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Title: Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

Abstract

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.

Authors:
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Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1547461
Grant/Contract Number:  
DESC0001160
Resource Type:
Published Article
Journal Name:
Science
Additional Journal Information:
Journal Name: Science Journal Volume: 364 Journal Issue: 6440; Journal ID: ISSN 0036-8075
Publisher:
American Association for the Advancement of Science (AAAS)
Country of Publication:
United States
Language:
English

Citation Formats

Fuller, Elliot J., Keene, Scott T., Melianas, Armantas, Wang, Zhongrui, Agarwal, Sapan, Li, Yiyang, Tuchman, Yaakov, James, Conrad D., Marinella, Matthew J., Yang, J. Joshua, Salleo, Alberto, and Talin, A. Alec. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. United States: N. p., 2019. Web. doi:10.1126/science.aaw5581.
Fuller, Elliot J., Keene, Scott T., Melianas, Armantas, Wang, Zhongrui, Agarwal, Sapan, Li, Yiyang, Tuchman, Yaakov, James, Conrad D., Marinella, Matthew J., Yang, J. Joshua, Salleo, Alberto, & Talin, A. Alec. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. United States. doi:10.1126/science.aaw5581.
Fuller, Elliot J., Keene, Scott T., Melianas, Armantas, Wang, Zhongrui, Agarwal, Sapan, Li, Yiyang, Tuchman, Yaakov, James, Conrad D., Marinella, Matthew J., Yang, J. Joshua, Salleo, Alberto, and Talin, A. Alec. Thu . "Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing". United States. doi:10.1126/science.aaw5581.
@article{osti_1547461,
title = {Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing},
author = {Fuller, Elliot J. and Keene, Scott T. and Melianas, Armantas and Wang, Zhongrui and Agarwal, Sapan and Li, Yiyang and Tuchman, Yaakov and James, Conrad D. and Marinella, Matthew J. and Yang, J. Joshua and Salleo, Alberto and Talin, A. Alec},
abstractNote = {Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.},
doi = {10.1126/science.aaw5581},
journal = {Science},
number = 6440,
volume = 364,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1126/science.aaw5581

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