4.6 Article

Photocatalysis-Induced Nanopores toward Highly Reliable Organic Electrochemical Metallization Memory

Journal

ADVANCED ELECTRONIC MATERIALS
Volume 8, Issue 9, Pages -

Publisher

WILEY
DOI: 10.1002/aelm.202200246

Keywords

alphabetic data storage; artificial neural network; engineered nanopore; photocatalytic methods; reliable organic ECM memory

Funding

  1. Ministry of Science and Technology of China [2018YFE0118300, 2019YFB2205101]
  2. NSFC for Distinguished Young Scholars [52025022]
  3. NSFC [11974072, 52072065, 51732003, 51872043, 51902048, 62004016, U19A2091]
  4. 111 Project [B13013]
  5. Jilin Province [20210509045RQ, YDZJ202101ZYTS021, 2412021ZD003, 20210201062GX]

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This study proposes a photocatalytic method to develop a nanoporous PVC/TiO2 nanocomposite for improving the resistive switching reliability of organic ECM memory. The method effectively simplifies the morphology of metal conductive filaments, resulting in excellent uniformity, good retention, and low cycling degradation. Additionally, alphabetic data storage and image pattern recognition are successfully achieved using multilevel resistive switching behavior and an artificial neural network (ANN) with a 200 x 200 memristive array.
Organic electrochemical metallization (ECM) memory that possesses high reliable switching performance is in great demand for the future smart wearable and flexible electronics. The resistive switching (RS) behavior of organic ECM memory is determined by the micro-morphology evolution of metal conductive filaments (CFs) during the operating process. However, the morphology controllability of CFs is generally deteriorated by their random distribution and unexpected overgrowth. Herein, a kind of nanoporous PVC/TiO2 nanocomposite is developed using photocatalytic method for improving the RS reliability of organic ECM memory. The introduction of engineered nanopores can effectively simplify the CFs morphology to obtain excellent uniformity, good retention, and low cycling degradation. In addition, taking advantage of the multilevel RS behavior and 200 x 200 memristive array artificial neural network (ANN), alphabetic data storage and image pattern recognition are successfully realized. The proposed approach is expected to provide novel platforms for the advance of highly reliable flexible electronics and ANN for intelligence applications.

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