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An Oxide Schottky Junction Artificial Optoelectronic Synapse
Abstract
The rapid development of artificial intelligence techniques and future advanced robot systems sparks emergent demand on the accurate perception and understanding of the external environments via visual sensing systems that can co-locate the self-adaptive detecting, processing, and memorizing of optical signals. In this contribution, a simple indium–tin oxide/Nb-doped SrTiO 3 (ITO/Nb:SrTiO 3 ) heterojunction artificial optoelectronic synapse is proposed and demonstrated. Through the light and electric field co-modulation of the Schottky barrier profile at the ITO/Nb:SrTiO 3 interface, the oxide heterojunction device can respond to the entire visible light region in a neuromorphic manner, allowing synaptic paired-pulse facilitation, short/long-term memory, and “learning-experience” behavior for optical information manipulation. More importantly, the photoplasticity of the artificial synapse has been modulated by heterosynaptic means with a sub-1 V external voltage, not only enabling an optoelectronic analog of the mechanical aperture device showing adaptive and stable optical perception capability under different illuminating conditions but also making the artificial synapse suitable for the mimicry of interest-modulated human visual memories.
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Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Human Memory: A Proposed System and its Control Processes
Atkinson, R. C., & Shiffrin, R. M. (1968) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60422-3 · Cited by 7,216
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Backus, J. (1978) · Communications of the ACM · doi:10.1145/359576.359579 · Cited by 2,609
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Band offsets of wide-band-gap oxides and implications for future electronic devices
Robertson, J. (2000) · Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena · doi:10.1116/1.591472 · Cited by 2,049
Organic Optoelectronic Materials: Mechanisms and Applications
Ostroverkhova, O. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00127 · Cited by 1,680
Solution-processed semiconductors for next-generation photodetectors
García de Arquer, F. P., Armin, A., Meredith, P., & Sargent, E. H. (2017) · Nature Reviews Materials · doi:10.1038/natrevmats.2016.100 · Cited by 1,529
Why visual attention and awareness are different
Lamme, V. A. F. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00013-x · Cited by 1,172
Stochastic phase-change neurons
Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A., & Eleftheriou, E. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,109
Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor
Chang, T., Jo, S.-H., & Lu, W. (2011) · ACS Nano · doi:10.1021/nn202983n · Cited by 1,058
Stretched exponential relaxation in molecular and electronic glasses
Phillips, J. C. (1996) · Reports on Progress in Physics · doi:10.1088/0034-4885/59/9/003 · Cited by 938
Ahmadi, M., Wu, T., & Hu, B. (2017) · Advanced Materials · doi:10.1002/adma.201605242 · Cited by 820
Wang, Z. Q., Xu, H. Y., Li, X. H., Yu, H., Liu, Y. C., & Zhu, X. J. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201103148 · Cited by 815
Work function of indium tin oxide transparent conductor measured by photoelectron spectroscopy
Park, Y., Choong, V., Gao, Y., Hsieh, B. R., & Tang, C. W. (1996) · Applied Physics Letters · doi:10.1063/1.116313 · Cited by 690
Choi, S., Tan, S. H., Li, Z., Kim, Y., Choi, C., Chen, P.-Y., Yeon, H., Yu, S., & Kim, J. (2018) · Nature Materials · doi:10.1038/s41563-017-0001-5 · Cited by 690
Anion vacancies as a source of persistent photoconductivity in II-VI and chalcopyrite semiconductors
Lany, S., & Zunger, A. (2005) · Physical Review B · doi:10.1103/physrevb.72.035215 · Cited by 650
Lee, M., Lee, W., Choi, S., Jo, J., Kim, J., Park, S. K., & Kim, Y. (2017) · Advanced Materials · doi:10.1002/adma.201700951 · Cited by 567
Work Function Engineering of Graphene Electrode via Chemical Doping
Shi, Y., Kim, K. K., Reina, A., Hofmann, M., Li, L.-J., & Kong, J. (2010) · ACS Nano · doi:10.1021/nn1005478 · Cited by 543
Wang, H., Zhao, Q., Ni, Z., Li, Q., Liu, H., Yang, Y., Wang, L., Ran, Y., Guo, Y., Hu, W., et al. (2018) · Advanced Materials · doi:10.1002/adma.201803961 · Cited by 443
An Artificial Flexible Visual Memory System Based on an UV‐Motivated Memristor
Chen, S., Lou, Z., Chen, D., & Shen, G. (2018) · Advanced Materials · doi:10.1002/adma.201705400 · Cited by 437
He, H., Yang, R., Zhou, W., Huang, H., Xiong, J., Gan, L., Zhai, T., & Guo, X. (2018) · Small · doi:10.1002/smll.201800079 · Cited by 312
Li, S., Zeng, F., Chen, C., Liu, H., Tang, G., Gao, S., Song, C., Lin, Y., Pan, F., & Guo, D. (2013) · Journal of Materials Chemistry C · doi:10.1039/c3tc30575a · Cited by 278
Self-citationOrganic Biomimicking Memristor for Information Storage and Processing Applications
Liu, G., Wang, C., Zhang, W., Pan, L., Zhang, C., Yang, X., Fan, F., Chen, Y., & Li, R. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201500298 · Cited by 255
Self-citationAll-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing
Kumar, M., Abbas, S., & Kim, J. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b10870 · Cited by 254
Bioinspired Artificial Eyes: Optic Components, Digital Cameras, and Visual Prostheses
Lee, G. J., Choi, C., Kim, D., & Song, Y. M. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201705202 · Cited by 252
Iris‐Like Tunable Aperture Employing Liquid‐Crystal Elastomers
Schuhladen, S., Preller, F., Rix, R., Petsch, S., Zentel, R., & Zappe, H. (2014) · Advanced Materials · doi:10.1002/adma.201402878 · Cited by 245
Light-Gated Memristor with Integrated Logic and Memory Functions
Tan, H., Liu, G., Yang, H., Yi, X., Pan, L., Shang, J., Long, S., Liu, M., Wu, Y., & Li, R.-W. (2017) · ACS Nano · doi:10.1021/acsnano.7b05762 · Cited by 242
Self-citationBroadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing
Tan, H., Ni, Z., Peng, W., Du, S., Liu, X., Zhao, S., Li, W., Ye, Z., Xu, M., Xu, Y., et al. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.08.018 · Cited by 234
An Optoelectronic Resistive Switching Memory with Integrated Demodulating and Arithmetic Functions
Tan, H., Liu, G., Zhu, X., Yang, H., Chen, B., Chen, X., Shang, J., Lu, W. D., Wu, Y., & Li, R. (2015) · Advanced Materials · doi:10.1002/adma.201500039 · Cited by 233
Self-citationCooper, D., Baeumer, C., Bernier, N., Marchewka, A., La Torre, C., Dunin‐Borkowski, R. E., Menzel, S., Waser, R., & Dittmann, R. (2017) · Advanced Materials · doi:10.1002/adma.201700212 · Cited by 203
Emulating the paired-pulse facilitation of a biological synapse with a NiOx-based memristor
Hu, S. G., Liu, Y., Chen, T. P., Liu, Z., Yu, Q., Deng, L. J., Yin, Y., & Hosaka, S. (2013) · Applied Physics Letters · doi:10.1063/1.4804374 · Cited by 193
Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics
Najem, J. S., Taylor, G. J., Weiss, R. J., Hasan, M. S., Rose, G., Schuman, C. D., Belianinov, A., Collier, C. P., & Sarles, S. A. (2018) · ACS Nano · doi:10.1021/acsnano.8b01282 · Cited by 193
Memristive Synapses with Photoelectric Plasticity Realized in ZnO1–x/AlOyHeterojunction
Hu, D.-C., Yang, R., Jiang, L., & Guo, X. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b01036 · Cited by 181
A Retina‐Like Dual Band Organic Photosensor Array for Filter‐Free Near‐Infrared‐to‐Memory Operations
Wang, H., Liu, H., Zhao, Q., Ni, Z., Zou, Y., Yang, J., Wang, L., Sun, Y., Guo, Y., Hu, W., et al. (2017) · Advanced Materials · doi:10.1002/adma.201701772 · Cited by 124
Simulation of retinal ganglion cell response using fast independent component analysis
Wang, G., Wang, R., Kong, W., & Zhang, J. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9490-4 · Cited by 120
Highly Compact Artificial Memristive Neuron with Low Energy Consumption
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., Wang, Y., Zhang, Z., Ji, X., Li, G., et al. (2018) · Small · doi:10.1002/smll.201802188 · Cited by 117
Organic Non‐Volatile Resistive Photo‐Switches for Flexible Image Detector Arrays
Nau, S., Wolf, C., Sax, S., & List, E. J. W. (2014) · Advanced Materials · doi:10.1002/adma.201403295 · Cited by 99
Yan, Z. B., & Liu, J. (2013) · Scientific Reports · doi:10.1038/srep02482 · Cited by 93
Jaafar, A. H., Gray, R. J., Verrelli, E., O'Neill, M. B., Kelly, S. M., & Kemp, N. T. (2017) · Nanoscale · doi:10.1039/c7nr06138b · Cited by 85
Baeumer, C., Raab, N., Menke, T., Schmitz, C., Rosezin, R., Müller, P., Andrä, M., Feyer, V., Bruchhaus, R., Gunkel, F., et al. (2016) · Nanoscale · doi:10.1039/c6nr00824k · Cited by 82
Yang, Y., Yin, M., Yu, Z., Wang, Z., Zhang, T., Cai, Y., Lu, W. D., & Huang, R. (2017) · Advanced Electronic Materials · doi:10.1002/aelm.201700032 · Cited by 71
Role of oxygen vacancies in resistive switching in Pt/Nb-doped SrTiO3
Park, J., Kwon, D., Park, H., Jung, C. U., & Kim, M. (2014) · Applied Physics Letters · doi:10.1063/1.4901053 · Cited by 62
Size Dependence of Resistive Switching at Nanoscale Metal‐Oxide Interfaces
Hou, J., Nonnenmann, S. S., Qin, W., & Bonnell, D. A. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201304121 · Cited by 38
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Hou, Y.-X., Li, Y., Zhang, Z.-C., Li, J.-Q., Qi, D.-H., Chen, X.-D., Wang, J.-J., Yao, B.-W., Yu, M.-X., Lu, T.-B., et al. (2021) · ACS Nano · doi:10.1021/acsnano.0c08921 · Cited by 438
Artificial Neural Networks Based on Memristive Devices: From Device to System
Huang, H.-M., Wang, Z., Wang, T., Xiao, Y., & Guo, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000149 · Cited by 82
Liu, S., Wu, Z., He, Z., Chen, W., Zhong, X., Guo, B., Liu, S., Duan, H., Guo, Y., Zeng, J., et al. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04398 · Cited by 34
Self-citation
Metadata
- Authors
- Gao, S., Liu, G., Yang, H., Hu, C., Chen, Q., Gong, G., Xue, W., Yi, X., Shang, J., & Li, R.-W.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-02-07
- Volume
- 13
- Issue
- 2
- Pages
- 2634-2642
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2911694631
- Cited by
- 494
- References
- 46
- FWCI
- 17.98
- Type
- journal article
- Language
- en
- Open access
- closed
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