shielded box. The crystallinity of the ion gel was characterized by XRD (DMAX-2500, Rigaku) at 2θ range from 5 to 80°. The surface morphology of the P3HT/ion-gel and ion-gel film was characterized by AFM (from Agilent Technologies) and scanning electron microscopy (SEM, JEOL JCM-5000). The absorption spectra of P3HT OECTs were collected in-situ with an ultraviolet−visible spectrophotometer (UV−vis, Puxi, T9, China). Two Keithley 2400 source meters were used to apply the voltage.

REFERENCES 1.

Eda, G.; Fanchini, G.; Chhowalla, M., Large-area ultrathin films of reduced graphene oxide as a transparent



and flexible electronic material. Nat. Nanotechnol. 2008, 3, 270-274. 2.

Logothetidis, S., Flexible organic electronic devices: Materials, process and applications. Mater. Sci. Eng., B

2008, 152, 96-104. 3.

Sekitani, T.; Zschieschang, U.; Klauk, H.; Someya, T., Flexible organic transistors and circuits with extreme

bending stability. Nat. Mater. 2010, 9, 1015-1022. 4.

Schwartz, G.; Tee, B. C.; Mei, J.; Appleton, A. L.; Kim, D. H.; Wang, H.; Bao, Z., Flexible polymer

transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nat. Commun. 2013, 4, 1859. 5.

Wang, C.; Li, X.; Gao, E.; Jian, M.; Xia, K.; Wang, Q.; Xu, Z.; Ren, T.; Zhang, Y., Carbonized Silk Fabric for

Ultrastretchable, Highly Sensitive, and Wearable Strain Sensors. Adv. Mater. 2016, 28, 6640-6648. 6.

Hines, L.; Petersen, K.; Lum, G. Z.; Sitti, M., Soft Actuators for Small-Scale Robotics. Adv. Mater. 2017, 29,

1603483. 7.

Lei, Z.; Wang, Q.; Sun, S.; Zhu, W.; Wu, P., A Bioinspired Mineral Hydrogel as a Self-Healable,

Mechanically Adaptable Ionic Skin for Highly Sensitive Pressure Sensing. Adv. Mater. 2017, 29, 1700321. 8.

Taube Navaraj, W.; Garcia Nunez, C.; Shakthivel, D.; Vinciguerra, V.; Labeau, F.; Gregory, D. H.; Dahiya, R.,

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin. Front. Neurosci. 2017, 11, 501. 9.

Someya, T.; Sekitani, T.; Iba, S.; Kato, Y.; Kawaguchi, H.; Sakurai, T., A large-area, flexible pressure sensor

matrix with organic field-effect transistors for artificial skin applications. Proc Natl Acad. Sci. U. S. A. 2004, 101, 9966-9970. 10.

Hu, Q.; Wu, H.; Sun, J.; Yan, D.; Gao, Y.; Yang, J., Large-area perovskite nanowire arrays fabricated by

large-scale roll-to-roll micro-gravure printing and doctor blading. Nanoscale 2016, 8, 5350-5357. 11.

Chen, S.; Lou, Z.; Chen, D.; Shen, G., An Artificial Flexible Visual Memory System Based on an

UV-Motivated Memristor. Adv. Mater. 2018, 30, 1705400. 12.

Zhang, Q.; Jiang, T.; Ho, D.; Qin, S.; Yang, X.; Cho, J. H.; Sun, Q.; Wang, Z. L., Transparent and

Self-Powered Multistage Sensation Matrix for Mechanosensation Application. ACS Nano 2018, 12, 254-262. 13.

Wang, H.; Liu, H.; Zhao, Q.; Ni, Z.; Zou, Y.; Yang, J.; Wang, L.; Sun, Y.; Guo, Y.; Hu, W.; Liu, Y., A

Retina-Like Dual Band Organic Photosensor Array for Filter-Free Near-Infrared-to-Memory Operations. Adv. Mater. 2017, 29, 1701772. 14.

Ho, D. H.; Sun, Q.; Kim, S. Y.; Han, J. T.; Kim, D. H.; Cho, J. H., Stretchable and Multimodal All Graphene

Electronic Skin. Adv. Mater. 2016, 28, 2601–2608. 15.

Wu, C.; Kim, T. W.; Choi, H. Y.; Strukov, D. B.; Yang, J. J., Flexible three-dimensional artificial synapse

networks with correlated learning and trainable memory capability. Nat. Commun. 2017, 8, 752. 16.

John, R. A.; Ko, J.; Kulkarni, M. R.; Tiwari, N.; Chien, N. A.; Ing, N. G.; Leong, W. L.; Mathews, N.,

Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired


Page 18 of 22

Page 19 of 22 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


Neuromorphic Computing. Small 2017, 13, 1701193. 17.

Zhang, C.; Tai, Y. T.; Shang, J.; Liu, G.; Wang, K. L.; Hsu, C., Yi, X., ; Yang, X.; Xue, W.; Tan, H.; Guo, S.,

Synaptic plasticity and learning behaviours in flexible artificial synapse based on polymer/viologen system. J. Mater. Chem. C 2016, 4, 3217-3223. 18.

Wu, C.; Kim, T. W.; Guo, T.; Li, F.; Lee, D. U.; Yang, J. J., Mimicking Classical Conditioning Based on a

Single Flexible Memristor. Adv. Mater. 2017, 29, 1602890. 19.

Zang, Y.; Shen, H.; Huang, D.; Di, C. A.; Zhu, D., A Dual-Organic-Transistor-Based Tactile-Perception

System with Signal-Processing Functionality. Adv. Mater. 2017, 29, 1606088. 20.

Bauer, S.; Bauer-Gogonea, S.; Graz, I.; Kaltenbrunner, M.; Keplinger, C.; Schwodiauer, R., 25th anniversary

article: A soft future: from robots and sensor skin to energy harvesters. Adv. Mater. 2014, 26, 149-161. 21.

Stieg, A. Z.; Avizienis, A. V.; Sillin, H. O.; Martin-Olmos, C.; Aono, M.; Gimzewski, J. K., Emergent

criticality in complex turing B-type atomic switch networks. Adv. Mater. 2012, 24, 286-293. 22.

Saighi, S.; Mayr, C. G.; Serrano-Gotarredona, T.; Schmidt, H.; Lecerf, G.; Tomas, J.; Grollier, J.; Boyn, S.;

Vincent, A. F.; Querlioz, D.; La Barbera, S.; Alibart, F.; Vuillaume, D.; Bichler, O.; Gamrat, C.; Linares-Barranco, B., Plasticity in memristive devices for spiking neural networks. Front. Neurosci. 2015, 9, 51. 23.

Chang, Y. F.; Fowler, B.; Chen, Y. C.; Zhou, F.; Pan, C. H.; Chang, T. C.; Lee, J. C., Demonstration of

Synaptic Behaviors and Resistive Switching Characterizations by Proton Exchange Reactions in Silicon Oxide. Sci. Rep. 2016, 6, 21268. 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.

Ohno, T.; Hasegawa, T.; Tsuruoka, T.; Terabe, K.; Gimzewski, J. K.; Aono, M., Nat. Mater. 2011, 10,

591-595. 26.

Howard, D.; Bull, L.; De Lacy Costello, B., Evolving unipolar memristor spiking neural networks. Connect.

Sci. 2015, 27, 397-416. 27.

Turova, T. S., The emergence of connectivity in neuronal networks: from bootstrap percolation to

auto-associative memory. Brain Res. 2012, 1434, 277-284. 28.

Gkoupidenis, P.; Koutsouras, D. A.; Lonjaret, T.; Fairfield, J. A.; Malliaras, G. G., Orientation selectivity in a

multi-gated organic electrochemical transistor. Sci. Rep. 2016, 6, 27007. 29.

Wan, C. J.; Zhu, L. Q.; Liu, Y. H.; Feng, P.; Liu, Z. P.; Cao, H. L.; Xiao, P.; Shi, Y.; Wan, Q.,

Proton-Conducting Graphene Oxide-Coupled Neuron Transistors for Brain-Inspired Cognitive Systems. Adv. Mater. 2016, 28, 3557-3563. 30.

Qian, C.; Kong, L.-a.; Yang, J.; Gao, Y.; Sun, J., Multi-gate organic neuron transistors for spatiotemporal

information processing. Appl. Phys. Lett. 2017, 110, 083302. 31.

Hu, S. G.; Liu, Y.; Li, H. K.; Chen, T. P.; Yu, Q.; Deng, L. J., A MoS2-based coplanar neuron transistor for



logic applications. Nanotechnology 2017, 28, 214001. 32.

Sangwan, V. K.; Lee, H. S.; Bergeron, H.; Balla, I.; Beck, M. E.; Chen, K. S.; Hersam, M. C., Multi-terminal

memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 2018, 554, 500-504. 33.

P. Gkoupidenis; D. A. Koutsouras; Malliaras, G. G., Neuromorphic device architectures with global

connectivity through electrolyte gating. Nat. Commun. 2017, 8, 15448. 34.

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, 4991-4997. 35.

Yu, S.; Gao, B.; Fang, Z.; Yu, H.; Kang, J.; Wong, H. S., A low energy oxide-based electronic synaptic device

for neuromorphic visual systems with tolerance to device variation. Adv. Mater. 2013, 25, 1774-1779. 36.

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. 37.

van de Burgt, Y.; Lubberman, E.; Fuller, E. J.; Keene, S. T.; Faria, G. C.; Agarwal, S.; Marinella, M. J.; Alec

Talin, A.; Salleo, A., A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 2017, 16, 414-418. 38.

O. Bichler; W. Zhao; F. Alibart; S. Pleutin; S. Lenfant; Vuillaume, D., Pavlov's Dog Associative Learning

Demonstrated on Synaptic-Like Organic Transistors. Neural Computation 2013, 25, 549–566. 39.

Kuzum, D.; Jeyasingh, R. G.; Lee, B.; Wong, H. S., Nanoelectronic programmable synapses based on phase

change materials for brain-inspired computing. Nano Lett. 2012, 12, 2179-2186. 40.

Li, Y.; Zhong, Y.; Zhang, J.; Xu, L.; Wang, Q.; Sun, H.; Tong, H.; Cheng, X.; Miao, X., Activity-dependent

synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci. Rep. 2014, 4, 4906. 41.

Shi, J.; Ha, S. D.; Zhou, Y.; Schoofs, F.; Ramanathan, S., A correlated nickelate synaptic transistor. Nat.

Commun. 2013, 4, 2676. 42. Tian, H.; Cao, X.; Xie, Y.; Yan, X.; Kostelec, A.; DiMarzio, D.; Chang, C.; Zhao, L. D.; Wu, W.; Tice, J.; Cha, J. J.; Guo, J.; Wang, H., Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device. ACS Nano 2017, 11, 7156-7163. 43.

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. 44.

Kong, L.-a.; Sun, J.; Qian, C.; Wang, C.; Yang, J.; Gao, Y., Spatially-correlated neuron transistors with

ion-gel gating for brain-inspired applications. Org. Electron. 2017, 44, 25-31. 45.

Tan, Z. H.; Yang, R.; Terabe, K.; Yin, X. B.; Zhang, X. D.; Guo, X., Synaptic Metaplasticity Realized in

Oxide Memristive Devices. Adv. Mater. 2016, 28, 377-384. 46.

Wang, S.; Ha, M.; Manno, M.; Daniel Frisbie, C.; Leighton, C., Hopping transport and the Hall effect near

the insulator-metal transition in electrochemically gated poly(3-hexylthiophene) transistors. Nat. Commun. 2012, 3,


Page 20 of 22

Page 21 of 22 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


1210. 47.

Burr, G. W.; Shelby, R. M.; Sebastian, A.; Kim, S.; Kim, S.; Sidler, S.; Virwani, K.; Ishii, M.; Narayanan, P.;

Fumarola, A.; Sanches, L. L.; Boybat, I.; Le Gallo, M.; Moon, K.; Woo, J.; Hwang, H.; Leblebici, Y., Neuromorphic computing using non-volatile memory. Adv. Phys., X 2016, 2, 89-124. 48.

Hernandez, E. M.; Quintero, C. M.; Kraieva, O.; Thibault, C.; Bergaud, C.; Salmon, L.; Molnar, G.;

Bousseksou, A., AFM imaging of molecular spin-state changes through quantitative thermomechanical measurements. Adv. Mater. 2014, 26, 2889-2893. 49.

Zhang, L.; Yang, Y.; Huang, H.; Lyu, L.; Zhang, H.; Cao, N.; Xie, H.; Gao, X.; Niu, D.; Gao, Y.,

Thickness-Dependent Air-Exposure-Induced Phase Transition of CuPc Ultrathin Films to Well-Ordered One-Dimensional Nanocrystals on Layered Substrates. J. Phys.Chem. C 2015, 119, 4217-4223. 50.

Wu, R.; Yang, J.; Xiong, J.; Liu, P.; Zhou, C.; Huang, H.; Gao, Y.; Yang, B., Efficient electron-blocking

layer-free planar heterojunction perovskite solar cells with a high open-circuit voltage. Org. Electron. 2015, 26, 265-272. 51.

Qian, C.; Sun, J.; Kong, L.-a.; Fu, Y.; Chen, Y.; Wang, J.; Wang, S.; Xie, H.; Huang, H.; Yang, J.; Gao, Y.,

Multilevel Nonvolatile Organic Photomemory Based on Vanadyl-Phthalocyanine/para-Sexiphenyl Heterojunctions. ACS Photonics 2017, 4, 2573-2579. 52.

Xiao, L.; Yuan, J.; Zou, Y.; Liu, B.; Jiang, J.; Wang, Y.; Jiang, L.; Li, Y. f., A new polymer from fluorinated

benzothiadiazole and alkoxylphenyl substituted benzo[1,2-b:4,5-b′]dithiophene: Synthesis and photovoltaic applications. Synth. Met. 2014, 187, 201-208. 53.

Wan, C. J.; Zhu, L. Q.; Zhou, J. M.; Shi, Y.; Wan, Q., Inorganic proton conducting electrolyte coupled

oxide-based dendritic transistors for synaptic electronics. Nanoscale 2014, 6, 4491-4497. 54.

Bullmore, E.; Sporns, O., The economy of brain network organization. Nat. Rev. Neurosci. 2012, 13,

336-349. 55.

John, R. A.; Liu, F.; Chien, N. A.; Kulkarni, M. R.; Zhu, C.; Fu, Q.; Basu, A.; Liu, Z.; Mathews, N.,

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity. Adv. Mater. 2018, 30, e1800220.



Table Of Contents (TOC) graphic


Page 22 of 22

Flexible Neuromorphic Architectures Based on Self-Supported Multi

Recommend Documents