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

Journal Article · · Science

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. In conclusion, the redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.

Research Organization:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0001160; AC04-94AL85000
OSTI ID:
1547461
Alternate ID(s):
OSTI ID: 1570281
Report Number(s):
SAND-2019-11518J; /sci/364/6440/570.atom
Journal Information:
Science, Journal Name: Science Vol. 364 Journal Issue: 6440; ISSN 0036-8075
Publisher:
AAASCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 341 works
Citation information provided by
Web of Science

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