Open AccessOptimization of an organic memristor as an adaptive memory element
Author(s) -
Tatiana Berzina,
Anteo Smerieri,
Marco Bernabò,
Andrea Pucci,
Giacomo Ruggeri,
Victor Erokhin,
Marco Fontana
Publication year - 2009
Publication title -
journal of applied physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.699
H-Index - 319
eISSN - 1089-7550
pISSN - 0021-8979
DOI - 10.1063/1.3153944
Subject(s) - memristor , polyaniline , materials science , conductive polymer , signal (programming language) , fabrication , electrical resistivity and conductivity , nanotechnology , computer science , conductivity , electronic engineering , polymer , electrical engineering , engineering , physics , composite material , medicine , alternative medicine , pathology , quantum mechanics , polymerization , programming language
The combination of memory and signal handling characteristics of a memristor makes it a promising candidate for adaptive bioinspired information processing systems. This poses stringent requirements on the basic device, such as stability and reproducibility over a large number of training/learning cycles, and a large anisotropy in the fundamental control material parameter, in our case the electrical conductivity. In this work we report results on the improved performance of electrochemically controlled polymeric memristors, where optimization of a conducting polymer polyaniline in the active channel and better environmental control of fabrication methods led to a large increase both in the absolute values of the conductivity in the partially oxydized state of polyaniline and of the on-off conductivity ratio. These improvements are crucial for the application of the organic memristor to adaptive complex signal handling networks
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