Unraveling Polymer–Ion Interactions in Electrochromic Polymers for their Implementation in Organic Electrochemical Synaptic Devices
Name
Adv Funct Materials - 2023 - Roh - Unraveling Polymer Ion Interactions in Electrochromic Polymers for their Implementation.pdf
Description
Published version
Size
2.6 MB
Format
Adobe PDF
Checksum (MD5)
65f9f4efe2be03d0319eb6278d148fe8
Author(s)
Roh, Heejung
Yue, Shuwen
Hu, Hang
Chen, Ke
Kulik, Heather J
Gumyusenge, Aristide
Date Issued
November 2, 2023
Journal
Advanced Functional Materials
Publisher
Wiley
Citation
H. Roh, S. Yue, H. Hu, K. Chen, H. J. Kulik, A. Gumyusenge, Unraveling Polymer–Ion Interactions in Electrochromic Polymers for their Implementation in Organic Electrochemical Synaptic Devices. Adv. Funct. Mater. 2023, 33, 2304893.
Version
Final published version
Abstract
Owing to low-power, fast and highly adaptive operability, as well as scalability, electrochemical random-access memory (ECRAM) technology is one of the most promising approaches for neuromorphic computing based on artificial neural networks. Despite recent advances, practical implementation of ECRAMs remains challenging due to several limitations including high write noise, asymmetric weight updates, and insufficient dynamic ranges. Here, inspired by similarities in structural and functional requirements between electrochromic devices and ECRAMs, high-performance, single-transistor and neuromorphic devices based on electrochromic polymers (ECPs) are demonstrated. To effectively translate electrochromism into electrochemical ion memory in polymers, this study systematically investigates polymer–ion interactions, redox activity, mixed ionic–electronic conduction, and stability of ECPs both experimentally and computationally using select electrolytes. The best-performing ECP-electrolyte combination is then implemented into an ECRAM device to further explore synaptic plasticity behaviors. The resulting ECRAM exhibits high linearity and symmetric conductance modulation, high dynamic range (≈1 mS or ≈6x), and high training accuracy (>84% within five training cycles on a standard image recognition dataset), comparable to existing state-of-the-art ECRAMs. This study offers a promising approach to discover and design novel polymer materials for organic ECRAMs and demonstrates potential applications, taking advantage of mature knowledge basis on electrochromic materials and devices.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/adfm.202304893
