Bionic Olfactory Neuron with In-Sensor Reservoir Computing for Intelligent Gas Recognition

Gas sensing and recognition are closely related to the sustainable development of human society, current electronic noses (e-noses) typically focus on detecting specific gases, with only a few capable of recognizing complex odor mixtures. Further, these sensors often struggle to distinguish between isomers and homologs, as these compounds usually have similar physical and chemical properties, yielding nearly identical sensor responses. Even the mammalian olfactory systems consisting of a large variety of receptor cells and efficient neuron networks sometimes fail in this task. The bottleneck stems from the inability to extract the fingerprints of these compounds and the inefficiency of signal processing. To address these limitations, a material-device-algorithm co-design strategy is proposed that integrates an organic field-effect transistor (OFET) array with in-sensor reservoir computing (RC) and the k-nearest neighbors (KNN) algorithm. Organic semiconductors diversify responses to different gases, while RC efficiently extracts spatiotemporal features with lower training costs and reduced energy overhead. This synergy achieves 100% classification accuracy for eight gases and 99.04% accuracy for a library of 26 gases, including mixtures, isomers, and homologs-among the highest reported accuracies. This work provides a groundbreaking hardware solution for bionic olfactory neurons with edge artificial intelligence (AI) functions, surpassing traditional e-noses.