Temperature Regulated Artificial Neuron Based on Memristor
Jianxin Wu, Weixi Ye, Jia‐Ming Lin, Xianghong Zhang, Bangyan Zeng, Yuyang Fan, Tailiang Guo, Huipeng Chen
Abstract
Recently, artificial neurons have attracted much attention due to their excellent energy efficiency and scalability. However, there are still few reports on the regulation of the performance of artificial neuron based on single device. In the letter, we present an artificial neuron device based on Ag/TaOx/Si that not only has excellent turn-on and turn-off performance, but also exhibits sustained stability under multiple cycle tests. Moreover, the Integrate -and-Fire (IF) neuron model is successfully simulated at room temperature. More importantly, we find that an increase of temperature could reduce the threshold voltage and reset voltage of neurons, improve the probability of neuronal firing, and promote the transformation of neurons from nonvolatile to volatile. This work provides an effective method for regulating artificial neurons and has important implications for studying information transfer in biology and adapting complex computations in a memory computing framework.