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Guo L, Sun H, Min L, Wang M, Cao F, Li L. Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402253. [PMID: 38553842 DOI: 10.1002/adma.202402253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/16/2024] [Indexed: 04/09/2024]
Abstract
Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.
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Affiliation(s)
- Linqi Guo
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Haoxuan Sun
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Liangliang Min
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Meng Wang
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Fengren Cao
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Liang Li
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
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Kim H, Kim M, Lee A, Park HL, Jang J, Bae JH, Kang IM, Kim ES, Lee SH. Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2300659. [PMID: 37189211 DOI: 10.1002/advs.202300659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/19/2023] [Indexed: 05/17/2023]
Abstract
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.
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Affiliation(s)
- Hyeongwook Kim
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Miseong Kim
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Aejin Lee
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Hea-Lim Park
- Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
| | - Jaewon Jang
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Jin-Hyuk Bae
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - In Man Kang
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
| | - Eun-Sol Kim
- Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Sin-Hyung Lee
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 702-701, Republic of Korea
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Lee DH, Park H, Cho WJ. Synaptic Transistors Based on PVA: Chitosan Biopolymer Blended Electric-Double-Layer with High Ionic Conductivity. Polymers (Basel) 2023; 15:polym15040896. [PMID: 36850180 PMCID: PMC9959983 DOI: 10.3390/polym15040896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
This study proposed a biocompatible polymeric organic material-based synaptic transistor gated with a biopolymer electrolyte. A polyvinyl alcohol (PVA):chitosan (CS) biopolymer blended electrolyte with high ionic conductivity was used as an electrical double layer (EDL). It served as a gate insulator with a key function as an artificial synaptic transistor. The frequency-dependent capacitance characteristics of PVA:CS-based biopolymer EDL were evaluated using an EDL capacitor (Al/PVA: CS blended electrolyte-based EDL/Pt configuration). Consequently, the PVA:CS blended electrolyte behaved as an EDL owing to high capacitance (1.53 µF/cm2) at 100 Hz and internal mobile protonic ions. Electronic synaptic transistors fabricated using the PVA:CS blended electrolyte-based EDL membrane demonstrated basic artificial synaptic behaviors such as excitatory post-synaptic current modulation, paired-pulse facilitation, and dynamic signal-filtering functions by pre-synaptic spikes. In addition, the spike-timing-dependent plasticity was evaluated using synaptic spikes. The synaptic weight modulation was stable during repetitive spike cycles for potentiation and depression. Pattern recognition was conducted through a learning simulation for artificial neural networks (ANNs) using Modified National Institute of Standards and Technology datasheets to examine the neuromorphic computing system capability (high recognition rate of 92%). Therefore, the proposed synaptic transistor is suitable for ANNs and shows potential for biological and eco-friendly neuromorphic systems.
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Affiliation(s)
- Dong-Hee Lee
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
- Correspondence: ; Tel.: +82-2-940-5163
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Kim JP, Kim SK, Park S, Kuk SH, Kim T, Kim BH, Ahn SH, Cho YH, Jeong Y, Choi SY, Kim S. Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference. NANO LETTERS 2023; 23:451-461. [PMID: 36637103 DOI: 10.1021/acs.nanolett.2c03453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al2O3/Ta2O5/Al2O3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.
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Affiliation(s)
- Joon Pyo Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong Kwang Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seohak Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Song-Hyeon Kuk
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Taeyoon Kim
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Bong Ho Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Seong-Hun Ahn
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Yong-Hoon Cho
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - YeonJoo Jeong
- Korea Institute of Science and Technology (KIST), Seoul02792, Republic of Korea
| | - Sung-Yool Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
| | - Sanghyeon Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon34141, Republic of Korea
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Ahn DH, Hu S, Ko K, Park D, Suh H, Kim GT, Han JH, Song JD, Jeong Y. Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:24592-24601. [PMID: 35580309 DOI: 10.1021/acsami.2c04404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A charge trap device based on field-effect transistors (FET) is a promising candidate for artificial synapses because of its high reliability and mature fabrication technology. However, conventional MOSFET-based charge trap synapses require a strong stimulus for synaptic update because of their inefficient hot-carrier injection into the charge trapping layer, consequently causing a slow speed operation and large power consumption. Here, we propose a highly efficient charge trap synapse using III-V materials-based tunnel field-effect transistor (TFET). Our synaptic TFETs present superior subthreshold swing and improved charge trapping ability utilizing both carriers as charge trapping sources: hot holes created by impact ionization in the narrow bandgap InGaAs after being provided from the p+-source, and band-to-band tunneling hot electrons (BBHEs) generated at the abrupt p+n junctions in the TFETs. Thanks to these advances, our devices achieved outstanding efficiency in synaptic characteristics with a 5750 times faster synaptic update speed and 51 times lower sub-fJ/um2 energy consumption per single synaptic update in comparison to the MOSFET-based synapse. An artificial neural network (ANN) simulation also confirmed a high recognition accuracy of handwritten digits up to ∼90% in a multilayer perceptron neural network based on our synaptic devices.
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Affiliation(s)
- Dae-Hwan Ahn
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Suman Hu
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Kyeol Ko
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Donghee Park
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Hoyoung Suh
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Gyu-Tae Kim
- School of Electrical Engineering, Korea University 1, Jongam-ro, Seongbuk-gu, Seoul 02841, South Korea
| | - Jae-Hoon Han
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - Jin-Dong Song
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
| | - YeonJoo Jeong
- Korea Institute of Science and Technology (KIST) 5, 14-gil, Hwarang-ro, Seongbuk-gu, Seoul 02792, South Korea
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Huang CH, Zhang Y, Nomura K. Reconfigurable Artificial Synapses with Excitatory and Inhibitory Response Enabled by an Ambipolar Oxide Thin-Film Transistor. ACS APPLIED MATERIALS & INTERFACES 2022; 14:22252-22262. [PMID: 35522905 DOI: 10.1021/acsami.1c24327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A gate-tunable synaptic device controlling dynamically reconfigurable excitatory and inhibitory synaptic responses, which can emulate the fundamental synaptic responses for developing diverse functionalities of the biological nervous system, was developed using ambipolar oxide semiconductor thin-film transistors (TFTs). Since the balanced ambipolarity is significant, a boron-incorporated SnO (SnO:B) oxide semiconductor channel was newly developed to improve the ambipolar charge transports by reducing the subgap defect density, which was reduced to less than 1017 cm-3. The ambipolar SnO:B-TFT could be fabricated with a good reproductivity at the maximum process temperature of 250 °C and exhibited good TFT performances, such as a nearly zero switching voltage, the saturation mobility of ∼1.3 cm2 V-1 s-1, s-value of ∼1.1 V decade-1, and an on/off-current ratio of ∼8 × 103 for the p-channel mode, while ∼0.14 cm2 V-1 s-1, ∼2.2 V decade-1and ∼1 × 103 for n-channel modes, respectively. The ambipolar device imitated potentiation/depression behaviors in both excitatory and inhibitory synaptic responses by using the p- and n-channel transports by tuning a gate bias. The low-power consumptions of <20 and <2 nJ per pulse for the excitatory and inhibitory operations, respectively, were also achieved. The presented device operated under an ambient atmosphere and confirmed a good operation reliability over 5000 pulses and a long-term air environmental stability. The study presents the high potential of an ambipolar oxide-TFT-based synaptic device with a good manufacturability to develop emerging neuromorphic perception and computing hardware for next-generation artificial intelligence systems.
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Affiliation(s)
- Chi-Hsin Huang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Yong Zhang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
| | - Kenji Nomura
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States
- Material Science and Engineering Program, University of California San Diego, La Jolla, California 92093, United States
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Artificial Neurons and Synapses Based on Al/a-SiNxOy:H/P+-Si Device with Tunable Resistive Switching from Threshold to Memory. NANOMATERIALS 2022; 12:nano12030311. [PMID: 35159656 PMCID: PMC8839940 DOI: 10.3390/nano12030311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 01/09/2023]
Abstract
As the building block of brain-inspired computing, resistive switching memory devices have recently attracted great interest due to their biological function to mimic synapses and neurons, which displays the memory switching or threshold switching characteristic. To make it possible for the Si-based artificial neurons and synapse to be integrated with the neuromorphic chip, the tunable threshold and memory switching characteristic is highly in demand for their perfect compatibility with the mature CMOS technology. We first report artificial neurons and synapses based on the Al/a-SiNxOy:H/P+-Si device with the tunable switching from threshold to memory can be realized by controlling the compliance current. It is found that volatile TS from Al/a-SiNxOy:H/P+-Si device under the lower compliance current is induced by the weak Si dangling bond conductive pathway, which originates from the broken Si-H bonds. While stable nonvolatile MS under the higher compliance current is attributed to the strong Si dangling bond conductive pathway, which is formed by the broken Si-H and Si-O bonds. Theoretical calculation reveals that the conduction mechanism of TS and MS agree with P-F model, space charge limited current model and Ohm’s law, respectively. The tunable TS and MS characteristic of Al/a-SiNxOy:H/P+-Si device can be successfully employed to mimic the biological behavior of neurons and synapse including the integrate-and-fire function, paired-pulse facilitation, long-term potentiation and long-term depression as well as spike-timing-dependent plasticity. Our discovery supplies an effective way to construct the neuromorphic devices for brain-inspired computing in the AI period.
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Wan X, Tsuruoka T, Terabe K. Neuromorphic System for Edge Information Encoding: Emulating Retinal Center-Surround Antagonism by Li-Ion-Mediated Highly Interactive Devices. NANO LETTERS 2021; 21:7938-7945. [PMID: 34516142 DOI: 10.1021/acs.nanolett.1c01990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Center-surround antagonism, a key mechanism in the retina, contributes to the encoding of edge contrast rather than of the overall information on a visual image. Here, a neuromorphic system consisting of multiple ionic devices is built, where each device has a lithium cobalt oxide channel arranged on a common lithium phosphorus oxynitride electrolyte. Because of the migration of Li ions between the channels through the electrolyte, the devices are highly interactive, as is seen with retinal neurons. On the basis of the excitation of single devices and device-to-device inhibition, the system successfully emulates the antagonistic center-surround receptive field and the Mach band effect in which perceived contrast is enhanced at the edges between dark and bright regions. Furthermore, a two-dimensional array system is simulated to implement edge detection for real images. This scheme enables computer vision tasks with simple and effective operations, owing to the intrinsic properties of the materials employed.
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Affiliation(s)
- Xiang Wan
- International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba 305-0044, Japan
| | - Tohru Tsuruoka
- International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba 305-0044, Japan
| | - Kazuya Terabe
- International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba 305-0044, Japan
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Shi H, Li M, Shi J, Zhang D, Fan Z, Zhang M, Liu L. Self-Assembled Peptide Nanofibers with Voltage-Regulated Inverse Photoconductance. ACS APPLIED MATERIALS & INTERFACES 2021; 13:1057-1064. [PMID: 33378176 DOI: 10.1021/acsami.0c18893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Inverse photoconductance is an uncommon phenomenon observed in selective low-dimensional materials, in which the electrical conductivity of the materials decreases under light illumination. The unique material property holds great promise for biomedical applications in photodetectors, photoelectric logic gates, and low-power nonvolatile memory, which remains a daunting challenge. Especially, tunable photoconductivity for biocompatible materials is highly desired for interfacing with biological systems but is less explored in organic materials. Here, we report nanofibers self-assembled with cyclo-tyrosine-tyrosine (cyclo-YY) having voltage-regulated inverse photoconductance and photoconductance. The peptide nanofibers can be switched back and forth by a bias voltage for imitating biological sensing in artificial vision and memory devices. A peptide optoelectronic resistive random access memory (PORRAM) device has also been fabricated using the nanofibers that can be electrically switched between long-term and short-term memory. The underlying mechanism of the reversible photoconductance is discussed in this paper. Due to the inherent biocompatibility of peptide materials, the reversible photoconductive nanofibers may have broad applications in sensing and storage for biotic and abiotic interfaces.
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Affiliation(s)
- Huiyao Shi
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minglin Li
- Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou 350108, China
- College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
| | - Jialin Shi
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dindong Zhang
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Shenyang 110016, China
| | - Zhen Fan
- Department of Polymeric Materials, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China
- Institute for Advanced Study, Tongji University, Shanghai 200092, China
| | - Mingjun Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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