Flexible Electronic Synapses for Face Recognition Application with

Oct 4, 2018 - Here, we present a two-terminal flexible organic synaptic device with ultra-multimodulated conductance states, realizing a face recognit...
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Cite This: ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Flexible Electronic Synapses for Face Recognition Application with Multimodulated Conductance States Tian-Yu Wang,† Zhen-Yu He, Hao Liu, Lin Chen,* Hao Zhu,† Qing-Qing Sun,* Shi-Jin Ding, Peng Zhou, and David Wei Zhang State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China

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S Supporting Information *

ABSTRACT: An artificial synaptic device with a continuous weight modulation behavior is fundamental to the hardware implementation of the bioinspired neuromorphic systems. Recent reported synaptic devices have a less number of conductance states, which is not beneficial for the continuous modulation of weights in neuromorphic computing. Preparing a device with as many conductance states as possible is of great significance to the development of brain-inspired neuromorphic computing. Here, we present a two-terminal flexible organic synaptic device with ultra-multimodulated conductance states, realizing a face recognition functionality with a strong error-tolerant nature for the first time. The device shows an excellent long-term potentiation or long-term depression behavior and reliability after 1000 folded destructive tests. There are 600 continuous ultra-multimodulated conductance states, which can be used to realize the great face recognition capability. The recognition rates were 95.2% and above 90% for the initial and 15% noise pixel images, respectively. The strong error-tolerant nature indicates a potential application of a flexible organic artificial synaptic device with ultra-multimodulated conductance states in the large-scale neuromorphic systems. KEYWORDS: flexible memristor, ultra-multimodulation, biocompatible polymers, face recognition, error-tolerant nature

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INTRODUCTION Compared with the conventional von Neumann systems, the human brain can integrate, learn, and remember complex information with a low power consumption based on a parallel system, which consisted of 1012 neurons and 1015 synapses.1,2 Synapses play important roles in the delivery of signal by the release of neurotransmitters between neurons.3 The synaptic plasticity modulated by the conductance (G) of memories, containing the short-term plasticity (STP), long-term potentiation (LTP), long-term depression (LTD), paired pulse facilitation (PPF) and Hebbian spike timing dependent plasticity (STDP) learning rules, has been widely demonstrated in biological neural networks.4,5 Compared with the conventional von Neumann system, artificial neural networks (ANNs), as new computational architectures, have the potential to improve the efficiency of the work system, decrease the power consumption, and emulate the cognitive process of brain.6,7 Recently, the research attention to the memory with synaptic plasticity has been increased.8−11 The performance of synaptic devices play important roles in neuromorphic computing.12,13 However, the reported artificial synaptic devices have less than 100 modulated conductance statements,14,15 which is not beneficial to realize the continuous modulation of weights in the ANNs. To better mimic the biosynapses and realize the various applications based on neuromorphic computing, fabricating a memory with more continuous modulated conductance statements is an urgent need.16 © 2018 American Chemical Society

Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) is widely used in the quantum dot lightemitting diodes (QLEDs)17 and organic solar cells18,19 because of high transparency, hole transporting ability, and the compatibility of traditional semiconductor processes. However, it is neglected in the field of flexible synaptic memory and neuromorphic computing. The recent reports show that PEDOT:PSS is stretchable, biocompatible, and can be chronically implanted without affecting neuronal function, which makes it an attractive candidate for the synaptic materials20 and its potential applications in flexible electronics.21 The PEDOT:PSS-based memory is worth of a discussion because of the above advantages and the potential to combine with the solar cells and neuromorphic computing. Although the PEDOT:PSS-based binary-resistive random access memory (RRAM) has been studied,22 the realization of face recognition application using the PEDOT:PSS-based synaptic device with ultra-multimodulated conductance states has not been reported. In this work, we design a two-terminal, flexible, biocompatible, PEDOT:PSS-based artificial synapse [Al/PEDOT:PSS/ indium tin oxide (ITO)], emulate the synaptic plasticity with 600 conductance states, and realize its applications in the face recognition with a strong error-tolerant nature. The fabrication Received: September 26, 2018 Accepted: October 4, 2018 Published: October 4, 2018 37345

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces

Figure 1. Artificial flexible synapses based on PEDOT:PSS. (a) Schematic diagram and (b) photograph of the PEDOT:PSS-based synaptic device. The bottom electrode (Al) and the top electrode (ITO) correspond to the post- and preneurons, respectively. (c) Morphology of the schematic diagram of PEDOT:PSS. (d) Fabrication process with the advantages of easy operation and low cost. (e) AFM images (2 × 2 μm2) of the PEDOT:PSS film on a Si substrate in the tapping mode. The Rq of the film is 1.54 nm. (f) XPS S (2p) core-level spectra of the PEDOT:PSS film. The binding energy peaks at about 164 and about 169 eV are originated from the sulfur atom of PEDOT and PSS, respectively.

Figure 2. Electrical behaviors of artificial flexible synapses. I−V characteristics of the memory under six consecutive (a) negative and (b) positive voltage sweeps. The inset shows the increase of postcurrent after each negative sweep. (c) Stable LTP and LTD behaviors of the memory with 600 consecutive negative and positive pulses. (d) Conductance after the series of input pulses with the amplitude of −1 V and interval of 10 s was applied. The flexible synaptic device shows short-term synaptic plasticity. (e) Schematic diagram of a variable oxidation state of the PEDOT induced by hole (h+) injection and extraction corresponding to the forming and rupture of the conductive paths of PEDOT+.

of our artificial synapse. The film of PEDOT:PSS is widely used as a hole transport layer and shows synaptic plasticity, which make the PEDOT:PSS-based flexible artificial synaptic device become a promising candidate for integration into organic light-emittingdiodes and solar cells for multifunctional applications.

process used in this study is low cost, simple, and compatible with a flexible substrate, which can be performed at the temperature lower than 120 °C. Unlike the silicon or glassbased electronic devices with the rigid characteristics, a flexible memory has a potential application in wearable devices.23,24 The 600 continuous statements of G in LTP and LTD show great reliability after 1000 folding destructive test, which laid the foundation for applications in face recognition. Furthermore, STP, PPF, and STDP learning rules were all emulated by a single synaptic RRAM. By updating the synaptic weights, we used 112 images (32 × 32 pixel gray scale) of 14 people from the Yale Face Database25 for training and 42 other face images of these people for performance verification. The successful recognition rate of PEDOT:PSS-based memory was 95.2% using initial untreated testing images, and the rate was always above 90% when there was up to 15% of noise pixels in testing images. The strong error-tolerant nature proves the feasibility

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RESULTS AND DISCUSSION Structure of an Artificial Synaptic Device. The highdensity array of memristors plays an important role in ANNs.26 RRAM with the simple structure of metal−insulator−metal has become one of the strongest candidates for constructing neuromorphic computing systems because of the advantages of high integration, low power consumption, and simple structure.27,28 The schematic diagram and photograph of the fabricated device were presented in Figure 1a,b, respectively. 37346

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces

Figure 3. Reliable synaptic plasticity of the PEDOT:PSS-based device. (a) Consecutive and stable LTP and LTD behaviors containing 10 cycles. Every cycle consisted of 600 clearly different conductance statements. (b) Pair of output spikes of EPSC triggered by two consecutive input pulses (an amplitude of −1.5 V, a duration of 50 ms, and Δt of 20 ms) on the artificial synaptic device. A1 and A2 represent the EPSCs of the input pulses. (c) PPF index determined by the time interval of two input presynaptic pulses (ΔtPPF = tpre2 − tpre1). (d) Hebbian STDP learning rules in the synaptic device. The input pulses of the pre- and postneurons (an amplitude of 1.5 V, a duration of 50 ms, and ΔtSTDP = tpost − tpre) are shown in the inset. The response of the PSC analyzed at (e) different numbers of pulses (10, 20, 50, 100, 200, and 300, an amplitude of −1.5 V/+1 V, and a duration of 50 ms) and (f) different frequencies of pulses (1, 10, 20, and 50 Hz, an amplitude of −1.5 V/+1 V, and a duration of 50 ms). (g) Retention behaviors of 10 conductance states have been demonstrated by different numbers of pulses (0, 1, 10, 20, 50, 100, 150, 200, 250, and 300). (h) Uniformity property of PEDOT:PSS-based synaptic devices. (i) LTP/LTD behaviors of flexible artificial synaptic device after 1000 folded cycles.

top electrode while the Al bottom electrode was grounded, the conductance of the device increased gradually corresponding to the potentiation behavior of synaptic plasticity. After that, the device showed the depression characteristics of biosyanpses with the consecutive positive voltage sweeps (0 → +1.5 V → 0). To demonstrate the behavior of LTP/LTD, we applied 600 consecutive bias pulses containing 300 negative bias pulses (a pulse amplitude of −1.5 V, pulse duration, i.e., a pulse width of 50 ms) and 300 positive bias pulses (a pulse amplitude of +1 V, pulse duration, i.e., a pulse width of 50 ms). After every bias pulse, the read current was recorded with the read voltage of 0.1 V. As shown in Figure 2e, the hole injection (PEDOT0 + h+ → PEDOT+) occurred during the opposite voltage contacting process.31 When the voltage of −1.5 V was applied to the top electrode of the ITO, which was equivalent to positive voltage (+1.5 V) applied to the bottom electrode of the Al, the PEDOT0 turned to the PEDOT+ and accumulated to form the conductive paths. Therefore, G increased gradually with negative pulses applied to the ITO electrode in the synaptic artificial device. Moreover, the hole extracted from PEDOT+ (PEDOT+ → PEDOT0 + h+) occurred during the negative voltage contacting process. The high conductive path of PEDOT+ turned to PEDOT0 with consecutive negative bias pulses and the device showed the LTD behavior. To analyze the asymmetrical current in the positive and negative bias, the curve of Ln(I) and V1/2 under dc sweep was plotted (Supporting Informantion, Figure S2). A linear relationship

The process of fabricating an active neuromorphic memory was suitable for the large area and low-cost applications as shown in Figure 1d. The sandwiched structure of our device was shown in the cross-sectional scanning electron microscope image (Supporting Informantion, Figure S1). For excellent and stable performance, the root-mean-square average roughness (Rq) of PEDOT:PSS spin-coated on a flexible substrate of polyethylene terephthalate (PET)/Al was controlled at 0.86 nm, which was lower than the value of 1.54 nm on the rigid silicon substrate and the value of 2.90 nm on the flexible substrate of PET/ITO (Supporting Informantion, Figure S1). Figure 1c shows the soft PSS matrix and the distributions of the PEDOT:PSS in the film,21 which can affect the conductivity. As shown in Figure 1f, the lower characteristic peaks of S 2p at about 164.1 and 165.4 eV belong to PEDOT. Two higher binding energy peaks belong to PSS. The peak at 169.1 eV corresponds to neutral sulfur, and the peak at 167.8 eV corresponds to ionic sulfur in the PSS dopant, respectively,29 whereas the well-signed peaks of O 1s in PEDOT:PSS appeared at 533 and 531 eV30 (Supporting Informantion, Figure S1). By analyzing the area of the XPS S(2p) spectra, we found that the PSS dopants in the PEDOT had the concentration ratio of 2.33:1. Synaptic Plasticity Behaviors for Neuromorphic Computing. The typical I−V curves of our artificial synaptic device were shown in Figure 2a,b. With the consecutive negative voltage sweeps (0 → −1.5 V → 0) applied to the ITO 37347

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces

Figure 4. Neuromorphic network with an artificial flexible synapsis array. Schematic illustration of (a) parallel read and modulate conductance by changing the number of pulses using a simulated device array, which consisted of 1024 × 256 RRAM as cells. (b) ANN with three layers, containing 1024 input neurons, 256 hidden neurons, and 14 output neurons. (c) Flowchart of the training process, where N = 112 represents the total number of training images, i is in the range 1−1024, j is in the range 1−256, k is in the range 1−14, and these indexes imply the sequence number of the input pixels, hidden neurons, and output results, respectively. Average confusion matrix of training results under different training epochs, (d) 0 epoch, (e) 100th epoch, (f) 200th epoch, (g) 300th epoch, (h) 400th epoch, and (i) 600th epoch. The ordinate denotes 14 input training faces, and the abscissa denotes the output and recognition results.

between Ln(I) and V1/2 could be well fitted with the Schottky thermionic emission model.32 The work functions of Al electrode and ITO electrode are 4.3 and 4.8 eV, respectively. For the Schottky controlled device, the asymmetric electrode structure may result to the asymmetrical conduction in LTP and LTD.33 The artificial synaptic device had a clear difference between different statements of G, as shown in Figure 2c. There were 600 gradually modulated G in total, which can greatly emulate the behavior of biosynapse. The number of statements of G in the device we presented was more than other reports,34 which were suitable for the simulation of pattern recognition.35 The retention time of G after a single input voltage spike was longer than 104 s in both LTP and LTD (Supporting Informantion, Figure S2). To demonstrate the multistates of synaptic device,

10 conductance states induced by different number of pulses (0, 1, 10, 20, 50, 100, 150, 200, 250, and 300) were recorded. The retention time of all 10 states could be longer than 104 s (Figure 3g). The modified G of the artificial synaptic device was corresponded to the variable synaptic weights (i.e., the connecting strength between pre- and postsynapses) and can be modulated by changing the number of applied bias pulses. The consecutive synaptic programming operations were repeated 10 times in one device without obvious degradation (Figure 3a). The similar synaptic behaviors were emulated by 10 different devices (Figure 3h), which proved uniformity of PEDOT:PSS-based devices. The LTP/LTD in a device could be repeated 100 cycles with a certain degree of current degradation (Supporting Informantion, Figure S2). The artificial synaptic device showed the reliability after 100 folded 37348

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces

to the memory in a unit time, the more pronounced of conductance response will be. Neuromorphic Network with PEDOT:PSS-Based Synapses. On the basis of the measured electrical characteristics of the PEDOT:PSS-based flexible device, a three-layer ANN was simulated (Figure 4b). The 1024 input neurons corresponded to the pixels of one image and 14 output neurons corresponded to 14 classes of faces. The supervised learning based on the back-propagation (BP) algorithm was performed using 112 images with 32 × 32 pixels and gray scale from the Yale Face Database.25 The weights were updated in the learning process based on the experimental data from LTP and LTD of our artificial synaptic device. The details of the connections between neurons are shown in Figure 4a. The circuit diagram consisted of 1024 × 256 artificial synaptic devices. The top and bottom electrodes could be connected to the bit line (BL) and the word line, simulating the pre- and postsynapses, respectively. Multilevel pulses were applied to the presynaptic device to modulate the conductance of a specific device, realizing weight updates and the function of face recognition. The ANN training contained two processes: training and validation processes, and the training operation consisted of forward propagation and BP.41 In the training process of forward propagation, 112 face images of 14 people were used as an input to the BL, and the vector of input neurons summed with a linear weight. The input signal of hidden neurons is defined by

destructive operations with a radius of curvature of 0 mm (Supporting Informantion, Figure S3), which provides the possibilities for large-scale applications of the PEDOT:PSSbased flexible memory. To demonstrate the excellent performance of a flexible synaptic device, 1000 times folded operations were applied to the device, and it could still work continuously at least 10 cycles with 6000 pulses (Figure 3i). The excitatory postsynaptic current (EPSC) of the latter pulse was larger than the former one, which is called the PPF and used to decode a temporal information.36 It is organized from the residual Ca2+ excited by the former spike. When the interval between two spikes is shorter than the recovery time of Ca2+, the latter spike could induce a higher concentration of Ca2+ than the former, corresponding to a stronger synaptic strength. PPF can be mimicked using our device with a pair of presynaptic spikes (a pulse amplitude of −1.5 V, a pulse duration of 50 ms, and ΔtPPF = tpre2 − tpre1), which is shown in Figure 3b. The facilitation resulted from the facts that the interval time between two spikes was shorter than the recovery time of the PEDOT+, and more conductive paths of the PEDOT+ were built up for enhancing the device conductance eventually. The PPF index (A2/A1) decreased with the increase of intervals between two pulses. There were no obvious differences between A2 and A1 when ΔtPPF was longer than 500 ms, which can be explained by that PEDOT+ relaxed back to the initial statement resulting to the weak conductance path before the latter voltage spike applied to the device. The synaptic behavior of STP can be emulated by applying a series of voltage pulses with a long-time interval (a pulse amplitude of −1 V, a pulse duration of 50 ms, and a pulse interval of 10 s) as shown in Figure 2d, which plays an important part in a neuronal signal transmission of biosynapses.37 STDP learning rule is one of the long-term memory functionalities.38,39 The temporal interval between pre- and postsynaptic activities is defined as ΔtSTDP = tpost − tpre, whereas the relationship of synaptic weights changing with different ΔtSTDP presented in Figure 3d is fitted. The change of relative synaptic weights (ΔG) can be defined by ΔG =

Gt − G0 × 100% Gt

1024

Ij(n) =

∑ Wijxi(n) i=1

(2)

where xi(n) is the input signal of the first ANN layer containing the pixel of the training face images and Wij denotes the weight between the input neuron i and the hidden neuron j. Then, the result was activated by a nonlinear log-sigmoid transfer function (logsig) function. The activated result of a hidden layer was transferred to the output neurons. With the sum and activation processes, the output signals were obtained:

ij 256 yz j z Ok = logsigjjj∑ Wjkf j (n)zzz jj zz (3) k j=1 { where f j and Ok are the output signals of the second and third layers of ANN, respectively. After the above calculation, the process of the forward propagation was finished. During the BP process, delta weights (ΔWjk) were calculated and transferred to modify the synaptic weights with the input pulses. The altering value of the second-layer synapses was

(1)

where Gt and G0 represent the conductances after and before applying the input spikes. The inset shows the preinput spike (a pulse amplitude of −1.5 V and a pulse duration of 50 ms) and postinput spike (a pulse amplitude of +1.5 V and a pulse duration of 50 ms) applied to our device. The fitting results showed that experimental weights enhanced when ΔtSTDP > 0, which corresponded to the LTP in a biosynapse,40 whereas ΔG decreased with the increase of ΔtSTDP from 10 to 60 ms. In contrast, the behavior corresponded to the LTD in a biosynapse and the weights depressed when ΔtSTDP < 0. The synaptic device can emulate the Hebbian STDP rule as explained above. To investigate the dependence of conductance on the input spikes, different numbers of pulses (Figure 3e) and different frequencies (Figure 3f), amplitudes, and durations were applied to our artificial synaptic device (Supporting Informantion, Figure S2). The recorded EPSC showed that the weights increased with the increase of the pulse number, frequency, amplitude, and duration, in the range of 10−300 pulses, 1−50 Hz, −0.5 to −1.5 V (negative voltage), and 5 to 50 ms, respectively. In short, the more input spikes are applied

ΔWjk = ηf j ek

(4)

where η is the learning rate and ek is the calculated error between the real output and the target output during the training. When the feedback was transferred to the weights of the first layer, an epoch finished. Figure 4d−i showed the average confusion matrices of face classification with 14 different images corresponding to the following numbers of training epoch: 0, 100, 200, 300, 400, and 600 epochs. In Figure 4i, yellow color represents the highest value in the average confusion matrix, which implies the identity being judged as person in the row. With the increase of the number of epochs, the trained face images of 14 persons could be identified precisely. 37349

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces

Figure 5. Realization of face recognition and a strong error-tolerant nature with noise pixels. (a) Recognition rate of testing face images was not used in training at different epochs. The recognition rate of 95.2% was achieved, which makes the device reliable for applications. (b) Distribution of weights at different numbers of epochs (100th epoch, 300th epoch, and 600th epoch). (c) Output signals of 14 persons during the test for the first face. (d) Evolution of recognition rate at different percentages of noise pixels in testing images. (The error bars indicate the standard deviation in 10 simulation results). (e) Schematic diagrams of a blank image with 0, 5, 10, 20, 30, and 40% of noise pixels. The white and black points are noise pixels. (f) Recognition rate curve with 20% of noise pixels under different numbers of hidden neurons (32, 64, 128, 256, 512, and 1024). The recognition rate can be achieved more easily with a larger number of hidden neurons.

Verification of Face Recognition Functionality with a Strong Error-Tolerant Nature. After 600th epoch training, 42 untrained face images were used to verify the recognition rate. The testing process with the distinction of 14 output curves under 600 epochs could be seen in the Supporting Information, Figure S4. G of our device was in the range 1.57− 1.93 μS. During the training process, the distribution of G became wider and more uniform (Figure 5b). The output signal of the first person was larger than the signals of others, indicating that perfect classification of the first person has been achieved with increased epochs (Figure 5c). Among them, the images of the second person and seventh person were misclassified out of 42 images in the testing process (Supporting Informantion, Figure S4). The final recognition accuracy of our device was 95.2%, which was higher than the reported results in the related studies.42 Furthermore, a strong error-tolerant nature was proved by recognizing the images containing noisy pixels. Our synaptic device had the classification accuracy of above 90% in 15% noise pixel patterns, which denoted a great potential for face recognition. Six blank schematic maps with different noise ratios (0, 5, 10, 20, 30, and 40%) were presented in Figure 5e, and the recognition rate of face images with different noisy levels were 95.2, 92.8, 92.8, 88.1, 78.6, and 78.6% (Supporting Informantion, Figure S5). By changing the number of hidden neurons, the speed of achieving the highest recognition rate (training speed) could be modified (Figure 5f). The more hidden neurons were used, the higher speed of achieving the highest recognition would be.

states in LTP and LTD. There were 600 continuous, clearly distinguished conductive states in LTP and LTD (a pulse amplitude of −1.5 V/+1 V and a pulse duration of 50 ms), which could be stably performed even after 1000 folded tests. The excellent stability was attributed to the forming and rupture of the conductive paths of the PEDOT+ induced by hole injection and extraction. The variation of conductance could be modulated by changing the number of pulses, frequencies, amplitudes, and durations, which provided the basis for neuromorphic network simulation. Using the electrical characteristics of the LTP and LTD, we realized supervised learning using the face images from the Yale Face Database. The successful recognition rate of 95.2% was achieved, and it was above 90% even when 15% noise pixels were applied to testing images. The results indicated an excellent face recognition and a strong error-tolerant nature. The weights, which corresponded to G of our PEDOT:PSSbased device, were distributed in the range 1.57−1.93 μS. This work presented a huge potential of two-terminal flexible PEDOT:PSS-based memristor with ultra-multimodulated conductance states for application in a large-scale, low-cost, high-performance flexible electronic systems to realize neuromorphic computing.

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EXPERIMENTAL SECTION

Materials. An aqueous solution of PEDOT:PSS (CLEVIOS PH1000), purchased from Heraeus, was used for coating without further purification. We used Al as a bottom electrode and ITO as a top electrode. The flexible artificial synaptic device was fabricated by the following processes. Namely, a 69 nm Al (bottom electrode) was deposited on a flexible PET by electron beam evaporation. The PEDOT:PSS solution was filtered by microfilters with a 0.22 μm pore size before spin coating. The filtered aqueous solution was spun on the PET/Al substrate at 500 rpm for 15 s and 4000 rpm for 1 min, respectively. Then, the device was baked on a hot plate at 120 °C for 10 min. Finally, the ITO top electrode with a diameter of 200 μm was deposited on the PEDOT:PSS film using a shadow mask by sputtering.

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CONCLUSIONS In this study, a flexible PEDOT:PSS-based memristor (Al/ PEDOT:PSS/ITO) was presented. The fabrication process was simple, low cost, low temperature, and can be compatible with QLEDs and organic solar cells. The artificial synaptic device exhibited excellent, stable, and reliable synaptic plasticities, containing the STP, LTP, LTD, STDP learning rules, and PPF, especially ultra-multimodulated conductance 37350

DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352

Research Article

ACS Applied Materials & Interfaces Characterization. The cross section of Si/Al/PEDOT:PSS was measured by the ZEISS SIGMA HD field emission scanning electron microscope. The ex situ SPECS-ray photoelectron spectroscopy system X provided an element analysis with an Al Kα X-ray source at ν = 1486.7 eV. The tapping mode of the Bruker multimode 8 system atomic force microscopy (AFM) was used to perform the surface topography with an area of 2 × 2 μm2. The electrical characteristics of the fabricated artificial device were analyzed using a semiconductor device analyzer (Agilent B1500A) and a semiconductor parameter analyzer (B1525) in the atmospheric environment at room temperature. During the measurement of electronic characteristics, the voltage was applied to the electrode of ITO while Al was grounded.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.8b16841. Fabrication of the flexible memristor, material characterization of the PEDOT:PSS film, electric properties of artificial synaptic device in the flat state and after folded destructive tests, output signals of testing images, and recognition rate of different noisy levels (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (L.C.). *E-mail: [email protected] (Q.-Q.S.). ORCID

Lin Chen: 0000-0002-7145-7564 Hao Zhu: 0000-0003-3890-6871 Qing-Qing Sun: 0000-0002-6533-1834 Shi-Jin Ding: 0000-0002-5766-089X Peng Zhou: 0000-0002-7301-1013 Author Contributions †

T.Y.W. and H.Z. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the use of the Yale Face Database. This work was supported by the NSFC ( 61704030 and 61522404 ), the 02 State Key Project (2017ZX02315005), the Program of Shanghai Subject Chief Scientist (18XD1402800), the Support Plans for the Youth Top-Notch Talents of China, and the “Chen Guang” project supported by the Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

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DOI: 10.1021/acsami.8b16841 ACS Appl. Mater. Interfaces 2018, 10, 37345−37352