Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
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
Web of Science
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