Flexible Electronic Synapses for Face Recognition Application with

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Organic Electronic Devices

Flexible Electronic Synapses for Face Recognition Application with Multi-modulated Conductance States Tian-Yu Wang, Zhen-Yu He, Hao Liu, Lin Chen, Hao Zhu, QingQing Sun, Shi-Jin Ding, Peng Zhou, and David Wei Zhang ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b16841 • Publication Date (Web): 04 Oct 2018 Downloaded from http://pubs.acs.org on October 9, 2018

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ACS Applied Materials & Interfaces

Flexible Electronic Synapses for Face Recognition Application with Multi-modulated Conductance States Tian-Yu Wang1#, Zhen-Yu He1, Hao Liu1, Lin Chen1*, Hao Zhu1#, Qing-Qing Sun1*, Shi-Jin Ding1, Peng Zhou1, and David Wei Zhang1 1

State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China #These authors contributed equally to this work * E-mail : [email protected]; [email protected];

Abstract Artificial synaptic device with a continuous weights modulation behavior is fundamental to the hardware implementation of the bioinspired neuromorphic systems. Recent reported synaptic devices have less number of conductance states, which is not beneficial for 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 multi-modulated conductance states, realizing a face recognition functionality with strong error-tolerant nature for the first time. The device shows an excellent long-term potentiation (LTP) or long-term depression (LTD) behavior and reliability after 1000 folded destructive tests. There are 600 continous ultra multi-modulated conductance states, which can be used to realize great face recognition capability. The recognition rates were 95.2% and above 90% for the initial and 15%-noise pixels images, respectively. The strong error-tolerant nature indicates a potential application of a flexible organic artificial synaptic device with ultra multi-modulated conductance states in the large-scale neuromorphic systems. Keywords: flexible memristor, ultra multi-modulation, biocompatible polymers, face recognition, error-tolerant nature ACS Paragon Plus Environment

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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 is consisted of 1012 neurons and 1015 synapses.1,2 Synapses play important roles in the delivery of signal by releasement of neurotransmitter 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, have been widely demonstrated in biological neural networks.4,5 Comparing with 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 brain’s cognitive process.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 continuous modulation of weights in the ANNs. To better mimic the bio-synapses and realize various applications based on neuromorphic computing, fabricating a memory with more continuous modulated conductance statemets is an urgent need.16 Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)

(PEDOT:PSS)

is

widely used in the quantum dot light-emitting diodes (QLEDs)17 and organic solar cells18,19 because of high transparency, hole transporting ability and the compatibility of traditional semiconductor processes. However, it’s neglected in the filed 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 flexilbe 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 ACS Paragon Plus Environment


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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 multi-modulated conductance states has not been reported. In

this

work,

we

design

a

two-terminal,

flexible,

biocompatible,

PEDOT:PSS-based artificial synapse (Al/PEDOT:PSS/ITO), emulate the synaptic plasticity with 600 conductance states and realize its applications in the face recognition with strong error-tolerant nature. The fabrication process used in this study is low-cost, simple and compatible with flexible substrate, which can be performed at the temperature lower than 120℃. Unlike the silicon or glass-based 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 grey-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 prove the feasibility of our artificial synapse. The film of PEDOT:PSS is widely used as hole transport layer and shows synaptic platisicity, which make PEDOT:PSS-based flexible artificial synaptic device become a promising candidate for integration into OLEDs and solar cells for multifunctional applications. RESULTS AND DISCUSSION Structure of artificial synaptic device. The high-denisty array of memristors play an important role in ANNs.26 RRAM with the simple structure of MetalInsulator- Metal (MIM) has become one of the strongest candidates for constructing neuromorphic computing systems due to the advantages of high-integration, low-power consumption and simple structure.27,28 The schematic diagram and


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photograph of the fabricated device were presented in Figure 1a and Figure 1b, respectively. 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(SEM) 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 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 are belong to PEDOT. Two higher binding energy peaks belong to PSS. The peak at 169.1eV corresponds to neutral sulfur and the peak at 167.8eV corresponds to ionic sulfur in the PSS dopant, respectively.29 While the well-signed peaks of O 1s in PEDOT:PSS appeared at 533 eV and 531eV30 (Supporting Informantion, Figure S1). By analyzing the area of the XPS S(2p) spectra, we found that 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- 2b. With the consecutive negative voltage sweeps (0 → -1.5 V→ 0 ) applied to the ITO 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 bio-syanpses 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 (pulse amplitude of -1.5 V, pulse duration, e.t., pulse width of 50 ms) and 300 positive bias pulses (pulse amplitude of +1 V, pulse duration, i.e., 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 (+1.5V) 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 analyse 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 between Ln(I) and V1/2 could be well fitted with Schottky thermionic emission model.32 The work functions of Al electrode and ITO electrode are 4.3 eV and 4.8 eV, respectively. For 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 gradully 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 simulation of pattern recognition.35 The retention time of G after a single input voltage spike was longer than 104s in both LTP and LTD (Supporting Informantion, Figure S2). To demonstrate the multi-states 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., connecting strength between pre- and post- synapses), 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 deivce could repeated 100 cycles with a certain degree of current ACS Paragon Plus Environment

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degradation (Supporting Informantion, Figure S2). The artificial synaptic device showed the reliability after 100 folded destructive operations with radius of curvature of 0 mm (Supporting Informantion, Figure S3), which provides the possibilities for large-scale applications of PEDOT:PSS-based flexible memory. To demonstrate the excellent performance of flexible synaptic device, 1000 times folded operations were applied to the device and it still could work continuously at least 10cycles 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’s 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 higher concentration of Ca2+ than the former, corresponding to stronger synaptic strength. PPF can be mimicked using our device with a pair of pre-synaptic spikes (pulse amplitude of -1.5 V, pulse duration of 50 ms, 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. PPF index (A2/A1) decreased with the increasing of intervals between two pulses. There were no obvious difference 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 (pulse amplitude of -1 V, pulse duration of 50 ms, pulse interval of 10 s) as shown in Figure 2d, which plays an important part in a neuronal signal transmission of bio-synapses.37 STDP learning rule is one of the long-term memory functionalities.38,39 The temporal interval between pre- and post-synaptic activities is defined as ΔtSTDP=tpost-tpre,



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while 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: ∆G =

Gt − G0 ×100% Gt

(1)

where Gt and G0 represent the conductances after and before applying the input spikes. The inset shows the pre-input spike (pulse amplitude of -1.5 V, pulse duration of 50 ms) and post-input spike (pulse amplitude of +1.5 V, 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 bio-synapse,40 while Δ G decreased with the increasing of ΔtSTDP from 10 ms to 60 ms. In contrast, the behavior corresponded to the LTD in a bio-synapse 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 number of pulses (Figure 3e), 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 increasing of pulse number, frequency, amplitude and duration, in the range of 10 to 300 pulses, 1 HZ to 50 HZ, -0.5 V to -1.5 V (negative voltage) and 5 ms to 50 ms, respectively. In short, the more input spikes are applied to the memory in a unit time, the more pronounced of conductance response will be.

Neuromorphic network with PEDOT:PSS-based synapses. Based on the measured electrical characteristics of 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 grey-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 ACS Paragon Plus Environment

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were shown in Figure 4a. The circuit diagram was consisted of 1024× 256 artificial synaptic devices. The top and bottom electrodes could be connected to the bit line (BL) and the word line (WL), simulating pre- and post- synapses, respectively. Multi-level pulses were applied to the pre-synaptic device to modulate conductance of a specific device, realizing weights update and the function of face recognition. The ANN traning contained two processes: training and validation processes, and the training operation was consisted of forward propagation and back-propagation.41 In the traning 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: 1024

I j (n) = ∑ Wij xi ( n)

(2)

i =1

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 input neuron i and hidden neuron j. Then the result was activated by a nonlinear log-sigmoid transfer function (logsig) function. The activated result of hidden layer was transferred to the output neurons. With the sum and activation processes, the output signals were obtained: 256

Ok = logsig (∑ W jk f j (n))

(3)

j =1

where fj and Ok are the output signals of the second and third layer of ANN, respectively. After above calculation, the process of the forward propagation was finished. During the back-propagation process, delta weights (∆Wjk) were calculated and transferred to modified the synaptic weights with the input pulses. The altering value of the second layer synapses was:

∆W jk = η f j ek

(4)

where η is the learning rate, and ek is the calculated error between the real output and target output during the training. When the feedback was transfered to the weights of the first layer, an epoch finished. Figures 4 d-i showed the average confusion matrices of face classification with 14 different images corresponding to the fowlling 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 increasing of the number of epochs, the trained face images of 14 persons could be identified precisely.

Verification of face recognition functionality with strong error-tolerant nature. After 600th epoch training, 42 untrained face images was used to verify recognition rate. The testing process with the distinction of 14 output curve under 600 epoch could be seen in Supporting Information, Figure S4. G of our device was in the range 1.57-1.93 µS. During training process. the distribution of G became wider and more uniform (Figure 5b). The output signal of the 1st person was larger than the signals of others, indicating that perfect classification of the 1st person has been achieved with increased epochs (Figure 5c). Among them, images of the 2nd person and 7th 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 reported results in the related studies.42 Furthermore, strong error-tolerant nature was proved by recognizing images containing noisy pixels. Our synaptic device had the classification accuracy of above 90% in 15%-noise pixels 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.

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 aritifical synaptic device exhibited excellent, stable and reliable synaptic plasticities, containing the STP, LTP,


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LTD, STDP learning rules and PPF, especially ultra multi-modulated conductance states in LTP and LTD. There were 600 continuous, clearly distinguished conductive states in the LTP and LTD (pulse amplitude of -1.5 V/+1 V, pulse duration of 50 ms), which could be stablely 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:PSS-based 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 multi-modulated conductance states for application in a large-scale, low-cost, high-performance flexible electronic systems to realize neuromorphic computing.

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 Indium–tin oxide (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 polyethylene terephthalate (PET) by the electron beam evaporation (EBE). The PEDOT:PSS solution was filtered by micro-filters 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℃ 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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Characterization. The cross-section of a Si/Al/PEDOT:PSS was measured by the ZEISS SIGMA HD field emission scanning electron microscope (FESEM). The Ex-situ SPECS-ray photoelectron spectroscopy (XPS) 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 the semiconductor device analyzer (Agilent B1500A) and 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.

ASSOCIATED CONTENT Supporting Information Frabication of the flexible memristor, material characterization of PEDOT:PSS film, electric properties of artificial synaptic device in the flat state and after folded destructive tests and additional figures.

AUTHOR INFORMATION Corresponding Author *Email: [email protected]; [email protected]

Author Contributions T.Y.W. and H.Z: These authors contributed equally to this work.

Notes The authors declare no competing financial interest

ACKNOWLEDGEMENTS We acknowledge the use of the Yale Face Database.The work was supported by the NSFC

(61704030,

61376092,

and

61427901),


02

State

Key

Project

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(2017ZX02315005), Shanghai Rising-Star Program (14QA1400200), Shanghai Educational Development Foundation, Program of Shanghai Subject Chief Scientist (14XD1400900), the S&T Committee of Shanghai (14521103000, 15DZ1100702, 15DZ1100503), and “Chen Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation.

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Figure 1. Artificial flexible synapses based on the 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 pre- neurons, respectively. (c) The morphology schematic diagram of the PEDOT:PSS. (d) Fabrication process with the advantages of easy operation and low-cost. (e) The 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 the PEDOT and PSS, respectively.


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Figure 2. Electrical behaviors of artificial flexible synapses. I-V characteristics of the memory under 6 consecutive (a) negative and (b) positive voltage sweeps. The inset shows the increase of post-current after each negative sweep. (c) The 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 holes (h+) injection and extraction corresponding to forming and rupture of the conductive paths of the PEDOT+.



Figure

3.

Reliable

synaptic

plasticity

of

the

PEDOT:PSS-based

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device.

(a) Consecutive and stable LTP and LTD behaviors containing 10 cycles. Every cycle consisted of 600 clearly different conductance statements. (b) A pair of output spikes of EPSC triggered by two consecutive input pulses (amplitude of -1.5 V, duration of 50 ms, Δt of 20 ms) on the artificial synaptic device. A1 and A2 represent the EPSCs of input

pulses, respectively. (c) The PPF index determined by the time interval of two input pre-synaptic pulses ( Δ tPPF=tpre2-tpre1). (d) The Hebbian STDP learning rules in the synaptic device. The input pulses of the pre- and post-neurons (amplitude of 1.5 V, duration of 50 ms, ΔtSTDP =tpost-tpre) are shown in the inset. The response of the PSC analyzed at (e) different number of pulses ( 10, 20, 50, 100, 200 and 300, amplitude of -1.5 V/+1 V, duration of 50 ms,), and (f) different frequencies of pulses(1 HZ, 10 HZ, 20 HZ and 50 HZ, amplitude of -1.5 V/+1 V, duration of 50 ms). (g) The retention behaviors of 10 conductance states have been demonstrated by different number of pulses (0, 1, 10, 20, 50, 100, 150, 200, 250 and 300). (h) The uniformity property of


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PEDOT:PSS-based synaptic devices. (i) The the LTP/LTD behaviors of flexible artificial synaptic device after 1000 folded cycles.

Figure 4. Neuromorphic network with an artificial flexible synapses array. The schematic illustration of (a) parallel read and modulate conductance by changing the number of pulses using a simulated device array, which is consisted of 1024 ×256 RRAM as cells. (b) The ANN with 3 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 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.

Figure 5. . Realization of face recognition and strong error-tolerant nature with noise pixels. (a) The recognition rate of testing face images were not used in training at different epochs. The recognition rate of 95.2% was achieved which makes the device reliable for applications. (b) The distribution of weights at different number of epochs (100th epoch, 300th epoch, and 600th epoch). (c) The output signals of 14 persons during the test for the 1st face. (d) The evolution of recognition rate at different percentages of noise pixels in testing images. (The error bars indicate 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 wihte and black points are noise pixels. (f) Recognition rate curve with 20% of noise pixels under different number 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.


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Flexible Electronic Synapses for Face Recognition Application with

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