Nonlinear Ion Dynamics Enable Spike Timing Dependent Plasticity of Electrochemical Ionic Synapses
Name
Advanced Materials - 2025 - Huang - Nonlinear Ion Dynamics Enable Spike Timing Dependent Plasticity of Electrochemical.pdf
Description
Published version
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4.41 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Huang, Mantao
Xu, Longlong
del Alamo, Jesús A
Li, Ju
Yildiz, Bilge
Date Issued
January 29, 2025
Journal
Advanced Materials
Publisher
Wiley
Citation
M. Huang, L. Xu, J. A. del Alamo, J. Li, B. Yildiz, Nonlinear Ion Dynamics Enable Spike Timing Dependent Plasticity of Electrochemical Ionic Synapses. Adv. Mater. 2025, 37, 2418484.
Version
Final published version
Abstract
Programmable synaptic devices that can achieve timing-dependent weightupdates are key components to implementing energy-efficient spiking neuralnetworks (SNNs). Electrochemical ionic synapses (EIS) enable theprogramming of weight updates with very low energy consumption and lowvariability. Here, the strongly nonlinear kinetics of EIS, arising from nonlineardynamics of ions and charge transfer reactions in solids, are leveraged toimplement various forms of spike-timing-dependent plasticity (STDP). Inparticular, protons are used as the working ion. Different forms of the STDPfunction are deterministically predicted and emulated by a linearsuperposition of appropriately designed pre- and post-synaptic neuronsignals. Heterogeneous STDP is also demonstrated within the array tocapture different learning rules in the same system. STDP timescales arecontrollable, ranging from milliseconds to nanoseconds. The STDP resultingfrom EIS has lower variability than other hardware STDP implementations,due to the deterministic and uniform insertion of charge in the tunablechannel material. The results indicate that the ion and charge transferdynamics in EIS can enable bio-plausible synapses for SNN hardware withhigh energy efficiency, reliability, and throughput.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1002/adma.202418484
