Emulating Nociceptive Receptor and LIF Neuron Behavior via ZrOx‐based Threshold Switching Memristor

Abstract

Abstract For the progress of artificial neural networks, the imitation of multiple biological functions is indispensable for processing more tasks in a complex working environment. Memristors, which possess these advantages such as uniformity, high switching speed, and smaller device scale, are the better candidates compared to conventional complementary metal–oxide–semiconductor (CMOS) technology in artificial neural networks. In this work, an Ag/ZrOx/Pt threshold switching memristor (TSM) is designed to overcome the drawback of the large variation in the non‐volatile filament type memristor. The cycle‐to‐cycle and device‐to‐device variations are 5.6% and 4.9%. This device has mimicked the “nociceptive threshold,” “relaxation,” “no adaptation,” and “sensitization” features for the nociceptor which can prevent the artificial intelligence system from dangers in the external environment. Additionally, with the change in the strength of the external stimulus, the artificial neuron is also built by emulating “all‐or‐nothing,” “threshold‐driven‐spiking,” and “strength‐modulated” characteristics. The proposed threshold‐switching memristor allows the simultaneous emulation of the biological nociceptor and leaky integrate‐and‐fire neuron for the first time, which represents an advance in the bioinspired technology adopted in future artificial neural networks and humanoid robots.

Keywords

• leaky integrate‐and‐fire neurons
• nociceptors
• threshold switching memristors
• volatile