Teams led by Dr. Hyunjung Yi and Suyoun Lee have developed a simple but highly accurate disease diagnosis technology by combining tactile neuron devices with artificial neural network learning methods. Unlike the previously reported artificial tactile neuron devices, this tactile neuron device can determine the stiffness of objects. Their results were published in Advanced Materials.

Neuromorphic technology is a research field that aims to emulate the human brain's information processing method, which is capable of high-level functions while consuming a small amount of energy using electronic circuits. It is gaining attention as a new data processing technology useful for AI, IoT and autonomous driving, requiring the real-time processing of complex and vast information.

Sensory neurons receive external stimuli through sensory receptors and convert them into electrical spike signals. Here, the generated spike pattern varies based on the external stimulus information. For example, higher stimulus intensity causes higher generated spike frequency. The research team developed an artificial tactile neuron device with a simple structure that combines a pressure sensor and an ovonic threshold switch device to produce such sensory neuron characteristics. Applying pressure to the pressure sensor causes the sensor's resistance to decrease and the connected ovonic switch element's spike frequency to change. The developed artificial tactile neuron device is a high-response, high-sensitivity device that allows the pressing force to generate faster electrical spikes while improving the pressure sensitivity, which focuses on the fact that stiffer materials result in faster pressure sensing when pressed.