An Artificial Olfactory Chemical-Resistant Synapse for Training-Free Gas Recognition

Transferring the concept of chemical-driven responses into artificial intelligence technology holds the key to mimicking olfactory for neuromorphic computing of chemical recognition. Currently, artificial olfactory systems are designed based on chemical sensor arrays. Time-dependent responses of the sensor arrays are processed by artificial neural networks for recognition. However, the sensors generate instantly volatile responses, and algorithms for the processing of the time-dependent responses have not been involved. The recognition accuracy and speed are severely impeded. A sensor array can only achieve an accuracy of 90% after at least 5 training epochs. Herein an artificial olfactory chemical-resistant synapse consisting of 3D hierarchical WO 3 @WO 3 nanofibers are demonstrated. The nanofibers exhibit persistent resistance responses through chemical exposures due to the strong chemisorption of water molecules. Typical synaptic behaviors including paired-pulse facilitation, long-term −1 short-term memory, and learning experience have been achieved. Next, a recurrent neural network that is committed to processing the time-dependent data is used to identify gas-phase chemicals of 3-hydroxy-2-butanone, triethylamine, and trimethylamine. Training-free gas recognition has been realized by a WO 3 @WO 3 nanofiber synapse only, in which the accuracy is above 90% at the first epoch. The results have great potential to satisfy stringent performance requirements on artificial perception systems.