Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays

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.