Water-based resistive switches for neuromorphic long-range connections. (15th March 2021)

• Record Type: Journal Article
• Authors: Ananthakrishnan, Akshay; Du, Xingyu; Allen, Mark G
• Is Part Of: Journal of physics. Volume 54:Number 22(2021)
• Publication Date: 2021-03-15
• Subjects: resistive switch -- liquid synapse -- long-range connection -- neuromorphic -- silver dendrite -- electrochemical; Physics -- Periodicals; 530
• DOI: 10.1088/1361-6463/abead7
• Languages: English
• ISSNs: 0022-3727

Abstract

Abstract: The brain's small-world network utilizes its short-range and long-range synaptic connections to process information in a complex and energy-efficient manner. To emulate the former, neuromorphic hardware typically leverages the conductance switching properties of thin-film dielectrics and semiconductors. Because these materials offer low ion mobilities, long-range connections built from thicker dielectrics require impractically-large forming voltages. To overcome this intrinsic shortcoming of solid-state active media, we present in this paper a simple Ag–H2 O–Au cell that takes advantage of the relatively high ion mobility offered by deionized water to enable programmable connectivity switches between neurons separated by large gaps (∼40 µ m). We introduce dual voltage programming schemes that allow the switch conductance to be modulated in analog and digital steps. When operating in the analog mode, the switch conductance could be potentiated and depressed over a relatively large (3.5×) range. In the digital mode, the Ag–H2 O–Au switch delivered a high ON/OFF current ratio of ∼600 and sustained this margin over 200 switching cycles. Additionally, both switch states could be maintained for at least 3 h without external power. We show that unlike their solid-state counterparts, the water-gap in the Ag–H2 O–Au cell can be easily refreshed without compromising the switching functionality. These attributes of Ag–H2 O–Au switches in addition to their biocompatibility and simple design make them attractive for neuromorphic wetware implementations.