The memristor is a circuit element whose conductance depends on its previous functioning history. Although postulated decades ago, it was actually fabricated only recently, spurring much debate and activity as to its possible applications in smart sensors and memory components in information handling systems. Recently we fabricated an organic memristor, basically a heterojunction between a conducting polymer (polyaniline) and a solid electrolyte (Li-doped polyethylene oxide). In this paper we describe the peculiar behavior of this device, due to the electrochemical control through ion flux and redox reactions in the conducting polymer, which lead to properties such as non linearity and memory. In special conditions, this organic memristor generates current auto-oscillation in fixed voltage conditions. Using these features we have fabricated several types of circuits which could be trained using the appropriate external stimuli, demonstrating supervised and unsupervised learning. Finally, the possibility of the formation of adaptive networks of statistically distributed self-assembled complex molecules for biologically inspired parallel information handling will be discussed.

Organic Memristor and Bio-Inspired Information Processing

Erokhin V;
2010

Abstract

The memristor is a circuit element whose conductance depends on its previous functioning history. Although postulated decades ago, it was actually fabricated only recently, spurring much debate and activity as to its possible applications in smart sensors and memory components in information handling systems. Recently we fabricated an organic memristor, basically a heterojunction between a conducting polymer (polyaniline) and a solid electrolyte (Li-doped polyethylene oxide). In this paper we describe the peculiar behavior of this device, due to the electrochemical control through ion flux and redox reactions in the conducting polymer, which lead to properties such as non linearity and memory. In special conditions, this organic memristor generates current auto-oscillation in fixed voltage conditions. Using these features we have fabricated several types of circuits which could be trained using the appropriate external stimuli, demonstrating supervised and unsupervised learning. Finally, the possibility of the formation of adaptive networks of statistically distributed self-assembled complex molecules for biologically inspired parallel information handling will be discussed.
2010
Istituto per i Processi Chimico-Fisici - IPCF
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/39065
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