Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Science (American Association for the Advancement of Science), 2019-05, Vol.364 (6440), p.570-574

Ort / Verlag

United States: The American Association for the Advancement of Science

Erscheinungsjahr

2019

Quelle

American Association for the Advancement of Science

Beschreibungen/Notizen

• 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.