Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays - Archive ouverte HAL Access content directly
Journal Articles Advanced Electronic Materials Year : 2018

Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays

Abstract

Based on bottom‐up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could potentially revolutionize materials and device engineering which are used for information processing. Here, an innovative approach that relies on both iono‐electronic materials and intrinsic device physics to show pattern classification out of a 12‐unit biosensing array is presented. The reservoir computing and learning concept to demonstrate relevant computing based on the ionic dynamics in 400 nm channel‐length organic electrochemical transistor is used. It is shown that this approach copes efficiently with the high level of variability obtained by bottom‐up fabrication using a new electropolymerizable polymer, which enables iono‐electronic device functionality and material stability in the electrolyte. The effect of the array size and variability on the performances for a real‐time classification task paving the way to new embedded sensing and processing approaches is investigated
Fichier principal
Vignette du fichier
1806.04748.pdf (3.15 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01827889 , version 1 (06-07-2022)

Identifiers

Cite

Sébastien Pecqueur, Maurizio Mastropasq Talamo, David Guérin, Philippe Blanchard, Jean Roncali, et al.. Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays. Advanced Electronic Materials, 2018, 4 (9), pp.1800166. ⟨10.1002/aelm.201800166⟩. ⟨hal-01827889⟩
162 View
5 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More