A tactile sensor system with sensory neurons and a perceptual synaptic network based on semivolatile carbon nanotube transistors

The human sensory system has a fascinating stimulus-detection capability attributed to the fact that the feature (pattern) of an input stimulus can be extracted through perceptual learning. Therefore, sensory information can be organized and identified efficiently based on iterative experiences, whereby the sensing ability is improved. Specifically, the distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. Herein, we demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network composed of a single device: a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. Furthermore, the developed circuit model of the system provides quantitative analytical and product-level feasibility. This work is a step toward the design and use of a neuromorphic sensory system with a learning capability for potential applications in robotics and prosthetics.