Risk-Perceptional and Feedback-Controlled Response System Based on NO₂-Detecting Artificial Sensory Synapse

Abstract

Bio-inspired artificial neural networks can be used to realize the efficient perception and parallel processing of unstructured data. This paper proposes a feedback-controlled response system based on a NO₂-detecting artificial sensory synapse, which can process, judge, and react to a varying gas environment. The NO₂-detecting artificial sensory synapse adopts an organic heterostructure involving the charge trapping layer (pentacene) and hole-conducting layer (copper-phthalocyanine). The electron-withdrawing nature of NO₂ and its high compatibility with copper-phthalocyanine induce the retentive behavior of an increase in the conductance at the hole conduction channel when consecutive positive pulses are applied to the gate terminal. The system consists of the artificial sensory synapse and artificial neuron circuits, which can provide systematic responses to varying NO₂ conditions, thereby successfully simulating the efficient risk-response system of biological neural networks. The proposed feedback-controlled response system can facilitate the development of bionic electronics and artificial intelligence frameworks.